connecting-to-data-source — Create and troubleshoot AWS Glue connections to JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Redshift, Snowflake, and BigQuery.
creating-data-lake-table — Create managed Iceberg tables using Amazon S3 Tables (s3tables API namespace) with automatic compaction and snapshot management.
exploring-data-catalog — Full inventory and audit of AWS Glue Data Catalog assets across S3 Tables, Redshift-federated, and remote Iceberg catalogs.
finding-data-lake-assets — Resolve data lake and lakehouse asset references across Glue Data Catalog, S3, S3 Tables, and Redshift.
ingesting-into-data-lake — Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift.
querying-data-lake — Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift).
redshift-guide — Amazon Redshift is NOT PostgreSQL — corrects PostgreSQL-derived LLM mistakes; covers Redshift-specific SQL, DDL, COPY/UNLOAD, system views, metadata discovery.
storing-and-querying-vectors — Store and query vector embeddings using Amazon S3 Vectors, a cost-effective long-term vector storage service with its own API namespace (s3vectors).