diff --git a/site/sigmaguides/src/administration_materialization/administration_materialization.md b/site/sigmaguides/src/administration_materialization/administration_materialization.md
index a8c200ca..f60efdb7 100644
--- a/site/sigmaguides/src/administration_materialization/administration_materialization.md
+++ b/site/sigmaguides/src/administration_materialization/administration_materialization.md
@@ -6,10 +6,9 @@ environments: web
status: Published
feedback link: https://github.com/sigmacomputing/sigmaquickstarts/issues
tags: default
-lastUpdated: 2023-06-06
+lastUpdated: 2025-08-21
# Materialization with Sigma
-
## Overview
Duration: 5
@@ -21,13 +20,15 @@ We will define it, provide some guidance on the why/when to use it and who sets
Lastly, we will step through using Sigma and Snowflake to materialize some data so that the workflow in Sigma is understood.
-IMPORTANT: IMPORTANT:
-For more information on Sigma's product release strategy, see [Sigma product releases.](https://help.sigmacomputing.com/docs/sigma-product-releases)
+For more information on Sigma's product release strategy, see [Sigma product releases](https://help.sigmacomputing.com/docs/sigma-product-releases)
- ### Target Audience
-Sigma administrators who are interested in improving performance when working with complex datasets or just generally interest in learning more about materialization.
+If something is not working as you expect, here's how to [contact Sigma support](https://help.sigmacomputing.com/docs/sigma-support)
+
+### Target Audience
+Sigma administrators who are interested in improving performance when working with complex datasets or just generally interest in learning more about materialization.
### Prerequisites
@@ -36,7 +37,7 @@ Sigma administrators who are interested in improving performance when working wi
Access to your Sigma environment.
Some familiarity with Sigma is assumed. Not all steps will be shown as the basics are assumed to be understood.
A Snowflake account with the proper administrative and security admin access.
- Write access must be enabled on your Sigma dataset’s connection.
+ Write access must be enabled on the Sigma connection to the warehouse.
@@ -45,26 +46,27 @@ Sigma administrators who are interested in improving performance when working wi
[Sigma Free Trial](https://www.sigmacomputing.com/free-trial/) [Snowflake Free Trial](https://signup.snowflake.com/)
-### What You’ll Learn
-This QuickStart discussed the features and benefits of using materialization in Sigma and also how to configure and schedule materializations to improve the speed and performance of your complex reports.
-
## Background
Duration: 20
-The fundamental concept of storing pre-calculated data for performance optimization purposes (ie: caching) has been around for several decades, with advancements and optimizations occurring over time.
+The concept of storing pre-calculated data for performance optimization (similar to caching) has been around for decades, with many advancements and refinements over time.
+
+Materialization is a form of caching where query results are written into a table in a data warehouse and refreshed at regular intervals (often daily).
-Materialization is a type of caching, where query results are written into a table in a data warehouse, and then refreshed at regular intervals (often daily).
+This pre-calculated or pre-aggregated data can be accessed more quickly and efficiently than recomputing results every time a query is executed.
**This pre-calculated or pre-aggregated data can be accessed more efficiently and quickly than recomputing the results every time the query is executed.**
-In general, the main advantages of materialization are:
-
- Improved query performance: Materialized tables eliminate the need for repetitive computations, resulting in faster query response times.Reduced resource consumption: By precomputing and storing the results, materialization reduces the computational resources required for query execution, leading to more efficient resource utilization.Query optimization: Materialized tables can be indexed and optimized for specific query patterns, further enhancing performance.Enhanced concurrency: Since the materialized tables store precomputed results, multiple queries can run concurrently without interfering with each other, improving overall system concurrency.Simplified data pipelines: Materialization can simplify complex data pipelines by providing a straightforward way to store and reuse intermediate or final query results.
+### Advantages of Materialization
+- **Improved query performance:** Eliminates repetitive computations, resulting in faster response times.
+
+- **Reduced resource consumption:** Precomputing and storing results lowers the compute cost of running queries.
+
+- **Query optimization:** Materialized tables can be indexed or tuned for specific query patterns.
+
+- **Enhanced concurrency:** Precomputed results allow multiple queries to run simultaneously without contention.
+
+- **Simplified data pipelines:** Provides a straightforward way to store and reuse intermediate or final query results.
@@ -72,45 +74,43 @@ In general, the main advantages of materialization are:
## Considerations
Duration: 20
-Materialization is a common strategy for improving performance of interactive queries, and it is most effective in certain situations.
+Materialization is a common strategy for improving the performance of interactive queries, but it is most effective in specific situations.
-You should consider materializing data in situations where it can provide **tangible benefits** in terms of query performance, data analysis, and resource management.
+You should consider materializing data when it can provide tangible benefits in query performance, data analysis, or resource management.
-
-IMPORTANT: IMPORTANT:
-At Sigma, we see customers immediately benefiting from materialization in these use cases:
+At Sigma, we see customers immediately benefit from materialization in use cases such as:
+- Flattening complex joins
+- Lowering the grain of data (e.g., materializing at an aggregated level)
+- Flattening slow calculations (such as JSON extracts)
+- Applying a permanent, restrictive filter (e.g., 100M rows exist but only 50K are relevant to the analysis)
-
- Flattening complex joins
- Lowering the grain of data (materializing an aggregated table level)
- Flattening out slow calculations (like json extracts)
- When applying a permanent, restrictive filter. For example, when there are 100M rows, but only 50k rows are relevant to the analysis)
-
+### Common Use Cases for Materialization
-There is a broad range of potential use cases that benefit from materializing. Here are the most common, should you want to know more:
+**Complex and Resource-Intensive Queries**
- Complex and Resource-Intensive Queries: Data Analysis and Reporting: Time-Dependent Data: Resource Management: Matching the Grain to the Need: Frequently Accessed Data:: If certain datasets or views are accessed frequently by multiple users or applications, materializing the data can help reduce the load on the underlying systems, assuming other materialization criteria are met.
+**Data Analysis and Reporting**
-IMPORTANT:
+**Time-Dependent Data**
-NOTE: Some other exceptions apply:
+**Frequently Accessed Data** IMPORTANT:
+
+ NOTE: Other limitations: CurrentTimeZone(), CurrentUser*()) cannot be materialized.
+
+It can be helpful to include a column that records the materialization run date. Use the Today() function to add this automatically.
[Help on Today() function](https://help.sigmacomputing.com/docs/today)
@@ -120,23 +120,25 @@ Sometimes it is useful to include a column that is populated with the date when
## Options
Duration: 20
-In the software market, there are several options available for caching/materialization, depending on your specific needs and the technology stack you are working with.
+In the software market, there are several options available for caching and materialization, depending on your specific needs and the technology stack you are working with.
- Here are some common options for materialization:
-
- Data Warehousing Solutions (and most RDBMS): In-Memory Databases: Caching Systems: Custom Data Pipelines and ETL Processes: In some cases, you may need to implement custom materialization solutions using data pipelines or Extract-Transform-Load (ETL) processes. Tools like DBT Labs, Apache Airflow, Apache Spark, or custom scripting can be utilized to schedule and automate the materialization process, ensuring that the materialized data remains up-to-date.
-
-IMPORTANT:
+**In-Memory Databases** IMPORTANT: Sigma for materialization because its user interface makes it easy to set up, monitor, and manage directly from a browser.
@@ -144,30 +146,31 @@ The availability and specific features of materialization options may vary depen
## Use Sigma to Materialize?
Duration: 20
-Given all the available choices, this can feel like a complex decision but there are some points that will help make it easier to decide.
-
-Some clients prefer a well governed, centralized materialization strategy, using a single, dedicated tool of their choice and that is fine too.
+With so many available options, deciding where to materialize data can feel complex—but there are some guidelines that can simplify the choice.
-For some situations, both solutions can work together to save time and get projects completed faster. some clients choose to use Sigma-based materialization to support prototyping and rapid development cycles, because they almost no development time and can be done by a larger team.
+Some clients prefer a well-governed, centralized materialization strategy, using a single dedicated tool of their choice. That approach works well in many environments.
-For example, you can allow materialization using Sigma, during early development phases, then, later on, move some of the most used materialization logic into the warehouse. That will allow you to have a rapid development cycle, but still having a well governed environment for production usage.
+In other situations, both approaches can complement each other. For example, some clients use Sigma-based materialization for prototyping and rapid development, since it requires little to no engineering effort and can be done directly by analysts or business teams. Later, once workflows mature, materialization logic can be migrated into the warehouse for long-term production use.
+This hybrid approach provides the speed and agility of Sigma during development while ensuring a governed, centralized strategy in production.
There are some clear benefits to using Sigma to materialize.
-The benefits of materializing from Sigma:
-
- Setup in a browser using Sigma makes it very simple and quick process Does not require data engineering expertise means a faster development cycle Role-base access control limits who can materialize The materialized data stays in your CDW Sigma manages all the complexity automatically, saving time and effort
+### Benefits of Using Sigma to Materialize
-If you have chosen to use Sigma to materialize, it is important to control who in Sigma is able to materialize. By default, only Sigma Administrators are able to materialize. You can also create a custom account type “Creators who can Materialize” and grant them the materialization permission.
+- Simple setup in a browser – No special tools required; start materializing in minutes
+- No data engineering expertise needed – Enables faster development cycles driven by a broader team
+- Role-based access control (RBAC) – Restricts who can materialize data
+- Data stays in your cloud data warehouse (CDW) – No new storage systems required
+- Automated complexity management – Sigma handles refreshes, dependencies, and orchestration behind the scenes
-The ability to materialize in Sigma should be governed in some way - you typically don’t want a large number of creators to materialize hundreds or thousands of potentially duplicate objects. It is common to grant the ability to materialize to a certain, smaller number of creators, using Sigma's RBAC system.
+### Governance Considerations
+If you choose to use Sigma for materialization, it’s important to control who has permission to materialize.
+- By default, only Sigma Administrators can materialize.
+- You can also create a custom account type (e.g., Creators who can Materialize) and grant that group the materialization permission.
+- To avoid uncontrolled growth, it’s best to limit this ability to a smaller set of creators, rather than allowing many users to create hundreds of duplicate materializations.
+Sigma’s RBAC system provides the flexibility to strike the right balance between agility and governance.
@@ -175,70 +178,84 @@ The ability to materialize in Sigma should be governed in some way - you typical
## Initial Setup
Duration: 20
-Lets start to go deeper into using materialization with Sigma.
+Materialization allows you to write datasets and workbook elements back to your warehouse as tables, reducing compute costs and improving query performance. By materializing, your data warehouse avoids recomputing the dataset every time it is used in a Sigma element or descendant analysis.
-Materializations allows you to write datasets and workbook elements back to your warehouse as tables which can reduce compute costs. Materialization enhances query performance by allowing your data warehouse to avoid recomputing the dataset when it's used by an element or a in descendant Sigma analysis.
+Materializations are stored directly in your warehouse, inside a scratch workspace schema automatically managed by Sigma.
-Materializations are stored in your warehouse and saved in scratch workspace schema ***automatically managed by Sigma***.
+**Sigma’s query compiler automatically and transparently uses the latest materialization.**
-**Sigma's query compiler automatically and transparently uses the latest materialization.**
+All data displayed in Sigma is always queried directly from the warehouse (Snowflake in this case). However, the complexity of the base query carries through to all subsequent queries, including sorting, filtering, and pagination. To solve this, Sigma datasets can be materialized back to Snowflake.
-All data displayed in Sigma is queried directly from the warehouse; Snowflake in this case.
+**This is different from a dataset view object in Snowflake.**
-The complexity of the base query will carry through in all subsequent queries associated with sorting, filtering, and paginating the data. To solve this, Sigma datasets can be materialized back to Snowflake.
+In Sigma’s implementation, a `CREATE TABLE AS` statement is used to store the result set of the SQL query generated by the dataset. You can also materialize a Sigma table that contains multiple grouping levels by selecting a specific grouping level to materialize.
-This is different from the dataset view object in Snowflake.
+Materializations can be refreshed on a schedule or triggered programmatically via the Sigma API.
-In Sigma’s implementation, a `CREATE TABLE AS` statement is used to store the result set of the SQL query generated by the dataset.
+By materializing a dataset, even extremely complex queries can be flattened into a simple SELECT, meaning all downstream queries run with far less strain.
-It is possible to materialize a Sigma table that contains multiple grouping levels by select a grouping level to materialize.
+ IMPORTANT:
-Materializations can be set on a schedule or triggered via the Sigma API.
+### Prepare Snowflake
+First, we need to create a database and schema in Snowflake to store our materialized data.
-By materializing a dataset an extremely complex query can be flattened down into a simple `SELECT` meaning that all downstream queries are able to run under far less strain.
+Log in to your Snowflake trial account as `ACCOUNTADMIN`.
-
-IMPORTANT:
-
-### Prepare Snowflake
-First, we need to create a database and schema in Snowflake that we can use when materializing data with Sigma.
+Open a new `SQL Worksheet`.
-Login to your Snowflake trial account as `AccountAdmin`.
+Copy/paste the following code:
+```code
+-- OPERATE AS ADMIN
+USE ROLE ACCOUNTADMIN;
-Create a new `SQL Worksheet` and copy/paste the following code into the worksheet:
-```console
-- SELECT WAREHOUSE
-use WAREHOUSE COMPUTE_WH;
+USE WAREHOUSE COMPUTE_WH;
-- CREATE DATABASE AND GRANT USAGE
-CREATE DATABASE if not exists SIGMA_WRITEDB;
-use DATABASE SIGMA_WRITEDB;
-grant usage on database SIGMA_WRITEDB to role ACCOUNTADMIN;
+CREATE DATABASE if not exists QUICKSTARTS;
+USE DATABASE QUICKSTARTS;
+GRANT USAGE ON DATABASE QUICKSTARTS to role sigma_service_account_role;
-- SCHEMA AND GRANT USAGE
-CREATE SCHEMA QUICKSTART;
-grant usage, create table, create view, create stage on schema QUICKSTART to role ACCOUNTADMIN
+CREATE SCHEMA if not exists MATERIALIZATION;
+GRANT USAGE, CREATE TABLE, CREATE VIEW, CREATE STAGE ON SCHEMA MATERIALIZATION TO ROLE sigma_service_account_role
```
-Click the drop arrow in the upper-right corner and select `Run all`:
+Click the dropdown arrow in the upper-right corner and select `Run All`.
IMPORTANT:
-
-NOTE:
+ NOTE:
-Materializations can be paused (manually or after a user-specified period of non-use) and deleted anytime.
+Materializations can be paused (either manually or automatically after a user-specified period of inactivity) and can be deleted at any time.
-
-NOTE:
+ NOTE:
### Limitations
-Materialization isn't available for datasets that use parameters or system functions, although there are a few exceptions to this. For example, the system function Today() does work. These datasets are expected to return different values when their parameters change. Materialized tables, on the other hand, always return the same value - the fixed output of the dataset at the time the materialization was run. As a result, using the materialized versions of datasets that use parameters or system functions would produce unexpected results.
+**Parameters and system functions:**
NOTE:
-IMPORTANT:
+ IMPORTANT:
-Click on the `Orders` (under number 3) to go to the `Materialization` Workbook.
+Click on the `Orders` text to return to the `Materialization QuickStart` workbook.
-Clicking on the `View materialization icon` will show the latest run information and links to `Materialize now` and `View Schedule` pages.
+Click the `View materialization` icon to see:
+- Details about the most recent run
+- A Materialize now button
+- Links to View Schedule pages
-
-IMPORTANT:
+If you need to access this data from other applications, see [Review warehouse view details](https://help.sigmacomputing.com/docs/review-warehouse-view-details)
+ IMPORTANT:
## Workbook Materialization
+Duration: 20
-Workbook materialization is similar to datasets, but have some major advantages, depending on the use case needs.
+Workbook materialization is similar to datasets, but has some major advantages depending on the use case.
-
- It allows you to materialize any workbook element (table, visualization, pivot) that can be used as a data source for another element.
- Workbook materialization offers a smarter refresh when published. Publishing a document only refreshes the materialization if necessary. For example, if the materialized element hasn't changed, materialization doesn't run. Datasets always refresh the materialization on publish. This matters when the dataset is very large, in terms of warehouse cost.
- It offers support for different levels of reference. Grouping levels can be independently materialized. Datasets only materialize at one grouping level.
- Materializations are used when viewing and editing a workbook. Datasets don't use the materialization in edit mode.
- The workbook viewer is notified of Materialization activity in a new "alerts" center (to be integrated with in-app notifications) and toasts.
-
+ It allows you to materialize any workbook element (table, visualization, pivot) that can be used as a data source for another element. Workbook materialization offers a smarter refresh when published. Publishing a document only refreshes the materialization if necessary. For example, if the materialized element hasn’t changed, materialization doesn’t run. Datasets always refresh the materialization on publish. This matters when the dataset is very large, in terms of warehouse cost. It offers support for different levels of reference. Grouping levels can be independently materialized. Datasets only materialize at one grouping level. Materializations are used when viewing and editing a workbook. Datasets don’t use the materialization in edit mode. The workbook viewer is notified of materialization activity in a new “alerts” center (to be integrated with in-app notifications) and toasts.
-The easiest way to demonstrate this is to use a Sigma Template as our "Workbook".
+The easiest way to demonstrate this is to use a `Sigma Template` as our workbook.
-
-NOTE:
+ NOTE:
-Navigate to `Templates` and select the `Plugs Electronics Sales Performance` template.
+Return to the Sigma homepage and click `Templates` > `External,` then search for `Plugs`. Click the `Plugs Electronics Sales Performance` template:
-IMPORTANT:
+ IMPORTANT:
-**Some general rules:**
-
- Any materialization with multiple levels creates tables for the selected level and each additional level above it.
-
- If you have four groupings and a base level, aggregating "all columns" will materialize five new tables total.
- All visualizations have at least one grouping level.
-
- If a user wants to materialize a single aggregated grouping level, then they can create a child table and materialize that.
- Materializing all of the grouping levels means that you can very easily build aggregation tables that we reference appropriately in down stream elements.
-
-
+### Some general rules:
-**For example:** Any materialization with multiple levels creates tables for the selected level and each level above it. If you have four groupings plus a base level, aggregating “all columns” will materialize five tables total. All visualizations have at least one grouping level. If you want to materialize only a single aggregated grouping level, you can create a child table and materialize that. Materializing all grouping levels allows you to build aggregation tables that downstream elements can reference efficiently.
-This is important because there is a trade off here; performance vs. storage cost for grouping levels that are in-frequently used.
+**For example:**
+If we have five grouping levels in a table element and materialize at the third level, Sigma creates three materializations: one for the top level, one for the second level, and one for the third. The fourth, fifth, and base grouping levels will not be materialized.
-If levels four and five have hundreds of millions of records that can't be materialized, Sigma allows materialization of levels one thru three, with great performance and depth of detail when needed, albeit slower.
+This trade-off matters: performance vs. storage cost. If levels four and five contain hundreds of millions of records, Sigma still allows materialization of levels one through three — delivering strong performance with the option to drill deeper when needed (though more slowly).
-
-IMPORTANT:
+ IMPORTANT:
-We will demonstrate by building on the Workbook we already have, but extend the data.
+We’ll build on the existing workbook but extend the data.
### Sample Use Case
-Users who are viewing Sigma content always want to see sales data rolled up from the `Region/Nation/Customer` level with summary sales totals and order counts broken out for each group. They rarely explore the line item detail, but want the option to do so from time to time.
-
-First, let's pause the existing materialization schedule so it no longer will run.
+Users viewing Sigma content often want to see sales data rolled up from the `Region/Nation/Customer` level, with summary sales totals and order counts broken out for each group. They rarely explore line item detail but want the option to do so occasionally.
-Click and open the `View schedule` option:
+First, pause the existing materialization schedule so it no longer runs.
-
-IMPORTANT:
+ IMPORTANT:
- IMPORTANT:
-We now have about 60M rows of order detail by customer.
+Click `Done`.
-To look at the row counts of each table, we created this graphic. It is typical to have the row counts increase in size such that the "detail" or "base" level is the lowest level and not grouped or materialize due to size and frequency of use:
+We now have about `60M rows` of order detail by customer:
-IMPORTANT:
+ IMPORTANT:
-Now that we have the table we want, we can materialize again, but this time we have the additional ability to select a grouping level for the table.
+### Data grouping
+Create the following three groups in Sigma:
-Open the `Schedule materialization` UI again:
+
-IMPORTANT:
-
-Once the materialization is completed, we can see that three new tables have been created in Snowflake. These tables are managed by Sigma and in normal operations, there is no need to look at them, but we want to show the results and make a few important points.
-
-Upon inspection of our `Sigma WriteDB` we see that three tables have been created (notice that the last character in it's name is "_0., _1 and _3". For the last table (our highest group level) we see three columns that correspond to our first group in Sigma.
+
-IMPORTANT:
+Create a schedule (once per day) based on the `Customer` grouping level:
- IMPORTANT:
-Once the materialized tables have been accessed, the exist in the warehouse cache (based on Snowflake caching rules) which also helps performance. We can see that Sigma has automatically read from warehouse cache (as shown below), by looking at the "behind the scenes" Sigma Query History.
+Once complete, three new tables will appear in Snowflake.
-Note how we are reading from the Sigma_WriteDB.QuickStart materialized tables, as the query builds each level of the query:
+Looking at the Sigma WriteDB, the three tables are organized with the lowest level first, followed by the next two grouping levels. Column names are not “friendly” — this is intentional:
- IMPORTANT:
-
-IMPORTANT:
+It is possible to access this data from other applications using Sigma’s Dataset Warehouse Views
+.
-Now our most commonly used order data is performant:
+Once materialized tables are accessed, they also exist in the warehouse cache (subject to Snowflake caching rules), further improving performance.
- IMPORTANT:
-For those interested, please refer to the [Sigma on Snowflake Best Practices guide, located here:](https://www.sigmacomputing.com/sigma-on-snowflake-best-practices)
+For those interested, please refer to the [Sigma on Snowflake Best Practices guide](https://www.sigmacomputing.com/sigma-on-snowflake-best-practices)
@@ -595,11 +570,11 @@ For those interested, please refer to the [Sigma on Snowflake Best Practices gui
## What we've covered
Duration: 5
-This QuickStart was designed as primer to materialization and the questions and issues that surround it.
+This QuickStart was designed as a primer on materialization and the questions and considerations that surround it.
-We defined it, provided guidance on the why/when to use it and who might set it up.
+We defined what materialization is, provided guidance on when and why to use it, and discussed who typically sets it up.
-Lastly, we stepped through using Sigma and Snowflake to materialize data using a Sigma, including grouping data.
+Finally, we stepped through using Sigma and Snowflake to materialize data — including examples with grouped data.
**Additional Resource Links**
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