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How it works
Data lake, analytics, and ETL workflows with S3 Tables, AWS Glue, and Athena. Covers managed Iceberg tables on S3 Tables, ingestion from JDBC databases, Amazon Redshift, Snowflake, BigQuery, and DynamoDB, AWS Glue Data Catalog inventory and asset discovery, federated Athena queries, and vector storage and semantic search on Amazon S3 Vectors.
What else can you do?
Find analytics data
AWS Data Analytics Find tables in the Glue Data Catalog in [region] that could answer [business question]. Summarize their schemas and locations, recommend the best sources, and flag missing data.
Analyze sales trends
AWS Data Analytics Use Athena to analyze monthly sales in [table] for the last quarter. Compare regions, identify the largest changes, and return the SQL with a concise findings summary.
Plan data lake ingestion
AWS Data Analytics Plan ingestion of [source table] into S3 Tables in [region]. Inspect the source schema, recommend an ingestion approach, and list prerequisites and validation checks before any data movement.
What’s included
App
AWS Data Analytics
Skills
- amazon-opensearch-service
- connecting-to-data-source
- creating-data-lake-table
- exploring-data-catalog
- finding-data-lake-assets
- ingesting-into-data-lake
- querying-data-lake
- redshift-guide
- 1 more
Resources
Add plugins in a few clicks
Availability depends on the plugin, your plan, and workspace settings. Some connections require admin setup or approval. Contact your workspace admin if access is blocked.
