diff --git a/README.md b/README.md index 18c195e..cb8efff 100644 --- a/README.md +++ b/README.md @@ -104,7 +104,8 @@ For a detailed, hands-on introduction to the project, please see our quickstart | **Sports Media** | [`quickstart_sports_media.ipynb`](notebooks/quickstart_sports_media.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_sports_media.ipynb) | | **Databricks Unity Catalog [Health Care]** | [`quickstart_healthcare_databricks.ipynb`](notebooks/quickstart_healthcare_databricks.ipynb) | Databricks Notebook Only | | **Snowflake Horizon Catalog [ FMCG ]** | [`quickstart_fmcg_snowflake.ipynb`](notebooks/quickstart_fmcg_snowflake.ipynb) | Snowflake Notebook Only | -| **Native Snowflake with Cortex Analyst [ Tech Manufacturing ]** | [`quickstart_native_snowflake.ipynb`](notebooks/quickstart_native_snowflake.ipynb) | Snowflake Notebook Only | +| **Native Snowflake with Cortex Analyst [ Tech Manufacturing ]** | [`quickstart_native_snowflake.ipynb`](notebooks/quickstart_native_snowflake.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) | +| **Native Databricks with AI/BI Genie [ Tech Manufacturing ]** | [`quickstart_native_databricks.ipynb`](notebooks/quickstart_native_databricks.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_databricks.ipynb) | These datasets will take you through the following steps: diff --git a/docsite/docs/connectors/databricks.md b/docsite/docs/connectors/databricks.md new file mode 100644 index 0000000..b1e0b41 --- /dev/null +++ b/docsite/docs/connectors/databricks.md @@ -0,0 +1,104 @@ +--- +sidebar_position: 2 +--- + +# Databricks + +`intugle` integrates with Databricks, allowing you to read data from your tables and deploy your `SemanticModel` by setting constraints and comments directly in your Databricks account. + +## Installation + +To use `intugle` with Databricks, you must install the optional dependencies: + +```bash +pip install "intugle[databricks]" +``` + +This installs the `pyspark`, `sqlglot` and `databricks-sql-connector` libraries. + +## Configuration + +The Databricks adapter can connect using credentials from a `profiles.yml` file or automatically use an active session when running inside a Databricks notebook. + +### Connecting from an External Environment + +When running `intugle` outside of a Databricks notebook, you must provide full connection credentials in a `profiles.yml` file at the root of your project. The adapter looks for a top-level `databricks:` key. + +**Example `profiles.yml`:** + +```yaml +databricks: + host: + http_path: + token: + schema: + catalog: # Optional, for Unity Catalog +``` + +### Connecting from a Databricks Notebook + +When your code is executed within a Databricks Notebook, the adapter automatically detects and uses the notebook's active Spark session for execution. However, it still requires a `profiles.yml` file to determine the target `schema` and `catalog` for your operations. + +**Example `profiles.yml` for Notebooks:** + +```yaml +databricks: + schema: + catalog: # Optional, for Unity Catalog +``` + +## Usage + +### Reading Data from Databricks + +To include a Databricks table in your `SemanticModel`, define it in your input dictionary with `type: "databricks"` and use the `identifier` key to specify the table name. + +:::caution Important +The dictionary key for your dataset (e.g., `"CUSTOMERS"`) must exactly match the table name specified in the `identifier`. +::: + +```python +from intugle import SemanticModel + +datasets = { + "CUSTOMERS": { + "identifier": "CUSTOMERS", # Must match the key above + "type": "databricks" + }, + "ORDERS": { + "identifier": "ORDERS", # Must match the key above + "type": "databricks" + } +} + +# Initialize the semantic model +sm = SemanticModel(datasets, domain="E-commerce") + +# Build the model as usual +sm.build() +``` + +### Materializing Data Products + +When you use the `DataProduct` class with a Databricks connection, the resulting data product will be materialized as a new **view** directly within your target schema. + +### Deploying the Semantic Model + +Once your semantic model is built, you can deploy it to Databricks using the `deploy()` method. This process syncs your model's intelligence to your physical tables by: +1. **Syncing Metadata:** It updates the comments on your physical Databricks tables and columns with the business glossaries from your `intugle` model. You can also sync tags. +2. **Setting Constraints:** It sets `PRIMARY KEY` and `FOREIGN KEY` constraints on your tables based on the relationships discovered in the model. + +```python +# Deploy the model to Databricks +sm.deploy(target="databricks") + +# You can also control which parts of the deployment to run +sm.deploy( + target="databricks", + sync_glossary=True, + sync_tags=True, + set_primary_keys=True, + set_foreign_keys=True +) +``` + diff --git a/docsite/docs/examples.md b/docsite/docs/examples.md index b359143..80ac6b7 100644 --- a/docsite/docs/examples.md +++ b/docsite/docs/examples.md @@ -15,7 +15,8 @@ For a detailed, hands-on introduction to the project, please see our quickstart | **Sports Media** | [`quickstart_sports_media.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_sports_media.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_sports_media.ipynb) | | **Databricks Unity Catalog [Health Care]** | [`quickstart_healthcare_databricks.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_healthcare_databricks.ipynb) | Databricks Notebook Only | | **Snowflake Horizon Catalog [ FMCG ]** | [`quickstart_fmcg_snowflake.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_fmcg_snowflake.ipynb) | Snowflake Notebook Only | -| **Native Snowflake with Cortex Analyst [ Tech Manufacturing ]** | [`quickstart_native_snowflake.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) | Snowflake Notebook Only | +| **Native Snowflake with Cortex Analyst [ Tech Manufacturing ]** | [`quickstart_native_snowflake.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) | +| **Native Databricks with AI/BI Genie [ Tech Manufacturing ]** | [`quickstart_native_databricks.ipynb`](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_native_databricks.ipynb) | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Intugle/data-tools/blob/main/notebooks/quickstart_native_databricks.ipynb) | These datasets will take you through the following steps: diff --git a/notebooks/quickstart_native_databricks.ipynb b/notebooks/quickstart_native_databricks.ipynb new file mode 100644 index 0000000..164bb50 --- /dev/null +++ b/notebooks/quickstart_native_databricks.ipynb @@ -0,0 +1,2332 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "51afdb98-f483-427c-bf3d-99e67555384f", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell1" + }, + "source": [ + "# Quickstart: Building a GenAI powered Semantic Data Model with Intugle natively in Databricks\n", + "\n", + "This quickstart shows you how to use Intugle’s open-source library to transform fragmented datasets into a connected semantic model. The semantic model captures profiling, relationships, and business meaning of your data — making it instantly usable for exploration, search, and data product creation.\n", + "\n", + "**What is a Semantic Model?**\n", + "\n", + "A semantic model is an intelligent knowledge graph of your data. It connects tables, discovers relationships, and enriches them with business glossaries — so both data teams and business users can query with clarity, not complexity.\n", + "\n", + "**Who is this for?**\n", + "\n", + "* **Data Engineers & Architects** often spend weeks manually profiling, classifying, and stitching together fragmented data assets. With Intugle, they can automate this process end-to-end, uncovering meaningful links and relationships to instantly generate a connected semantic layer.\n", + "* **Data Analysts & Scientists** spend endless hours on data readiness and preparation before they can even start the real analysis. Intugle accelerates this by providing contextual intelligence, automatically generating SQL and reusable data products enriched with relationships and business meaning.\n", + "* **Business Analysts & Decision Makers** are slowed down by constant dependence on technical teams for answers. Intugle removes this bottleneck by enabling natural language queries and semantic search, giving them trusted insights on demand.\n", + "\n", + "**In this notebook, you will learn how to:**\n", + "\n", + "* **Generate Semantic Model** → The unified layer that transforms fragmented datasets, creating the foundation for connected intelligence.\n", + " * **1.1 Profile and classify data** → Analyze your data sources to understand their structure, data types, and other characteristics.\n", + " * **1.2 Discover links & relationships among data** → Reveal meaningful connections (PK & FK) across fragmented tables.\n", + " * **1.3 Generate a business glossary** → Create business-friendly terms and use them to query data with context.\n", + " * **1.4 Enable Semantic search** → Intelligent search that understands meaning, not just keywords—making data more accessible across both technical and business users.\n", + " * **1.5 Visualize semantic model** → Get access to enriched metadata of the semantic model and visualize your data and relationships.\n", + "* **Build Unified Data Products** → Simply pick the attributes across your data tables, and let the toolkit auto-generate queries with all the required joins, transformations, and aggregations using the semantic layer. When executed, these queries produce reusable data products.\n", + "* Sync the semantic model and data products to Databricks Unity Catalog\n", + "* Converse with your data using Databricks Genie\n", + "\n", + "Before you start, make sure you install the **Intugle Data Tools** in your environemt: `pip install intugle[databricks]`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "df4da28c-dbaf-4440-84a0-95cbfd4683f3", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell3" + }, + "source": [ + "## 1. LLM Configuration\n", + "\n", + "Before running the project, you need to configure a Large Language Model (LLM). This is used for tasks like generating business glossaries and predicting links between tables. For the semantic search feature, you will also need to set up Qdrant and provide an OpenAI API key. For detailed setup instructions, please refer to the [README.md](README.md) file.\n", + "\n", + "You can configure the necessary services by setting the following environment variables:\n", + "\n", + "* `LLM_PROVIDER`: The LLM provider and model to use (e.g., `openai:gpt-3.5-turbo`). The format follows langchain's format for initializing chat models. Checkout how to specify your model [here](https://python.langchain.com/docs/integrations/chat/)\n", + "* `API_KEY`: Your API key for the LLM provider. The exact name of the variable may vary from provider to provider (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`).\n", + "\n", + "Here's an example of how to set these variables in your environment:\n", + "\n", + "```bash\n", + "export LLM_PROVIDER=\"openai:gpt-3.5-turbo\"\n", + "export OPENAI_API_KEY=\"your-openai-api-key\"\n", + "```\n", + "Alternatively, you can set them in the notebook like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c60bc6a8-2656-4393-a92d-f0a0a0122a79", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell4" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "os.environ[\"LLM_PROVIDER\"] = \"openai:gpt-3.5-turbo\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\" # Replace with your actual key\n", + "\n", + "\n", + "# Load environment variables from .env file\n", + "load_dotenv(override=True)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "fb8538ea-a5d8-4501-a13a-6bff29d7d5aa", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell5" + }, + "source": [ + "> Currently the langchain packages for OpenAI, Anthropic and Gemini is installed by default. For additional models, make sure you have the integration packages installed. E.g. you should have langchain-deepseek installed to use a DeepSeek model. You can get these packages here: [LangChain Chat Models](https://python.langchain.com/docs/integrations/chat/)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "db5c1e88-f1b4-4f50-a584-6a9a0584fa2a", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell6" + }, + "source": [ + "## 2. Building the Semantic Model\n", + "\n", + "The `SemanticModel` is the entry point for building your semantic layer. It takes a dictionary of datasets as input and performs the following steps:\n", + "\n", + "1. **Data Profiling:** Calculates statistics for each column, such as distinct count, uniqueness, and completeness.\n", + "2. **Datatype Identification:** Identifies the data type of each column (e.g., integer, string, datetime).\n", + "3. **Key Identification:** Identifies potential primary keys.\n", + "4. **Glossary Generation:** Generates a business glossary for each column using an LLM.\n", + "5. **Link Prediction:** Predicts the relationships (foreign keys) between tables.\n", + "\n", + "Let's start by defining the datasets we want to use. The path shown below can be a local file path or a remote URL." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "cfe62d69-97cc-420a-8405-6560a6384407", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell37" + }, + "source": [ + "> For this demo, we will be using the technology manufacturing dataset which can be found under [sample_data/tech_manufacturing](https://github.com/Intugle/data-tools/blob/main/notebooks/quickstart_native_snowflake.ipynb) in the repo" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b563f3bc-bc34-4d09-85b7-ebe94f2b3a95", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell7" + }, + "outputs": [], + "source": [ + "def generate_config(table_name: str) -> str:\n", + " \"\"\"Append the base URL to the table name.\"\"\"\n", + " return {\n", + " \"identifier\": table_name,\n", + " \"type\": \"databricks\"\n", + " }\n", + "\n", + "\n", + "table_names = \\\n", + "[\n", + " \"campaigns\",\n", + " \"campaign_survey\",\n", + " \"customer_hierarchy\",\n", + " \"customers\",\n", + " \"delivery_survey\",\n", + " \"expense\",\n", + " \"install_base\",\n", + " \"inventory\",\n", + " \"logistics\",\n", + " \"nps_survey\",\n", + " \"opportunity\",\n", + " \"orders\",\n", + " \"prob_statement_issue\",\n", + " \"product_feature\",\n", + " \"product_hierarchy\",\n", + " \"products\",\n", + " \"renewals\",\n", + " \"returns\",\n", + " \"service_requests\",\n", + " \"website\",\n", + "]\n", + "\n", + "\n", + "datasets = {table: generate_config(table) for table in table_names}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "cee3f9d4-9cb1-412f-8cc2-5f0f9c8fd65a", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell8" + }, + "source": [ + "Now, let's use the `SemanticModel` to build our semantic layer:\n", + "\n", + "> The `domain` parameter helps the LLM generate a more contextual business glossary. It specifies the industry domain that the dataset belongs to (e.g., \"Healthcare\", \"Finance\", \"E-commerce\")." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "fdc5c410-f3a7-4c12-83c6-ca2fa71f1446", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Found existing YAML for 'campaigns'. Checking for staleness.\ncampaigns loaded\nFound existing YAML for 'campaign_survey'. Checking for staleness.\ncampaign_survey loaded\nFound existing YAML for 'customer_hierarchy'. Checking for staleness.\ncustomer_hierarchy loaded\nFound existing YAML for 'customers'. Checking for staleness.\ncustomers loaded\nFound existing YAML for 'delivery_survey'. Checking for staleness.\ndelivery_survey loaded\nFound existing YAML for 'expense'. Checking for staleness.\nexpense loaded\nFound existing YAML for 'install_base'. Checking for staleness.\ninstall_base loaded\nFound existing YAML for 'inventory'. Checking for staleness.\ninventory loaded\nFound existing YAML for 'logistics'. Checking for staleness.\nlogistics loaded\nFound existing YAML for 'nps_survey'. Checking for staleness.\nnps_survey loaded\nFound existing YAML for 'opportunity'. Checking for staleness.\nopportunity loaded\nFound existing YAML for 'orders'. Checking for staleness.\norders loaded\nFound existing YAML for 'prob_statement_issue'. Checking for staleness.\nprob_statement_issue loaded\nFound existing YAML for 'product_feature'. Checking for staleness.\nproduct_feature loaded\nFound existing YAML for 'product_hierarchy'. Checking for staleness.\nproduct_hierarchy loaded\nFound existing YAML for 'products'. Checking for staleness.\nproducts loaded\nFound existing YAML for 'renewals'. Checking for staleness.\nrenewals loaded\nFound existing YAML for 'returns'. Checking for staleness.\nreturns loaded\nFound existing YAML for 'service_requests'. Checking for staleness.\nservice_requests loaded\nFound existing YAML for 'website'. Checking for staleness.\nwebsite loaded\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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column_nametable_namebusiness_namedatatype_l1datatype_l2business_glossarybusiness_tagscountnull_countdistinct_countuniquenesscompletenesssample_data
0Product IDproductsproduct_idalphanumericdimensionServes as a unique identifier for individual i...[Product Identification, Inventory Tracking, M...1000010001.0001.0[PROD-11196, PROD-11735, PROD-11723, PROD-1140...
1Product Nameproductsproduct_nameclose_ended_textdimensionIdentifies the name used to distinguish a spec...[Product Portfolio, Technology Solutions, Bran...1000050.0051.0[CoreAnalytics, CloudEdge, NetConnect, Insight...
2Product Categoryproductsproduct_categoryclose_ended_textdimensionGroups products into predefined classification...[Technology Solutions, Business Segmentation, ...1000050.0051.0[Networking, Cloud Platform, IoT, Security, An...
3Product Statusproductsproduct_statusclose_ended_textdimensionIndicates the current lifecycle phase of a pro...[Lifecycle Management, Product Development Sta...1000050.0051.0[In Development, GA (General Availability), Ac...
4R&D Initiation Dateproductsr_d_initiation_datedate & timedimensionMarks the date when the research and developme...[Research And Development Timeline, Product De...100007770.7771.0[2022-06-05, 2025-01-08, 2021-07-07, 2023-02-0...
5R&D Stageproductsr_d_stageclose_ended_textdimensionIndicates the current phase of development and...[Product Development Lifecycle, Innovation Tra...1000050.0051.0[Concept, Pre-Launch, Enhancement, Testing, Pr...
6Launch Statusproductslaunch_statusclose_ended_textdimensionIndicates the current phase or outcome of a pr...[Product Lifecycle, Launch Planning, Market Re...1000050.0051.0[Not Launched, Planned, Launched, Cancelled, I...
7Launch Dateproductslaunch_datedate & timedimensionIndicates the scheduled date and time when a p...[Product Launch Timeline, Go-To-Market Strateg...100006590.6591.0[2025-07-14, 2025-07-03, 2025-03-15, 2024-07-2...
8PM Nameproductspm_nameclose_ended_textdimensionIdentifies the individual responsible for mana...[Product Manager, Responsible Party, Ownership]100009940.9941.0[Lucas Marsh, Jillian Brady, Theresa Hall, Bri...
9Product Cost ($)productsproduct_costintegermeasureMonetary value associated with the production ...[Product Manufacturing Cost, Cost Analysis, Fi...100009980.9981.0[189597, 576010, 217526, 391686, 218954, 26701...
\n", + "
" + ], + "text/plain": [ + " column_name ... sample_data\n", + "0 Product ID ... [PROD-11196, PROD-11735, PROD-11723, PROD-1140...\n", + "1 Product Name ... [CoreAnalytics, CloudEdge, NetConnect, Insight...\n", + "2 Product Category ... [Networking, Cloud Platform, IoT, Security, An...\n", + "3 Product Status ... [In Development, GA (General Availability), Ac...\n", + "4 R&D Initiation Date ... [2022-06-05, 2025-01-08, 2021-07-07, 2023-02-0...\n", + "5 R&D Stage ... [Concept, Pre-Launch, Enhancement, Testing, Pr...\n", + "6 Launch Status ... [Not Launched, Planned, Launched, Cancelled, I...\n", + "7 Launch Date ... [2025-07-14, 2025-07-03, 2025-03-15, 2024-07-2...\n", + "8 PM Name ... [Lucas Marsh, Jillian Brady, Theresa Hall, Bri...\n", + "9 Product Cost ($) ... [189597, 576010, 217526, 391686, 218954, 26701...\n", + "\n", + "[10 rows x 13 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "products_dataset = sm.datasets['products']\n", + "products_dataset.profiling_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "5ccdcad7-775f-4450-98c5-b0edf9290669", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell12" + }, + "source": [ + "The profiling results can be accessed through the `profiling_df` property of the `DataSet` object. It's a pandas DataFrame that you can easily explore. \n", + "> The business glossary is also available in the `profiling_df`:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c1fd43dd-8b0a-4ccd-b060-39529680c265", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell13" + }, + "source": [ + "### Visualizing Relationships\n", + "\n", + "The `SemanticModel` automatically discovers the relationships between your tables. You can access the predicted links as a list of `PredictedLink` objects:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "a7171e09-db28-42cb-b110-58f3a2935590", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell14" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[PredictedLink(from_dataset='campaigns', from_column='Campaign ID', to_dataset='campaign_survey', to_column='Camp ID', intersect_count=635, intersect_ratio_from_col=0.634, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='campaigns', from_column='Prospect ID', to_dataset='customers', to_column='C_ID', intersect_count=622, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.622, accuracy=1.0),\n", + " PredictedLink(from_dataset='campaigns', from_column='Prod_ID', to_dataset='products', to_column='Product ID', intersect_count=633, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.633, accuracy=1.0),\n", + " PredictedLink(from_dataset='campaigns', from_column='Campaign ID', to_dataset='website', to_column='Cmgn ID', intersect_count=618, intersect_ratio_from_col=0.618, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customer_hierarchy', from_column='Party ID', to_dataset='customers', to_column='C_ID', intersect_count=616, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.616, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='delivery_survey', to_column='Customer ID', intersect_count=627, intersect_ratio_from_col=0.627, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='expense', to_column='Contact ID', intersect_count=646, intersect_ratio_from_col=0.646, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='install_base', to_column='Party ID', intersect_count=638, intersect_ratio_from_col=0.638, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='logistics', to_column='Cust_ID', intersect_count=612, intersect_ratio_from_col=0.612, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='nps_survey', to_column='Customer_ID', intersect_count=615, intersect_ratio_from_col=0.615, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='opportunity', to_column='Cust ID', intersect_count=636, intersect_ratio_from_col=0.636, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='orders', to_column='Party ID', intersect_count=994, intersect_ratio_from_col=0.994, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='renewals', to_column='Cus ID', intersect_count=637, intersect_ratio_from_col=0.637, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='returns', to_column='Customer ID', intersect_count=652, intersect_ratio_from_col=0.652, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='customers', from_column='C_ID', to_dataset='service_requests', to_column='C ID', intersect_count=993, intersect_ratio_from_col=0.993, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='delivery_survey', from_column='Odr ID', to_dataset='orders', to_column='Order ID', intersect_count=893, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.179, accuracy=1.0),\n", + " PredictedLink(from_dataset='delivery_survey', from_column='PD ID', to_dataset='products', to_column='Product ID', intersect_count=640, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.64, accuracy=1.0),\n", + " PredictedLink(from_dataset='expense', from_column='Invoice Number', to_dataset='inventory', to_column='Inventory ID', intersect_count=1000, intersect_ratio_from_col=1.0, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='install_base', from_column='Order ID', to_dataset='orders', to_column='Order ID', intersect_count=1000, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.2, accuracy=1.0),\n", + " PredictedLink(from_dataset='install_base', from_column='Product ID', to_dataset='products', to_column='Product ID', intersect_count=630, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.63, accuracy=1.0),\n", + " PredictedLink(from_dataset='inventory', from_column='Prod_ID', to_dataset='products', to_column='Product ID', intersect_count=621, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.621, accuracy=1.0),\n", + " PredictedLink(from_dataset='logistics', from_column='Od_Contract_ID', to_dataset='orders', to_column='Order ID', intersect_count=890, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.178, accuracy=1.0),\n", + " PredictedLink(from_dataset='nps_survey', from_column='Return_ID', to_dataset='returns', to_column='Return_ID', intersect_count=627, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.627, accuracy=1.0),\n", + " PredictedLink(from_dataset='nps_survey', from_column='Incident ID', to_dataset='service_requests', to_column='SR ID', intersect_count=911, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.182, accuracy=1.0),\n", + " PredictedLink(from_dataset='orders', from_column='Product ID', to_dataset='products', to_column='Product ID', intersect_count=994, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.994, accuracy=1.0),\n", + " PredictedLink(from_dataset='orders', from_column='Order ID', to_dataset='renewals', to_column='Od ID', intersect_count=906, intersect_ratio_from_col=0.181, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='orders', from_column='Order ID', to_dataset='returns', to_column='R Order ID', intersect_count=925, intersect_ratio_from_col=0.185, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='orders', from_column='Order ID', to_dataset='service_requests', to_column='Sales Ord ID', intersect_count=3223, intersect_ratio_from_col=0.645, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='prob_statement_issue', from_column='Pd_ID', to_dataset='products', to_column='Product ID', intersect_count=628, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.628, accuracy=1.0),\n", + " PredictedLink(from_dataset='prob_statement_issue', from_column='SR ID', to_dataset='service_requests', to_column='SR ID', intersect_count=890, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.178, accuracy=1.0),\n", + " PredictedLink(from_dataset='product_feature', from_column='P_ID', to_dataset='products', to_column='Product ID', intersect_count=644, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.644, accuracy=1.0),\n", + " PredictedLink(from_dataset='product_hierarchy', from_column='Product ID', to_dataset='products', to_column='Product ID', intersect_count=631, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.631, accuracy=1.0),\n", + " PredictedLink(from_dataset='products', from_column='Product ID', to_dataset='renewals', to_column='Pr ID', intersect_count=646, intersect_ratio_from_col=0.646, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='products', from_column='Product ID', to_dataset='returns', to_column='Product ID', intersect_count=642, intersect_ratio_from_col=0.642, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='products', from_column='Product ID', to_dataset='service_requests', to_column='Prod ID', intersect_count=993, intersect_ratio_from_col=0.993, intersect_ratio_to_col=1.0, accuracy=1.0),\n", + " PredictedLink(from_dataset='returns', from_column='SR ID', to_dataset='service_requests', to_column='SR ID', intersect_count=1000, intersect_ratio_from_col=1.0, intersect_ratio_to_col=0.2, accuracy=1.0)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sm.links" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "2033c057-a741-4fa2-8fc0-ef1ffd2085f2", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell15" + }, + "source": [ + "You can also visualize these relationships as a graph. In case you run into an error, make sure you install/upgrade your ipykernel package:\n", + "> %pip install --upgrade ipykernel" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "81aa7147-5d92-48f1-a908-2816920d886b", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell16" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sm.visualize() # To visualize the relationships as a graph" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "e957ab4c-80e7-4542-a0ef-5f2c070636f0", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell17" + }, + "source": [ + "## 4. The Semantic Layer\n", + "\n", + "The SemanticModel results are used to generate YAML files which are saved automatically. These files defines the semantic layer, including the models (tables) and their relationships. \n", + "\n", + "By default, these files are saved in the current working directory. You can configure this path by setting the `PROJECT_BASE` environment variable." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "4eb1f6de-9b2e-45e8-89d6-1351a713bcac", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell19" + }, + "source": [ + "## 5. Deploying to Databricks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "93149df1-dc2d-4947-aba4-20517b702a1d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell29" + }, + "source": [ + "Syncs the business glossaries, tags, primary keys and relationsips with the source tables. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "61ab3d8e-ffd8-4b68-8716-4ffb7906585c", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell21" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
Starting deployment to 'databricks' based on project YAML files...\n",
+       "
\n" + ], + "text/plain": [ + "\u001B[33mStarting deployment to \u001B[0m\u001B[32m'databricks'\u001B[0m\u001B[33m based on project YAML files\u001B[0m\u001B[33m...\u001B[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting primary key constraints...\nSet primary key on `intugle`.`tech_manufacturing`.`product_hierarchy` (`SKU ID`)\nSkipping primary key for table 'product_feature' due to missing or invalid key.\nSet primary key on `intugle`.`tech_manufacturing`.`website` (`Customer Session ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`prob_statement_issue` (`Issue ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`customer_hierarchy` (`Account Manager`)\nSet primary key on `intugle`.`tech_manufacturing`.`campaigns` (`Campaign ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`products` (`Product ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`service_requests` (`SR ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`logistics` (`Logistics ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`customers` (`C_ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`expense` (`Expense ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`install_base` (`Order ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`campaign_survey` (`Participant Name`)\nSet primary key on `intugle`.`tech_manufacturing`.`nps_survey` (`NPS_ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`orders` (`Order ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`opportunity` (`Opportunity ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`renewals` (`Renw ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`returns` (`Return_ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`delivery_survey` (`Odr ID`)\nSet primary key on `intugle`.`tech_manufacturing`.`inventory` (`Inventory ID`)\nPrimary key setting complete.\nSetting foreign key constraints...\nCould not set foreign key for relationship install_base_orders: Failed to create foreign key constraint `fk_install_base_orders`: table `intugle.tech_manufacturing.orders` already has a foreign key constraint: `fk_delivery_survey_orders`, that has the same set of child columns.\n\nJVM stacktrace:\norg.apache.spark.sql.catalyst.analysis.ConstraintAlreadyExistsException\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.$anonfun$createTableConstraint$1(ManagedCatalogClientImpl.scala:4008)\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.$anonfun$recordAndWrapExceptionBase$2(ManagedCatalogClientImpl.scala:7505)\n\tat com.databricks.spark.util.FrameProfiler$.$anonfun$record$1(FrameProfiler.scala:114)\n\tat com.databricks.spark.util.FrameProfilerExporter$.maybeExportFrameProfiler(FrameProfilerExporter.scala:200)\n\tat com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:105)\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.$anonfun$recordAndWrapExceptionBase$1(ManagedCatalogClientImpl.scala:7504)\n\tat com.databricks.managedcatalog.ErrorDetailsHandler.wrapServiceException(ErrorDetailsHandler.scala:74)\n\tat com.databricks.managedcatalog.ErrorDetailsHandler.wrapServiceException$(ErrorDetailsHandler.scala:66)\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.wrapServiceException(ManagedCatalogClientImpl.scala:268)\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.recordAndWrapExceptionBase(ManagedCatalogClientImpl.scala:7485)\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.recordAndWrapException(ManagedCatalogClientImpl.scala:7471)\n\tat com.databricks.managedcatalog.ManagedCatalogClientImpl.createTableConstraint(ManagedCatalogClientImpl.scala:3913)\n\tat com.databricks.sql.managedcatalog.ManagedCatalogCommon.$anonfun$addTableConstraint$1(ManagedCatalogCommon.scala:2084)\n\tat scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)\n\tat com.databricks.sql.managedcatalog.ManagedCatalogCommon.withTableCacheInvalidated(ManagedCatalogCommon.scala:2336)\n\tat com.databricks.sql.managedcatalog.ManagedCatalogCommon.addTableConstraint(ManagedCatalogCommon.scala:2076)\n\tat com.databricks.sql.managedcatalog.ProfiledManagedCatalog.$anonfun$addTableConstraint$1(ProfiledManagedCatalog.scala:356)\n\tat scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)\n\tat org.apache.spark.sql.catalyst.MetricKeyUtils$.measure(MetricKey.scala:1892)\n\tat com.databricks.sql.managedcatalog.ProfiledManagedCatalog.$anonfun$profile$1(ProfiledManagedCatalog.scala:64)\n\tat com.databricks.spark.util.FrameProfiler$.$anonfun$record$1(FrameProfiler.scala:114)\n\tat com.databricks.spark.util.FrameProfilerExporter$.maybeExportFrameProfiler(FrameProfilerExporter.scala:200)\n\tat com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:105)\n\tat com.databricks.sql.managedcatalog.ProfiledManagedCatalog.profile(ProfiledManagedCatalog.scala:63)\n\tat com.databricks.sql.managedcatalog.ProfiledManagedCatalog.addTableConstraint(ProfiledManagedCatalog.scala:356)\n\tat com.databricks.sql.managedcatalog.ManagedCatalogSessionCatalog.addTableConstraint(ManagedCatalogSessionCatalog.scala:1551)\n\tat com.databricks.sql.transaction.tahoe.commands.AlterTableAddTableConstraintDeltaCommand.$anonfun$run$66(alterDeltaTableCommands.scala:1924)\n\tat com.databricks.sql.transaction.tahoe.commands.AlterTableAddTableConstraintDeltaCommand.$anonfun$run$66$adapted(alterDeltaTableCommands.scala:1923)\n\tat scala.Option.foreach(Option.scala:437)\n\tat com.databricks.sql.transaction.tahoe.commands.AlterTableAddTableConstraintDeltaCommand.run(alterDeltaTableCommands.scala:1923)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.$anonfun$alterTable$44(DeltaCatalog.scala:2083)\n\tat scala.collection.IterableOnceOps.foreach(IterableOnce.scala:619)\n\tat scala.collection.IterableOnceOps.foreach$(IterableOnce.scala:617)\n\tat scala.collection.AbstractIterable.foreach(Iterable.scala:935)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.$anonfun$alterTable$22(DeltaCatalog.scala:2079)\n\tat scala.collection.immutable.HashMap.foreach(HashMap.scala:1115)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.$anonfun$alterTable$8(DeltaCatalog.scala:1853)\n\tat scala.util.DynamicVariable.withValue(DynamicVariable.scala:59)\n\tat com.databricks.sql.transaction.tahoe.redirect.RedirectFeature$.withUpdateTableRedirectDDL(TableRedirect.scala:796)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.$anonfun$alterTable$1(DeltaCatalog.scala:1762)\n\tat com.databricks.spark.util.FrameProfiler$.$anonfun$record$1(FrameProfiler.scala:114)\n\tat com.databricks.spark.util.FrameProfilerExporter$.maybeExportFrameProfiler(FrameProfilerExporter.scala:200)\n\tat com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:105)\n\tat com.databricks.sql.transaction.tahoe.metering.DeltaLogging.recordFrameProfile(DeltaLogging.scala:420)\n\tat com.databricks.sql.transaction.tahoe.metering.DeltaLogging.recordFrameProfile$(DeltaLogging.scala:418)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.recordFrameProfile(DeltaCatalog.scala:144)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.alterTable(DeltaCatalog.scala:1741)\n\tat com.databricks.sql.transaction.tahoe.catalog.DeltaCatalog.alterTable(DeltaCatalog.scala:144)\n\tat com.databricks.sql.managedcatalog.UnityCatalogV2Proxy.alterTable(UnityCatalogV2Proxy.scala:261)\n\tat com.databricks.sql.managedcatalog.UnityCatalogV2Proxy.alterTable(UnityCatalogV2Proxy.scala:57)\n\tat org.apache.spark.sql.execution.datasources.v2.AlterTableExec.run(AlterTableExec.scala:38)\n\tat org.apache.spark.sql.execution.datasources.v2.V2CommandExec.$anonfun$result$2(V2CommandExec.scala:48)\n\tat org.apache.spark.sql.execution.SparkPlan.runCommandInAetherOrSpark(SparkPlan.scala:195)\n\tat org.apache.spark.sql.execution.datasources.v2.V2CommandExec.$anonfun$result$1(V2CommandExec.scala:48)\n\tat com.databricks.spark.util.FrameProfiler$.$anonfun$record$1(FrameProfiler.scala:114)\n\tat com.databricks.spark.util.FrameProfilerExporter$.maybeExportFrameProfiler(FrameProfilerExporter.scala:200)\n\tat com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:105)\n\tat org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result$lzycompute(V2CommandExec.scala:47)\n\tat org.apache.spark.sql.execution.datasources.v2.V2CommandExec.result(V2CommandExec.scala:45)\n\tat org.apache.spark.sql.execution.datasources.v2.V2CommandExec.executeCollect(V2CommandExec.scala:56)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$5(QueryExecution.scala:507)\n\tat com.databricks.util.LexicalThreadLocal$Handle.runWith(LexicalThreadLocal.scala:63)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$4(QueryExecution.scala:507)\n\tat org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:267)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$3(QueryExecution.scala:506)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$14(SQLExecution.scala:561)\n\tat com.databricks.sql.util.MemoryTrackerHelper.withMemoryTracking(MemoryTrackerHelper.scala:111)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$13(SQLExecution.scala:475)\n\tat org.apache.spark.sql.execution.SQLExecution$.withSessionTagsApplied(SQLExecution.scala:859)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$12(SQLExecution.scala:403)\n\tat org.apache.spark.JobArtifactSet$.withActiveJobArtifactState(JobArtifactSet.scala:97)\n\tat org.apache.spark.sql.artifact.ArtifactManager.$anonfun$withResources$1(ArtifactManager.scala:121)\n\tat org.apache.spark.sql.artifact.ArtifactManager.withClassLoaderIfNeeded(ArtifactManager.scala:115)\n\tat org.apache.spark.sql.artifact.ArtifactManager.withResources(ArtifactManager.scala:120)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$11(SQLExecution.scala:403)\n\tat org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:888)\n\tat org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId0$1(SQLExecution.scala:402)\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:860)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId0(SQLExecution.scala:238)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:812)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$2(QueryExecution.scala:502)\n\tat org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:1449)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$1(QueryExecution.scala:498)\n\tat org.apache.spark.sql.execution.QueryExecution.withMVTagsIfNecessary(QueryExecution.scala:418)\n\tat org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$eagerlyExecute$1(QueryExecution.scala:496)\n\tat org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$8$1.applyOrElse(QueryExecution.scala:578)\n\tat org.apache.spark.sql.execution.QueryExecution$$anonfun$$nestedInanonfun$eagerlyExecuteCommands$8$1.applyOrElse(QueryExecution.scala:570)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:529)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(origin.scala:121)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:529)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:42)\n\tat org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:361)\n\tat org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:357)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:42)\n\tat org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:42)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:505)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$eagerlyExecuteCommands$8(QueryExecution.scala:570)\n\tat org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:418)\n\tat org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:570)\n\tat org.apache.spark.sql.execution.QueryExecution.$anonfun$lazyCommandExecuted$1(QueryExecution.scala:374)\n\tat scala.util.Try$.apply(Try.scala:217)\n\tat org.apache.spark.util.Utils$.doTryWithCallerStacktrace(Utils.scala:1686)\n\tat org.apache.spark.util.Utils$.getTryWithCallerStacktrace(Utils.scala:1747)\n\tat org.apache.spark.util.LazyTry.get(LazyTry.scala:75)\n\tat org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:379)\n\tat org.apache.spark.sql.classic.Dataset.(Dataset.scala:432)\n\tat org.apache.spark.sql.classic.Dataset$.$anonfun$ofRows$3(Dataset.scala:155)\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:860)\n\tat org.apache.spark.sql.classic.SparkSession.$anonfun$withActiveAndFrameProfiler$1(SparkSession.scala:1072)\n\tat com.databricks.spark.util.FrameProfiler$.$anonfun$record$1(FrameProfiler.scala:114)\n\tat com.databricks.spark.util.FrameProfilerExporter$.maybeExportFrameProfiler(FrameProfilerExporter.scala:200)\n\tat com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:105)\n\tat org.apache.spark.sql.classic.SparkSession.withActiveAndFrameProfiler(SparkSession.scala:1072)\n\tat org.apache.spark.sql.classic.Dataset$.ofRows(Dataset.scala:146)\n\tat org.apache.spark.sql.classic.SparkSession.$anonfun$sql$4(SparkSession.scala:851)\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:860)\n\tat org.apache.spark.sql.classic.SparkSession.sql(SparkSession.scala:814)\n\tat org.apache.spark.sql.connect.planner.SparkConnectPlanner.executeSQL(SparkConnectPlanner.scala:3531)\n\tat org.apache.spark.sql.connect.planner.SparkConnectPlanner.handleSqlCommand(SparkConnectPlanner.scala:3361)\n\tat org.apache.spark.sql.connect.planner.SparkConnectPlanner.process(SparkConnectPlanner.scala:3238)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.handleCommand(ExecuteThreadRunner.scala:385)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$executeInternal$1(ExecuteThreadRunner.scala:282)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$executeInternal$1$adapted(ExecuteThreadRunner.scala:238)\n\tat org.apache.spark.sql.connect.service.SessionHolder.$anonfun$withSession$2(SessionHolder.scala:466)\n\tat org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:860)\n\tat org.apache.spark.sql.connect.service.SessionHolder.$anonfun$withSession$1(SessionHolder.scala:466)\n\tat org.apache.spark.JobArtifactSet$.withActiveJobArtifactState(JobArtifactSet.scala:97)\n\tat org.apache.spark.sql.artifact.ArtifactManager.$anonfun$withResources$1(ArtifactManager.scala:121)\n\tat org.apache.spark.sql.artifact.ArtifactManager.withClassLoaderIfNeeded(ArtifactManager.scala:115)\n\tat org.apache.spark.sql.artifact.ArtifactManager.withResources(ArtifactManager.scala:120)\n\tat org.apache.spark.sql.connect.service.SessionHolder.withSession(SessionHolder.scala:465)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.executeInternal(ExecuteThreadRunner.scala:238)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.$anonfun$execute$1(ExecuteThreadRunner.scala:141)\n\tat scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)\n\tat com.databricks.spark.connect.service.UtilizationMetrics.recordActiveQueries(UtilizationMetrics.scala:43)\n\tat com.databricks.spark.connect.service.UtilizationMetrics.recordActiveQueries$(UtilizationMetrics.scala:40)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.recordActiveQueries(ExecuteThreadRunner.scala:53)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner.org$apache$spark$sql$connect$execution$ExecuteThreadRunner$$execute(ExecuteThreadRunner.scala:139)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner$ExecutionThread.$anonfun$run$2(ExecuteThreadRunner.scala:586)\n\tat scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.scala:18)\n\tat com.databricks.unity.UCSEphemeralState$Handle.runWith(UCSEphemeralState.scala:51)\n\tat com.databricks.unity.HandleImpl.runWith(UCSHandle.scala:104)\n\tat com.databricks.unity.HandleImpl.$anonfun$runWithAndClose$1(UCSHandle.scala:109)\n\tat scala.util.Using$.resource(Using.scala:296)\n\tat com.databricks.unity.HandleImpl.runWithAndClose(UCSHandle.scala:108)\n\tat org.apache.spark.sql.connect.execution.ExecuteThreadRunner$ExecutionThread.run(ExecuteThreadRunner.scala:586)\nForeign key setting complete.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
Successfully deployed semantic model to 'databricks'.\n",
+       "
\n" + ], + "text/plain": [ + "\u001B[1;32mSuccessfully deployed semantic model to \u001B[0m\u001B[32m'databricks'\u001B[0m\u001B[1;32m.\u001B[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sm.deploy('databricks', sync_glossary=True, sync_tags=True, set_primary_keys=True, set_foreign_keys=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "37951c61-5627-434c-adfc-f55b9b1d4c34", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell22" + }, + "source": [ + "## 6. Data Product Creation\n", + "\n", + "The semantic layer serves as a foundation for the DataProduct, which streamlines the creation of reusable data products. This allows you\n", + "to encapsulate business logic and create standardized, trustworthy data assets that can be easily shared and reused across different teams and \n", + "applications.\n", + "\n", + "Let's define the model for the data product we want to build:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "5322b219-a8c2-4dab-89a1-e245fa2a7a6d", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell23" + }, + "outputs": [], + "source": [ + "data_product_config = \\\n", + "{\n", + " \"name\": \"customer_kpi_summary\",\n", + " \"fields\": [\n", + " {\n", + " \"id\": \"customers.C_ID\",\n", + " \"name\": \"c_id\"\n", + " },\n", + " {\n", + " \"id\": \"customers.Customer Name\",\n", + " \"name\": \"customer_name\"\n", + " },\n", + " {\n", + " \"id\": \"customer_hierarchy.Global / Parent Account\",\n", + " \"name\": \"global_parent_account\"\n", + " },\n", + " {\n", + " \"id\": \"customer_hierarchy.Region\",\n", + " \"name\": \"region\"\n", + " },\n", + " {\n", + " \"id\": \"customer_hierarchy.Global / Local Entity\",\n", + " \"name\": \"global_local_entity\"\n", + " },\n", + " {\n", + " \"id\": \"products.Product Name\",\n", + " \"name\": \"product_name\"\n", + " },\n", + " {\n", + " \"id\": \"orders.Order Value ($)\",\n", + " \"name\": \"sum_order_value\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"sum\"\n", + " },\n", + " {\n", + " \"id\": \"orders.Order ID\",\n", + " \"name\": \"count_distinct_order_id\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"count\"\n", + " },\n", + " {\n", + " \"id\": \"orders.Order Qty\",\n", + " \"name\": \"sum_order_qty\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"sum\"\n", + " },\n", + " {\n", + " \"id\": \"service_requests.SR ID\",\n", + " \"name\": \"count_distinct_sr_id\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"count\"\n", + " },\n", + " {\n", + " \"id\": \"service_requests.Prod ID\",\n", + " \"name\": \"count_distinct_prod_id\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"count\"\n", + " },\n", + " {\n", + " \"id\": \"returns.R Order ID\",\n", + " \"name\": \"count_distinct_r_order_id\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"count\"\n", + " },\n", + " {\n", + " \"id\": \"nps_survey.Survey Score\",\n", + " \"name\": \"sum_survey_score\",\n", + " \"category\": \"measure\",\n", + " \"measure_func\": \"sum\"\n", + " }\n", + " ]\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "1618b7a2-46ef-4cbf-9d8c-036d9dce7f51", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "> Checkout the Intugle documentation to learn how to add sorting and filters to your data product" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "94509207-5e86-4470-91df-c394197839fa", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell24" + }, + "source": [ + "Now, let's use the `DataProduct` to generate the data product:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "6061cfc7-59ec-4ca8-8e8a-91c1c57511ee", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell25" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Found existing YAML for 'product_hierarchy'. Checking for staleness.\nproduct_hierarchy loaded\nFound existing YAML for 'product_feature'. Checking for staleness.\nproduct_feature loaded\nFound existing YAML for 'website'. Checking for staleness.\nwebsite loaded\nFound existing YAML for 'prob_statement_issue'. Checking for staleness.\nprob_statement_issue loaded\nFound existing YAML for 'customer_hierarchy'. Checking for staleness.\ncustomer_hierarchy loaded\nFound existing YAML for 'campaigns'. Checking for staleness.\ncampaigns loaded\nFound existing YAML for 'products'. Checking for staleness.\nproducts loaded\nFound existing YAML for 'service_requests'. Checking for staleness.\nservice_requests loaded\nFound existing YAML for 'logistics'. Checking for staleness.\nlogistics loaded\nFound existing YAML for 'customers'. Checking for staleness.\ncustomers loaded\nFound existing YAML for 'expense'. Checking for staleness.\nexpense loaded\nFound existing YAML for 'install_base'. Checking for staleness.\ninstall_base loaded\nFound existing YAML for 'campaign_survey'. Checking for staleness.\ncampaign_survey loaded\nFound existing YAML for 'nps_survey'. Checking for staleness.\nnps_survey loaded\nFound existing YAML for 'orders'. Checking for staleness.\norders loaded\nFound existing YAML for 'opportunity'. Checking for staleness.\nopportunity loaded\nFound existing YAML for 'renewals'. Checking for staleness.\nrenewals loaded\nFound existing YAML for 'returns'. Checking for staleness.\nreturns loaded\nFound existing YAML for 'delivery_survey'. Checking for staleness.\ndelivery_survey loaded\nFound existing YAML for 'inventory'. Checking for staleness.\ninventory loaded\ncustomer_kpi_summary loaded\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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c_idcustomer_nameglobal_parent_accountregionglobal_local_entityproduct_namesum_order_valuecount_distinct_order_idsum_order_qtycount_distinct_sr_idcount_distinct_prod_idcount_distinct_r_order_idsum_survey_score
0CUST-10521Morris, Tate and WoodsPowell HoldingsEuropeLocalCoreAnalyticsNaN0NaN00022
1CUST-10902Allen, Hernandez and TylerSchultz-Reyes HoldingsMiddle EastGlobalNoneNaN0NaN0005
2CUST-10214King, Reynolds and KennedyAnderson, HoldingsAPACLocalNoneNaN0NaN00029
3CUST-10976Rush LLCSchultz-Vargas HoldingsNorth AmericaGlobalCloudEdge53870.04158.044414
4CUST-10502Conner IncWarren HoldingsAPACLocalCoreAnalytics22939.0249.022212
..........................................
1084CUST-10668Jefferson, Riggs and MorrowBennett-Flores HoldingsEuropeGlobalNoneNaN0NaN00014
1085CUST-10050Glover GroupNoneNoneNoneDataSphere22286.0128.01119
1086CUST-10870Wiley, Perez and RuizWalker-Shea HoldingsAPACLocalCoreAnalytics12736.0121.01117
1087CUST-10384Rice, Phillips and SmithBerry-Bates HoldingsNorth AmericaGlobalCoreAnalytics7256.011.01119
1088CUST-10683Hess, Moreno and MillerNoneNoneNoneCoreAnalytics19839.0144.01116
\n", + "

1089 rows × 13 columns

\n", + "
" + ], + "text/plain": [ + " c_id ... sum_survey_score\n", + "0 CUST-10521 ... 22\n", + "1 CUST-10902 ... 5\n", + "2 CUST-10214 ... 29\n", + "3 CUST-10976 ... 14\n", + "4 CUST-10502 ... 12\n", + "... ... ... ...\n", + "1084 CUST-10668 ... 14\n", + "1085 CUST-10050 ... 9\n", + "1086 CUST-10870 ... 7\n", + "1087 CUST-10384 ... 9\n", + "1088 CUST-10683 ... 6\n", + "\n", + "[1089 rows x 13 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from intugle import DataProduct\n", + "\n", + "# Create a DataProduct\n", + "dp = DataProduct()\n", + "\n", + "# Generate the data product\n", + "data_product = dp.build(data_product_config)\n", + "\n", + "data_product.to_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "25339458-dfcc-4548-86ed-0d9b6e552206", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "name": "cell26" + }, + "source": [ + "The `build` function returns a `DataSet` object. You can also view the generated SQL query used for creating the data product:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "111d079c-6d52-4a26-b281-5db95662d5d7", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell27" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'SELECT `customers`.`C_ID` AS c_id, `customers`.`Customer Name` AS customer_name, `customer_hierarchy`.`Global / Parent Account` AS global_parent_account, `customer_hierarchy`.`Region` AS region, `customer_hierarchy`.`Global / Local Entity` AS global_local_entity, `products`.`Product Name` AS product_name, SUM(`orders`.`Order Value ($)`) AS sum_order_value, COUNT(`orders`.`Order ID`) AS count_distinct_order_id, SUM(`orders`.`Order Qty`) AS sum_order_qty, COUNT(`service_requests`.`SR ID`) AS count_distinct_sr_id, COUNT(`service_requests`.`Prod ID`) AS count_distinct_prod_id, COUNT(`returns`.`R Order ID`) AS count_distinct_r_order_id, SUM(`nps_survey`.`Survey Score`) AS sum_survey_score FROM nps_survey LEFT JOIN customers ON `customers`.`C_ID` = `nps_survey`.`Customer_ID` LEFT JOIN customer_hierarchy ON `customer_hierarchy`.`Party ID` = `customers`.`C_ID` LEFT JOIN campaigns ON `campaigns`.`Prospect ID` = `customers`.`C_ID` LEFT JOIN products ON `campaigns`.`Prod_ID` = `products`.`Product ID` LEFT JOIN returns ON `products`.`Product ID` = `returns`.`Product ID` LEFT JOIN service_requests ON `returns`.`SR ID` = `service_requests`.`SR ID` LEFT JOIN orders ON `orders`.`Order ID` = `service_requests`.`Sales Ord ID` GROUP BY `customers`.`C_ID`, `customers`.`Customer Name`, `customer_hierarchy`.`Global / Parent Account`, `customer_hierarchy`.`Region`, `customer_hierarchy`.`Global / Local Entity`, `products`.`Product Name`'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The generated SQL query\n", + "data_product.sql_query" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "e6f15e02-9bf3-4448-97e2-71b856ac231a", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell36" + }, + "source": [ + "### Enriching the Data Product\n", + "\n", + "The `data_product` is in itself a DataSet object. Hence we can run generate glossaries for it as well. " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "fc185269-6aa9-4486-a623-5e6faca1a306", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell28" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "output_type": "stream", + "text": [ + "I0000 00:00:1759658943.597447 27837 fork_posix.cc:75] Other threads are currently calling into gRPC, skipping fork() handlers\nI0000 00:00:1759658943.622177 27837 fork_posix.cc:75] Other threads are currently calling into gRPC, skipping fork() handlers\n/databricks/python/lib/python3.12/site-packages/numpy/core/fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n return _methods._mean(a, axis=axis, dtype=dtype,\n/databricks/python/lib/python3.12/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5b9161d8b4cf45649235f2931933223c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/13 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
column_nametable_namebusiness_namedatatype_l1datatype_l2business_glossarybusiness_tagscountnull_countdistinct_countuniquenesscompletenesssample_data
0c_idcustomer_kpi_summaryc_idalphanumericL2OutputTypes.dimensionIdentifies individual customers for tracking a...[Customer Identifier, Customer Record Tracking...108906150.5647381.000000[CUST-10707, CUST-10806, CUST-10591, CUST-1048...
1customer_namecustomer_kpi_summarycustomer_nameclose_ended_textL2OutputTypes.dimensionIdentifies the legal or commercial entity asso...[Customer Identification, Business Entity Name...108905990.5500461.000000[Lee PLC, Turner, Butler and Morgan, House-Ram...
2global_parent_accountcustomer_kpi_summaryglobal_parent_accountclose_ended_textL2OutputTypes.dimensionIdentifies the overarching corporate entity as...[Global Account Hierarchy, Parent Company Iden...10892994090.3755740.725436[French Holdings, Morales Holdings, Perez-Camp...
3regioncustomer_kpi_summaryregionclose_ended_textL2OutputTypes.dimensionGeographical area associated with customer per...[Geographical Region, Market Segmentation, Reg...108929950.0045910.725436[APAC, Europe, LATAM, North America, Middle East]
4global_local_entitycustomer_kpi_summaryglobal_local_entityclose_ended_textL2OutputTypes.dimensionIndicates whether a customer-related metric is...[Entity Scope, Operational Coverage, Geographi...108929920.0018370.725436[Local, Global]
5product_namecustomer_kpi_summaryproduct_nameclose_ended_textL2OutputTypes.dimensionName of a product associated with customer per...[Product Portfolio, Customer Offering, Technol...108932950.0045910.697888[CoreAnalytics, NetConnect, Insight360, DataSp...
6sum_order_valuecustomer_kpi_summarysum_order_valueintegerL2OutputTypes.measureAggregates the total monetary value of all ord...[Customer Order Value, Revenue Aggregation, Sa...10895623420.3140500.483930[8922, 69204, 2469, 18393, 75163, 62820, 6389,...
7count_distinct_order_idcustomer_kpi_summarycount_distinct_order_idintegerL2OutputTypes.measureTracks the total number of unique orders assoc...[Customer Order Metrics, Unique Order Tracking...10890140.0128561.000000[10, 4, 6, 3, 14, 2, 12, 1, 5, 0]
8sum_order_qtycustomer_kpi_summarysum_order_qtyintegerL2OutputTypes.measureAggregates the total quantity of orders associ...[Customer Order Volume, Sales Performance Metr...10895621380.1267220.483930[116, 96, 54, 7, 50, 232, 76, 52, 61, 15]
9count_distinct_sr_idcustomer_kpi_summarycount_distinct_sr_idintegerL2OutputTypes.measureTracks the number of unique service request id...[Service Request Tracking, Customer Interactio...10890140.0128561.000000[14, 5, 6, 1, 0, 20, 2, 12, 3, 9]
10count_distinct_prod_idcustomer_kpi_summarycount_distinct_prod_idintegerL2OutputTypes.measureTracks the number of unique products associate...[Product Diversity, Customer Purchase Insights...10890140.0128561.000000[16, 9, 2, 4, 1, 5, 14, 6, 12, 3]
11count_distinct_r_order_idcustomer_kpi_summarycount_distinct_r_order_idintegerL2OutputTypes.measureTracks the number of unique orders associated ...[Customer Order Metrics, Distinct Order Tracki...10890140.0128561.000000[14, 5, 6, 1, 16, 20, 3, 12, 10, 2]
12sum_survey_scorecustomer_kpi_summarysum_survey_scoreintegerL2OutputTypes.measureAggregates the total score from customer feedb...[Customer Satisfaction, Survey Performance Met...10890530.0486691.000000[29, 9, 23, 24, 40, 51, 13, 152, 36, 69]
\n", + "" + ], + "text/plain": [ + " column_name ... sample_data\n", + "0 c_id ... [CUST-10707, CUST-10806, CUST-10591, CUST-1048...\n", + "1 customer_name ... [Lee PLC, Turner, Butler and Morgan, House-Ram...\n", + "2 global_parent_account ... [French Holdings, Morales Holdings, Perez-Camp...\n", + "3 region ... [APAC, Europe, LATAM, North America, Middle East]\n", + "4 global_local_entity ... [Local, Global]\n", + "5 product_name ... [CoreAnalytics, NetConnect, Insight360, DataSp...\n", + "6 sum_order_value ... [8922, 69204, 2469, 18393, 75163, 62820, 6389,...\n", + "7 count_distinct_order_id ... [10, 4, 6, 3, 14, 2, 12, 1, 5, 0]\n", + "8 sum_order_qty ... [116, 96, 54, 7, 50, 232, 76, 52, 61, 15]\n", + "9 count_distinct_sr_id ... [14, 5, 6, 1, 0, 20, 2, 12, 3, 9]\n", + "10 count_distinct_prod_id ... [16, 9, 2, 4, 1, 5, 14, 6, 12, 3]\n", + "11 count_distinct_r_order_id ... [14, 5, 6, 1, 16, 20, 3, 12, 10, 2]\n", + "12 sum_survey_score ... [29, 9, 23, 24, 40, 51, 13, 152, 36, 69]\n", + "\n", + "[13 rows x 13 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_product.run(domain=\"Technology Manufacturing Company\")\n", + "data_product.profiling_df" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "09a1ce49-e9d8-41d1-b26d-b2c92a105675", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell35" + }, + "source": [ + "### Syncing with Databricks Unity Catalog\n", + "Lets sync the data product with the Databricks Unity Catalog as well" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c2e48111-74fe-4fca-a795-f066865a1230", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "language": "python", + "name": "cell18" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": [ + "
Starting deployment to 'databricks' based on project YAML files...\n",
+       "
\n" + ], + "text/plain": [ + "\u001B[33mStarting deployment to \u001B[0m\u001B[32m'databricks'\u001B[0m\u001B[33m based on project YAML files\u001B[0m\u001B[33m...\u001B[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "output_type": "stream", + "name": "stdout", + "output_type": "stream", + "text": [ + "Syncing metadata to Databricks tables...\nMetadata sync complete.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
Successfully deployed semantic model to 'databricks'.\n",
+       "
\n" + ], + "text/plain": [ + "\u001B[1;32mSuccessfully deployed semantic model to \u001B[0m\u001B[32m'databricks'\u001B[0m\u001B[1;32m.\u001B[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sm.deploy('databricks')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "9e763741-1f33-4efc-8670-213fedbbbb97", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + } + }, + "source": [ + "%md\n", + ">Now that you have synced with databricks, you can use **AI|BI Genie** to **converse with your data** using natural language. AI|BI Genie leverages the relationships and context that were synced to databricks to answer questions without requiring you to write SQL.\n", + "To get started, navigate to **Genie** -> Create a new space -> Pick your datasets and start conversing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "847eadda-f22a-4998-a6bf-35ce4010267c", + "showTitle": false, + "tableResultSettingsMap": {}, + "title": "" + }, + "collapsed": false, + "name": "cell30" + }, + "source": [ + "## Conclusion\n", + "\n", + "You've learned how to:\n", + "\n", + "* Configure your LLM provider\n", + "* Build a semantic model using the `SemanticModel`.\n", + "* Access enriched metadata, business glossaries and visualize the relationships between your tables.\n", + "* Generate data products from the semantic layer using the `DataProduct`.\n", + "* Sync the semantic model with Databricks Unity Catalog\n", + "* Converse with your data using AI|BI Genie\n", + "\n", + "This is just a starting point. This project has many other features to explore. We encourage you to try it with your own data and see how it can help you build a powerful semantic layer." + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "computePreferences": { + "hardware": { + "accelerator": null, + "gpuPoolId": null, + "memory": null + } + }, + "dashboards": [], + "environmentMetadata": { + "base_environment": "", + "dependencies": [ + "/Workspace/Users/raphael.tony@intugle.ai/intugle-1.0.4rc1-py3-none-any.whl[databricks]" + ], + "environment_version": "3" + }, + "inputWidgetPreferences": null, + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 4 + }, + "notebookName": "quickstart_native_databricks", + "widgets": {} + }, + "kernelspec": { + "display_name": "intugle", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "lastEditStatus": { + "authorEmail": "RAPHAEL.TONY@INTUGLE.AI", + "authorId": "9200264168148", + "authorName": "RAPHAEL.TONY", + "lastEditTime": 1759433896509, + "notebookId": "vk6kvtijqawizengiokk", + "sessionId": "ce6ad5bc-8f6f-4bfd-93a8-bd6841b44952" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/quickstart_native_snowflake.ipynb b/notebooks/quickstart_native_snowflake.ipynb index 6d685ec..d63f245 100644 --- a/notebooks/quickstart_native_snowflake.ipynb +++ b/notebooks/quickstart_native_snowflake.ipynb @@ -203,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "a55fa189-8c6c-45b0-a58c-f58bd448985d", "metadata": { "language": "python", @@ -215,6 +215,7 @@ " \"\"\"Append the base URL to the table name.\"\"\"\n", " return {\n", " \"identifier\": table_name,\n", + " \"type\": \"snowflake\"\n", " }\n", "\n", "\n", diff --git a/pyproject.toml b/pyproject.toml index 5d2344b..bc64354 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "intugle" -version = "1.0.3" +version = "1.0.4" authors = [ { name="Intugle", email="hello@intugle.ai" }, ] @@ -49,13 +49,18 @@ dependencies = [ "scikit-learn==1.7.1", "langchain[anthropic,google-genai,openai]>=0.3.27", "qdrant-client>=1.15.1", - "rich>=14.1.0" + "rich>=14.1.0", ] [project.optional-dependencies] snowflake = [ "snowflake-snowpark-python[pandas]>=1.12.0" ] +databricks = [ + "databricks-sql-connector>=4.1.3", + "pyspark>=3.5.0", + "sqlglot>=27.20.0", +] [project.urls] diff --git a/src/intugle/adapters/adapter.py b/src/intugle/adapters/adapter.py index 9d2fae9..f7fe7db 100644 --- a/src/intugle/adapters/adapter.py +++ b/src/intugle/adapters/adapter.py @@ -48,7 +48,7 @@ def to_df_from_query(self, query: str) -> pd.DataFrame: raise NotImplementedError() @abstractmethod - def create_table_from_query(self, table_name: str, query: str): + def create_table_from_query(self, table_name: str, query: str) -> str: raise NotImplementedError() @abstractmethod diff --git a/src/intugle/adapters/common/models.py b/src/intugle/adapters/common/models.py new file mode 100644 index 0000000..c612780 --- /dev/null +++ b/src/intugle/adapters/common/models.py @@ -0,0 +1,10 @@ +from pydantic import BaseModel + + +class ResolvedRelationship(BaseModel): + """Represents a relationship with a clearly identified parent (one) and child (many) side.""" + + parent_table: str + parent_column: str + child_table: str + child_column: str diff --git a/src/intugle/adapters/common/relationships.py b/src/intugle/adapters/common/relationships.py new file mode 100644 index 0000000..691bb54 --- /dev/null +++ b/src/intugle/adapters/common/relationships.py @@ -0,0 +1,55 @@ +import re + +from typing import Dict, Optional + +from intugle.adapters.common.models import ResolvedRelationship +from intugle.models.resources.relationship import Relationship +from intugle.models.resources.source import Source + + +def clean_name(name: str) -> str: + """Cleans a string to be a valid SQL identifier by replacing non-alphanumeric characters with underscores.""" + return re.sub(r'[^a-zA-Z0-9_]', '_', name) + + +def resolve_relationship_direction( + rel: Relationship, sources: Dict[str, Source] +) -> Optional[ResolvedRelationship]: + """ + Determines the direction of a relationship by identifying the primary key. + + Args: + rel: The relationship object from the manifest. + sources: A dictionary of all sources from the manifest. + + Returns: + A ResolvedRelationship object with parent/child identified, or None if it's not a valid FK relationship. + """ + source_table_info = sources.get(rel.source.table) + target_table_info = sources.get(rel.target.table) + + if not source_table_info or not target_table_info: + return None + + # Case 1: The source column is the primary key of the source table. + # This means the target table is the child (many side). + if source_table_info.table.key and source_table_info.table.key == rel.source.column: + return ResolvedRelationship( + parent_table=rel.source.table, + parent_column=rel.source.column, + child_table=rel.target.table, + child_column=rel.target.column, + ) + + # Case 2: The target column is the primary key of the target table. + # This means the source table is the child (many side). + elif target_table_info.table.key and target_table_info.table.key == rel.target.column: + return ResolvedRelationship( + parent_table=rel.target.table, + parent_column=rel.target.column, + child_table=rel.source.table, + child_column=rel.source.column, + ) + + # If neither side is a primary key, it's not a valid FK relationship for our purposes. + return None diff --git a/src/intugle/adapters/factory.py b/src/intugle/adapters/factory.py index 3b0bb65..3521932 100644 --- a/src/intugle/adapters/factory.py +++ b/src/intugle/adapters/factory.py @@ -22,6 +22,7 @@ def import_module(name: str) -> ModuleInterface: "intugle.adapters.types.pandas.pandas", "intugle.adapters.types.duckdb.duckdb", "intugle.adapters.types.snowflake.snowflake", + "intugle.adapters.types.databricks.databricks", ] diff --git a/src/intugle/adapters/models.py b/src/intugle/adapters/models.py index b5393f9..0ccc1ad 100644 --- a/src/intugle/adapters/models.py +++ b/src/intugle/adapters/models.py @@ -5,11 +5,12 @@ from pydantic import BaseModel, Field +from intugle.adapters.types.databricks.models import DatabricksConfig from intugle.adapters.types.duckdb.models import DuckdbConfig from intugle.adapters.types.snowflake.models import SnowflakeConfig # FIXME load dynamically -DataSetData = pd.DataFrame | DuckdbConfig | SnowflakeConfig +DataSetData = pd.DataFrame | DuckdbConfig | SnowflakeConfig | DatabricksConfig class ProfilingOutput(BaseModel): diff --git a/src/intugle/adapters/types/databricks/databricks.py b/src/intugle/adapters/types/databricks/databricks.py new file mode 100644 index 0000000..ed5f8de --- /dev/null +++ b/src/intugle/adapters/types/databricks/databricks.py @@ -0,0 +1,421 @@ +import re +import time + +from typing import TYPE_CHECKING, Any, Optional + +import numpy as np +import pandas as pd + +from intugle.adapters.adapter import Adapter +from intugle.adapters.common.relationships import clean_name, resolve_relationship_direction +from intugle.adapters.factory import AdapterFactory +from intugle.adapters.models import ColumnProfile, DataSetData, ProfilingOutput +from intugle.adapters.types.databricks.models import ( + DatabricksConfig, + DatabricksNotebookConfig, + DatabricksSQLConnectorConfig, +) +from intugle.adapters.utils import convert_to_native +from intugle.core import settings +from intugle.core.utilities.processing import string_standardization + +if TYPE_CHECKING: + from intugle.analysis.models import DataSet + from intugle.models.manifest import Manifest + +try: + from pyspark.sql import SparkSession + PYSPARK_AVAILABLE = True +except ImportError: + PYSPARK_AVAILABLE = False + +try: + from databricks import sql + DATABRICKS_SQL_AVAILABLE = True +except ImportError: + DATABRICKS_SQL_AVAILABLE = False + +try: + from sqlglot import transpile + SQLGLOT_AVAILABLE = True +except ImportError: + SQLGLOT_AVAILABLE = False + + +DATABRICKS_AVAILABLE = PYSPARK_AVAILABLE and DATABRICKS_SQL_AVAILABLE and SQLGLOT_AVAILABLE + + +def clean_tag(name: str) -> str: + """Cleans a string to be a valid Databricks tag name.""" + return re.sub(r'[^a-zA-Z0-9_ ]', '_', name) + + +class DatabricksAdapter(Adapter): + _instance = None + _initialized = False + + def __new__(cls, *args, **kwargs): + if not cls._instance: + cls._instance = super().__new__(cls) + return cls._instance + + def __init__(self): + if self._initialized: + return + + if not DATABRICKS_AVAILABLE: + raise ImportError( + "Databricks dependencies are not installed. Please run 'pip install intugle[databricks]'.." + ) + + self.spark: Optional["SparkSession"] = None + self.connection: Optional[Any] = None + self.catalog: Optional[str] = None + self.schema: Optional[str] = None + self.connect() + self._initialized = True + + def connect(self): + connection_parameters_dict = settings.PROFILES.get("databricks", {}) + if not connection_parameters_dict: + raise ValueError( + "Could not create Databricks connection. No 'databricks' section found in profiles.yml." + ) + + # Try to get an active Spark session (for notebook environment) + if PYSPARK_AVAILABLE: + try: + self.spark = SparkSession.getActiveSession() + if self.spark: + print("Found active Spark session. Using it for execution.") + params = DatabricksNotebookConfig.model_validate(connection_parameters_dict) + self.catalog = params.catalog + self.schema = params.schema + return + except (AttributeError, TypeError): + self.spark = None + + # If no active Spark session, create a SQL connector connection (for external environment) + if not self.spark: + if not DATABRICKS_SQL_AVAILABLE: + raise ImportError( + "databricks-sql-connector is not installed. Please run 'pip install intugle[databricks]' to connect from outside a Databricks notebook." + ) + print("No active Spark session found. Creating a new SQL connector connection.") + params = DatabricksSQLConnectorConfig.model_validate(connection_parameters_dict) + self.catalog = params.catalog + self.schema = params.schema + self.connection = sql.connect( + server_hostname=params.host, http_path=params.http_path, access_token=params.token + ) + + def _get_fqn(self, identifier: str) -> str: + """Gets the fully qualified name for a table identifier.""" + # An identifier is already fully qualified if it contains a dot. + if "." in identifier: + return identifier + + # Backticks are used to handle reserved keywords and special characters. + safe_schema = f"`{self.schema}`" + safe_identifier = f"`{identifier}`" + + if self.catalog: + safe_catalog = f"`{self.catalog}`" + return f"{safe_catalog}.{safe_schema}.{safe_identifier}" + + return f"{safe_schema}.{safe_identifier}" + + @staticmethod + def check_data(data: Any) -> DatabricksConfig: + try: + data = DatabricksConfig.model_validate(data) + except Exception: + raise TypeError("Input must be a Databricks config.") + return data + + def _execute_sql(self, query: str) -> list[Any]: + if self.spark: + if self.catalog: + self.spark.sql(f"USE CATALOG `{self.catalog}`") + if self.schema: + self.spark.sql(f"USE `{self.schema}`") + return self.spark.sql(query).collect() + elif self.connection: + with self.connection.cursor() as cursor: + if self.catalog: + cursor.execute(f"USE CATALOG `{self.catalog}`") + if self.schema: + cursor.execute(f"USE `{self.schema}`") + cursor.execute(query) + try: + return cursor.fetchall() + except Exception: + return [] + raise ConnectionError("No active Databricks connection.") + + def _get_pandas_df(self, query: str) -> pd.DataFrame: + if self.spark: + if self.catalog: + self.spark.sql(f"USE CATALOG `{self.catalog}`") + if self.schema: + self.spark.sql(f"USE `{self.schema}`") + return self.spark.sql(query).toPandas() + elif self.connection: + with self.connection.cursor() as cursor: + if self.catalog: + cursor.execute(f"USE CATALOG `{self.catalog}`") + if self.schema: + cursor.execute(f"USE `{self.schema}`") + cursor.execute(query) + data = cursor.fetchall() + columns = [column[0] for column in cursor.description] + return pd.DataFrame(data, columns=columns) + raise ConnectionError("No active Databricks connection.") + + def profile(self, data: DatabricksConfig, table_name: str) -> ProfilingOutput: + data = self.check_data(data) + fqn = self._get_fqn(data.identifier) + if self.spark: + table = self.spark.table(fqn) + total_count = table.count() + columns = table.columns + dtypes = {field.name: str(field.dataType) for field in table.schema.fields} + else: + rows = self._execute_sql(f"DESCRIBE TABLE {fqn}") + columns = [row.col_name for row in rows] + dtypes = {row.col_name: row.data_type for row in rows} + total_count = self._execute_sql(f"SELECT COUNT(*) FROM {fqn}")[0][0] + + return ProfilingOutput( + count=total_count, + columns=columns, + dtypes=dtypes, + ) + + def column_profile( + self, + data: DatabricksConfig, + table_name: str, + column_name: str, + total_count: int, + sample_limit: int = 10, + dtype_sample_limit: int = 10000, + ) -> Optional[ColumnProfile]: + data = self.check_data(data) + fqn = self._get_fqn(data.identifier) + start_ts = time.time() + + # Null and distinct counts + query = f""" + SELECT + COUNT(CASE WHEN `{column_name}` IS NULL THEN 1 END) as null_count, + COUNT(DISTINCT `{column_name}`) as distinct_count + FROM {fqn} + """ + result = self._execute_sql(query)[0] + null_count = result.null_count + distinct_count = result.distinct_count + not_null_count = total_count - null_count + + # Sampling + sample_query = f""" + SELECT DISTINCT CAST(`{column_name}` AS STRING) FROM {fqn} WHERE `{column_name}` IS NOT NULL LIMIT {dtype_sample_limit} + """ + distinct_values_result = self._execute_sql(sample_query) + distinct_values = [row[0] for row in distinct_values_result] + + if distinct_count > 0: + distinct_sample_size = min(distinct_count, dtype_sample_limit) + sample_data = list(np.random.choice(distinct_values, distinct_sample_size, replace=False)) + else: + sample_data = [] + + dtype_sample = None + if distinct_count >= dtype_sample_limit: + dtype_sample = sample_data + elif distinct_count > 0 and not_null_count > 0: + remaining_sample_size = dtype_sample_limit - distinct_count + additional_samples_query = f""" + SELECT CAST(`{column_name}` AS STRING) FROM {fqn} WHERE `{column_name}` IS NOT NULL ORDER BY RAND() LIMIT {remaining_sample_size} + """ + additional_samples_result = self._execute_sql(additional_samples_query) + additional_samples = [row[0] for row in additional_samples_result] + dtype_sample = list(distinct_values) + additional_samples + else: + dtype_sample = [] + + native_sample_data = convert_to_native(sample_data) + native_dtype_sample = convert_to_native(dtype_sample) + business_name = string_standardization(column_name) + + return ColumnProfile( + column_name=column_name, + table_name=table_name, + business_name=business_name, + null_count=null_count, + count=total_count, + distinct_count=distinct_count, + uniqueness=distinct_count / total_count if total_count > 0 else 0.0, + completeness=not_null_count / total_count if total_count > 0 else 0.0, + sample_data=native_sample_data[:sample_limit], + dtype_sample=native_dtype_sample, + ts=time.time() - start_ts, + ) + + def load(self, data: DatabricksConfig, table_name: str): + self.check_data(data) + # No-op, we assume the table already exists in Databricks. + + def execute(self, query: str): + return self._execute_sql(query) + + def to_df(self, data: DatabricksConfig, table_name: str) -> pd.DataFrame: + data = self.check_data(data) + fqn = self._get_fqn(data.identifier) + return self._get_pandas_df(f"SELECT * FROM {fqn}") + + def to_df_from_query(self, query: str) -> pd.DataFrame: + return self._get_pandas_df(query) + + def create_table_from_query(self, table_name: str, query: str): + fqn = self._get_fqn(table_name) + transpiled_sql = transpile(query, write="databricks")[0] + self._execute_sql(f"CREATE OR REPLACE VIEW {fqn} AS {transpiled_sql}") + return transpiled_sql + + def create_new_config_from_etl(self, etl_name: str) -> "DataSetData": + fqn = self._get_fqn(etl_name) + return DatabricksConfig(identifier=fqn) + + def deploy_semantic_model( + self, + manifest: "Manifest", + sync_glossary: bool = True, + sync_tags: bool = False, + set_primary_keys: bool = True, + set_foreign_keys: bool = True, + **kwargs, + ): + if sync_glossary or sync_tags: + self._sync_metadata(manifest, sync_glossary, sync_tags) + if set_primary_keys: + self._set_primary_keys(manifest) + if set_foreign_keys: + self._set_foreign_keys(manifest) + + def _sync_metadata(self, manifest: "Manifest", sync_glossary: bool, sync_tags: bool): + """ + Syncs metadata (comments for glossaries, and tags) from the manifest to the physical Databricks tables. + """ + print("Syncing metadata to Databricks tables...") + + for source in manifest.sources.values(): + fqn = self._get_fqn(source.table.name) + + # Set table comment + if sync_glossary and source.table.description: + table_comment = source.table.description.replace("'", "\\'") + self._execute_sql(f"COMMENT ON TABLE {fqn} IS '{table_comment}'") #Works for views too + + # Set column comments and tags + for column in source.table.columns: + if sync_glossary and column.description: + col_comment = column.description.replace("'", "\\'") + self._execute_sql(f"COMMENT ON COLUMN {fqn}.`{column.name}` IS '{col_comment}'") + + if sync_tags and column.tags: + cleaned_tags = [clean_tag(tag) for tag in column.tags] + tag_assignments = ", ".join([f"'{tag}'" for tag in cleaned_tags]) + + # FIXME: Need to differentiate between TABLES and VIEWS for setting tags + try: + self._execute_sql(f"ALTER TABLE {fqn} ALTER COLUMN `{column.name}` SET TAGS ({tag_assignments})") + except Exception as e: + try: + self._execute_sql(f"ALTER VIEW {fqn} ALTER COLUMN `{column.name}` SET TAGS ({tag_assignments})") + except Exception as e: + print(f"Could not set tags '{tag_assignments}' on {fqn}.`{column.name}`: {e}") + + + print("Metadata sync complete.") + + def _set_primary_keys(self, manifest: "Manifest"): + """ + Sets primary key constraints on the tables based on the manifest. + """ + print("Setting primary key constraints...") + for source in manifest.sources.values(): + if not source.table.key or not isinstance(source.table.key, str): + print(f"Skipping primary key for table '{source.table.name}' due to missing or invalid key.") + continue + + fqn = self._get_fqn(source.table.name) + pk_column = source.table.key + constraint_name = f"pk_{source.table.name}" + try: + # First, ensure the column is not nullable + self._execute_sql(f"ALTER TABLE {fqn} ALTER COLUMN `{pk_column}` SET NOT NULL") + # Then, add the primary key constraint + self._execute_sql(f"ALTER TABLE {fqn} ADD CONSTRAINT {constraint_name} PRIMARY KEY (`{pk_column}`)") + print(f"Set primary key on {fqn} (`{pk_column}`)") + except Exception as e: + print(f"Could not set primary key for {fqn}: {e}") + print("Primary key setting complete.") + + def _set_foreign_keys(self, manifest: "Manifest"): + """ + Sets foreign key constraints between tables based on the manifest relationships. + """ + print("Setting foreign key constraints...") + for rel in manifest.relationships.values(): + resolved = resolve_relationship_direction(rel, manifest.sources) + if not resolved: + print(f"Skipping invalid or ambiguous relationship '{rel.name}'.") + continue + + try: + child_fqn = self._get_fqn(resolved.child_table) + parent_fqn = self._get_fqn(resolved.parent_table) + constraint_name = f"fk_{rel.name}" + cleaned_constraint_name = clean_name(constraint_name) + + self._execute_sql( + f"ALTER TABLE {child_fqn} ADD CONSTRAINT {cleaned_constraint_name} " + f"FOREIGN KEY (`{resolved.child_column}`) REFERENCES {parent_fqn} (`{resolved.parent_column}`)" + ) + except Exception as e: + print(f"Could not set foreign key for relationship {rel.name}: {e}") + print("Foreign key setting complete.") + + def intersect_count(self, table1: "DataSet", column1_name: str, table2: "DataSet", column2_name: str) -> int: + table1_adapter = self.check_data(table1.data) + table2_adapter = self.check_data(table2.data) + + fqn1 = self._get_fqn(table1_adapter.identifier) + fqn2 = self._get_fqn(table2_adapter.identifier) + + query = f""" + SELECT COUNT(*) FROM ( + SELECT DISTINCT `{column1_name}` FROM {fqn1} WHERE `{column1_name}` IS NOT NULL + INTERSECT + SELECT DISTINCT `{column2_name}` FROM {fqn2} WHERE `{column2_name}` IS NOT NULL + ) + """ + return self._execute_sql(query)[0][0] + + def get_details(self, data: DatabricksConfig): + data = self.check_data(data) + return data.model_dump() + + +def can_handle_databricks(df: Any) -> bool: + try: + DatabricksConfig.model_validate(df) + return True + except Exception: + return False + + +def register(factory: AdapterFactory): + if DATABRICKS_AVAILABLE: + factory.register("databricks", can_handle_databricks, DatabricksAdapter) \ No newline at end of file diff --git a/src/intugle/adapters/types/databricks/models.py b/src/intugle/adapters/types/databricks/models.py new file mode 100644 index 0000000..1566bbe --- /dev/null +++ b/src/intugle/adapters/types/databricks/models.py @@ -0,0 +1,21 @@ +from typing import Optional + +from intugle.common.schema import SchemaBase + + +class DatabricksSQLConnectorConfig(SchemaBase): + host: str + http_path: str + token: str + schema: str + catalog: Optional[str] = None + + +class DatabricksNotebookConfig(SchemaBase): + schema: str + catalog: Optional[str] = None + + +class DatabricksConfig(SchemaBase): + identifier: str + type: str = "databricks" diff --git a/src/intugle/adapters/types/duckdb/duckdb.py b/src/intugle/adapters/types/duckdb/duckdb.py index 5966d19..301517f 100644 --- a/src/intugle/adapters/types/duckdb/duckdb.py +++ b/src/intugle/adapters/types/duckdb/duckdb.py @@ -1,6 +1,6 @@ import time -from typing import Any, Optional +from typing import TYPE_CHECKING, Any, Optional import duckdb import numpy as np @@ -18,6 +18,9 @@ from intugle.common.exception import errors from intugle.core.utilities.processing import string_standardization +if TYPE_CHECKING: + from intugle.analysis.models import DataSet + class DuckdbAdapter(Adapter): @@ -240,6 +243,7 @@ def to_df_from_query(self, query: str) -> pd.DataFrame: def create_table_from_query(self, table_name: str, query: str): duckdb.sql(f'CREATE OR REPLACE VIEW "{table_name}" AS {query}') + return query def create_new_config_from_etl(self, etl_name: str) -> "DataSetData": return DuckdbConfig(path=etl_name, type="table") diff --git a/src/intugle/adapters/types/snowflake/snowflake.py b/src/intugle/adapters/types/snowflake/snowflake.py index 3f384cb..9797bcf 100644 --- a/src/intugle/adapters/types/snowflake/snowflake.py +++ b/src/intugle/adapters/types/snowflake/snowflake.py @@ -11,6 +11,7 @@ from intugle.models.manifest import Manifest from intugle.adapters.adapter import Adapter +from intugle.adapters.common.relationships import resolve_relationship_direction from intugle.adapters.factory import AdapterFactory from intugle.adapters.models import ( ColumnProfile, @@ -37,7 +38,18 @@ class SnowflakeAdapter(Adapter): + _instance = None + _initialized = False + + def __new__(cls, *args, **kwargs): + if not cls._instance: + cls._instance = super().__new__(cls) + return cls._instance + def __init__(self): + if self._initialized: + return + if not SNOWFLAKE_AVAILABLE: raise ImportError("Snowflake dependencies are not installed. Please run 'pip install intugle[snowflake]'.") @@ -45,6 +57,7 @@ def __init__(self): self.database: Optional[str] = None self.schema: Optional[str] = None self.connect() + self._initialized = True def connect(self): try: @@ -179,6 +192,7 @@ def _clean_column_quotes(sql: str) -> str: query = _clean_column_quotes(query) self.session.sql(f"CREATE OR REPLACE TABLE {table_name} AS {query}").collect() + return query def create_new_config_from_etl(self, etl_name: str) -> "DataSetData": return SnowflakeConfig(identifier=etl_name) @@ -268,28 +282,16 @@ def deploy_semantic_model(self, manifest: "Manifest", **kwargs): # -- RELATIONSHIPS clause -- relationship_clauses = [] for rel in manifest.relationships.values(): - source_table_info = manifest.sources.get(rel.source.table) - target_table_info = manifest.sources.get(rel.target.table) - - if not source_table_info or not target_table_info: + resolved = resolve_relationship_direction(rel, manifest.sources) + if not resolved: continue - # Determine which table is the 'one' side (contains the PK for the join) - if source_table_info.table.key == rel.source.column: - # source is the 'one' side (referenced table) - ref_table_alias = clean_name(rel.source.table) - ref_column = clean_name(rel.source.column) - table_alias = clean_name(rel.target.table) - column = clean_name(rel.target.column) - elif target_table_info.table.key == rel.target.column: - # target is the 'one' side (referenced table) - ref_table_alias = clean_name(rel.target.table) - ref_column = clean_name(rel.target.column) - table_alias = clean_name(rel.source.table) - column = clean_name(rel.source.column) - else: - # This is not a valid FK relationship for the semantic view, skip it - continue + # The table with the FK is the "referencing" table + table_alias = clean_name(resolved.child_table) + column = clean_name(resolved.child_column) + # The table with the PK is the "referenced" table + ref_table_alias = clean_name(resolved.parent_table) + ref_column = clean_name(resolved.parent_column) clause = f"{clean_name(rel.name)} AS {table_alias}({column}) REFERENCES {ref_table_alias}({ref_column})" relationship_clauses.append(clause) diff --git a/src/intugle/data_product.py b/src/intugle/data_product.py index bc104a7..bf76494 100644 --- a/src/intugle/data_product.py +++ b/src/intugle/data_product.py @@ -86,7 +86,7 @@ def build(self, etl: ETLModel) -> DataSet: sql_query = self.generate_query(etl) # 3. Materialize the query as a new table in the target database - execution_adapter.create_table_from_query(etl.name, sql_query) + dialect_sql = execution_adapter.create_table_from_query(etl.name, sql_query) # 4. Create a new config object pointing to the newly created table new_config = execution_adapter.create_new_config_from_etl(etl.name) @@ -94,7 +94,7 @@ def build(self, etl: ETLModel) -> DataSet: # 5. Return a new DataSet pointing to the materialized table result_dataset = DataSet(data=new_config, name=etl.name) # Attach the query for inspection - result_dataset.sql_query = sql_query + result_dataset.sql_query = dialect_sql return result_dataset diff --git a/src/intugle/exporters/snowflake.py b/src/intugle/exporters/snowflake.py index 4754aac..467d1c1 100644 --- a/src/intugle/exporters/snowflake.py +++ b/src/intugle/exporters/snowflake.py @@ -1,11 +1,15 @@ import re -from .base import Exporter -from intugle.libs.smart_query_generator.models.models import CategoryType + +from intugle.adapters.common.relationships import resolve_relationship_direction from intugle.core import settings +from intugle.libs.smart_query_generator.models.models import CategoryType from intugle.models.resources.relationship import RelationshipType +from .base import Exporter + RESERVED_WORDS = {'start', 'end', 'select', 'from', 'where', 'order', 'group', 'join', 'table', 'on'} + def clean_name(name: str) -> str: """Cleans an identifier to be a safe logical name for the Snowflake Semantic Model.""" cleaned = name.strip().strip('"') @@ -19,11 +23,13 @@ def clean_name(name: str) -> str: return f'_{cleaned}' return cleaned + def quote_identifier(name: str) -> str: """Ensure the identifier is wrapped in exactly one pair of double quotes.""" clean_name = name.strip().strip('"') return f'"{clean_name}"' + # Mapping from our types to Snowflake's expected data types DATA_TYPE_MAPPING = { "integer": "NUMBER", @@ -36,6 +42,7 @@ def quote_identifier(name: str) -> str: # Add other mappings as necessary } + class SnowflakeExporter(Exporter): def export(self, **kwargs) -> dict: """ @@ -73,7 +80,7 @@ def export(self, **kwargs) -> dict: # Map columns to dimensions and facts for column in source.table.columns: - snowflake_type = DATA_TYPE_MAPPING.get(column.type, "TEXT") # Default to TEXT + snowflake_type = DATA_TYPE_MAPPING.get(column.type, "TEXT") # Default to TEXT if column.category == CategoryType.dimension: dimension = { "name": clean_name(column.name), @@ -96,40 +103,18 @@ def export(self, **kwargs) -> dict: # Process relationships for rel in manifest.relationships.values(): - source_table_name = rel.source.table - target_table_name = rel.target.table - - source_table_info = manifest.sources.get(source_table_name) - target_table_info = manifest.sources.get(target_table_name) - - if not source_table_info or not target_table_info: - continue - - # Determine which table is the 'one' side (contains the PK for the join) - if source_table_info.table.key == rel.source.column: - # source is the 'one' side - right_table = source_table_name - right_column = rel.source.column - left_table = target_table_name - left_column = rel.target.column - elif target_table_info.table.key == rel.target.column: - # target is the 'one' side - right_table = target_table_name - right_column = rel.target.column - left_table = source_table_name - left_column = rel.source.column - else: - # 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