1-Day Instructor-Led AWS & Data Engineering Workshops for Your Team

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.

Your team will be able to: Build a governed Bronze-Silver-Gold pipeline in Databricks — ready for BI and reporting the same day.

Build a Bronze, Silver, and Gold Medallion Architecture pipeline in a real Databricks workspace. Covers Delta Lake, Unity Catalog governance, and Lakeflow orchestration.

Your team will be able to: Let non-technical stakeholders query your data warehouse in plain English — no SQL, no waiting on the data team.

Deploy a production text-to-SQL agent against your data warehouse. Covers schema-aware query generation, SELECT-only safety guardrails, and LLM endpoint configuration.

Your team will be able to: Equip your finance team to use Claude for real analyst work — models, memos, and reporting — with the guardrails a finance workflow actually needs.

Hands-on workshop for finance professionals: use Claude for financial modeling, variance analysis, and memo drafting, wire it into spreadsheets and internal data via MCP, and apply guardrails for accuracy, auditability, and confidential data handling.

Your team will be able to: Run LLM inference in-house with full cost controls and observability dashboards — without depending on managed APIs.

Deploy Qwen3 with vLLM on a GPU instance. Wire in LiteLLM as an API gateway with virtual keys, per-team budget caps, and model routing. Instrument with Prometheus and Grafana dashboards for TTFT and throughput.

Your team will be able to: Write least-privilege IAM policies from scratch and audit existing ones without guessing.

Read, predict, and write IAM policies using a real sandbox policy as the textbook. Six exercises from basic allow/deny logic to least-privilege policy design from scratch.

Your team will be able to: Ship a production-ready full-stack app on AWS — backend, frontend, infrastructure, and CI/CD pipeline.

Build and ship a full-stack application on AWS: React frontend, FastAPI backend, Terraform infrastructure, and a GitHub Actions CI/CD pipeline. Seven chapters covering the complete engineering lifecycle.