updated based on feedback

Author: teetangh

test/test-markdown-frontmatter.js

@@ -6,7 +6,7 @@ const chalk = require('chalk')
 // accepted data field values
 const sdk_languages = ['nodejs', 'scala', 'python', 'swift', 'csharp', 'objective-c', 'android-java', 'any', 'java', 'kotlin', 'dart', 'golang', 'c++']
 
-const tags = ['Ottoman', 'Ktor', 'REST API', 'Express', 'Flask', 'TLS', 'Configuration', 'Next.js', 'iOS', 'Xcode', '.NET', 'Xamarin', 'Authentication', 'OpenID', 'Keycloak', 'Android', 'P2P', 'UIKit', 'Installation', 'Spring Boot', 'Spring Data', 'Transactions', 'SQL++ (N1QL)', 'Optimization', 'Community Edition', 'Docker', 'Data Modeling', 'Metadata', 'Best Practices', 'Data Ingestion', 'Kafka', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', 'LangChain', "OpenAI", "Streamlit", 'Google Gemini', 'Nvidia NIM', 'LLama3', 'AWS', 'Artificial Intelligence', 'Cohere', 'Jina AI', 'Mistral AI', 'Ragas', 'Haystack', 'LangGraph', 'Amazon Bedrock', 'CrewAI', 'PydanticAI', 'C++', 'C++ SDK', 'smolagents', 'Ag2', 'Autogen', 'Couchbase Edge Server', 'Deepseek', 'OpenRouter', 'mastra', 'Looker Studio', 'Google Data Studio', 'Connector', 'Couchbase Columnar', 'Views-only', 'Data API']
+const tags = ['Ottoman', 'Ktor', 'REST API', 'Express', 'Flask', 'TLS', 'Configuration', 'Next.js', 'iOS', 'Xcode', '.NET', 'Xamarin', 'Authentication', 'OpenID', 'Keycloak', 'Android', 'P2P', 'UIKit', 'Installation', 'Spring Boot', 'Spring Data', 'Transactions', 'SQL++ (N1QL)', 'Optimization', 'Community Edition', 'Docker', 'Data Modeling', 'Metadata', 'Best Practices', 'Data Ingestion', 'Kafka', 'Support', 'Customer', 'Prometheus', 'Monitoring', 'Observability', 'Metrics', 'Query Workbench', 'ASP.NET', 'linq', 'DBaaS', 'App Services', 'Flutter', 'Gin Gonic', 'FastAPI', 'LangChain', "OpenAI", "Streamlit", 'Google Gemini', 'Nvidia NIM', 'LLama3', 'AWS', 'Artificial Intelligence', 'Cohere', 'Jina AI', 'Mistral AI', 'Ragas', 'Haystack', 'LangGraph', 'Amazon Bedrock', 'CrewAI', 'PydanticAI', 'C++', 'C++ SDK', 'smolagents', 'Ag2', 'Autogen', 'Couchbase Edge Server', 'Deepseek', 'OpenRouter', 'mastra', 'Looker Studio', 'Google Data Studio', 'Connector', 'Couchbase Columnar', 'TAVs', 'Custom Queries', 'Data API']
 
 const technologies = ['connectors', 'kv', 'query', 'capella', 'server', 'index', 'mobile', 'fts', 'sync gateway', 'eventing', 'analytics', 'udf', 'vector search', 'react', 'edge-server', 'app-services']
 

tutorial/markdown/connectors/looker-studio/columnar/readme.md

@@ -17,7 +17,8 @@ tags:
   - Looker Studio
   - Couchbase Columnar
   - Connector
-  - Views-only
+  - TAVs
+  - Custom Queries
 sdk_language:
   - nodejs
 length: 20 Mins
@@ -57,11 +58,12 @@ When you add the Couchbase Columnar connector in Looker Studio, you'll see the a
 
 For the "By View" mode, create Tabular Analytics Views in Capella:
 
-1. Open your Capella cluster, go to the Analytics tab, and launch the Analytics Workbench.
-2. Prepare a SQL++ query that returns a tabular result. For simple, flat data structures, basic SELECT statements work well. For nested objects, consider flattening them for better BI tool compatibility. For example:
+1. **Open Analytics Workbench**: In your Capella cluster, go to the Analytics tab and launch the Analytics Workbench.
+
+2. **Create your SQL++ query**: Write a query that returns the data structure you want for reporting. For nested objects, consider flattening them for better BI tool compatibility:
 
 ```sql
--- Example with flattening for nested data structures
+-- Example: Flattened airport data for reporting
 SELECT a.airportname AS airport_name,
        a.city AS city,
        a.country AS country,
@@ -73,7 +75,16 @@ WHERE a.country = 'United States'
 LIMIT 100;
 ```
 
-3. Run the query, then click Save as View → Annotate for Tabular View. Define the schema (column names, data types, and primary keys) and save with a descriptive name.
+3. **Test the query**: Run your query to verify it returns the expected results and data types.
+
+4. **Create the TAV**:
+   - Click **Save as View** in the Analytics Workbench
+   - Select **Annotate for Tabular View**
+   - **Define the schema**: Specify column names, data types (STRING, NUMBER, BOOLEAN), and mark primary key fields
+   - **Name your view**: Use a descriptive name like `airports_us_flattened`
+   - **Save**: The TAV will be created with enforced schema
+
+5. **Verify creation**: Your TAV will appear in the database browser under the specified scope and can be queried like a table.
 
 - For details, see [Tabular Analytics Views](https://docs.couchbase.com/columnar/query/views-tavs.html) and [Buckets, Scopes, and Collections](https://docs.couchbase.com/cloud/clusters/data-service/about-buckets-scopes-collections.html).
 
@@ -151,7 +162,7 @@ Once your schema is configured, you can customize the fields in your Looker Stud
 - **Schema inference errors**: Ensure your TAV exists and contains data. Try adding a `LIMIT` clause for faster sampling (e.g., `LIMIT 100`).
 - **API error from Columnar**: Review the response message in Looker Studio and verify TAV names, permissions, and that the view is properly created in Capella.
 - **Empty or missing TAV**: Verify that your Tabular Analytics View was saved correctly in the Analytics Workbench and contains data.
-- **Mixed data types**: If fields appear as STRING when they should be NUMBER, your data may have mixed types. Consider modifying your TAV creation query to cast fields to consistent types (e.g., `CAST(price AS NUMBER)`) or filter out inconsistent records.
+- **Schema mismatch**: If fields appear with unexpected types, verify your TAV schema definition matches your query results. TAVs enforce schema consistency, so ensure your query produces data that matches the defined column types.
 
 ## Next Steps