AWS Data Engineering Workshops

Your team will be able to: Design and operate the full AWS data stack — raw S3 files to a governed query layer in Athena and Redshift.

Ingest raw data into S3, crawl schema with Glue, transform CSV to Parquet with PySpark ETL, and query with Athena. The foundation of every AWS data platform.

Your team will be able to: Build pipelines that react to data as it lands — no polling, no cron jobs.

Wire S3 events to SQS and Lambda to build an event-driven ingestion pipeline. Three patterns: S3 to SQS to Lambda, direct S3 to Lambda, and Lambda-as-ETL.

Your team will be able to: Enforce row, column, and tag-based access on your data lake without writing custom auth code.

Apply row-level, column-level, and tag-based access control to a live data lake using AWS Lake Formation. Real multi-persona access scenarios.

Your team will be able to: Query S3 data lake files directly from Redshift and run federated queries against live databases.

Stand up Redshift Serverless, query S3 directly with Spectrum, and run federated queries against Aurora PostgreSQL. Covers the analytics tier end-to-end.

Your team will be able to: Replicate row-level database changes to the cloud in real time — full load plus ongoing CDC.

Capture row-level changes from a PostgreSQL source using AWS DMS and land them in S3 or a target database. Covers full load plus ongoing replication.

Your team will be able to: Add full-text search and operational dashboards to your data platform without a third-party SaaS.

Ingest processed data from your data lake into OpenSearch, build search queries, and create operational dashboards. Adds a real-time search and observability layer to the data platform.