Hands-on workshop labs built by BeCloudReady for engineering and analytics teams. Each lab is self-contained, with pre-scoped IAM permissions, step-by-step walkthroughs, and sample data included. Drop into a workshop or run independently.
| Workshop | What you build | What you will be able to do | Stack |
|---|---|---|---|
workshops/databricks-genie-ai-agents/ |
A governed conversational AI agent on Databricks AI/BI Genie: LLM fundamentals in SQL, Genie space, Knowledge Store curation, benchmarks | Stand up a Genie agent your stakeholders can trust — curate it, benchmark it, and govern it with Unity Catalog. No Python required for the core track | Databricks, AI/BI Genie, Unity Catalog, SQL |
workshops/aws-data-lake/ |
End-to-end data lake: raw ingestion, ETL, governance, CDC, and analytics | Design and operate the full AWS data engineering stack, from raw S3 files to a governed query layer in Athena and Redshift | S3, Glue, Athena, Lake Formation, Redshift, DMS, OpenSearch |
workshops/fullstack-aws/ |
Full-stack app on AWS: React, FastAPI, MongoDB, Terraform, and CI/CD across 7 chapters and 4 deployable projects | Ship a production-ready app on AWS end-to-end, including infrastructure and automated deployment | React, FastAPI, Lambda, S3, DynamoDB, API Gateway, Terraform, GitHub Actions |
workshops/llmops/ |
Deploy, observe, and route production LLM workloads on a GPU instance: vLLM serving, Prometheus/Grafana dashboards, and LiteLLM gateway with virtual keys and spend tracking | Run LLM inference in-house with full observability and cost controls, without depending on managed APIs | vLLM, LiteLLM, Prometheus, Grafana, DCGM, Docker, Ansible |
| Track | Content |
|---|---|
| Analyst track (SQL + UI) | LLM fundamentals via ai_query(), build a Genie space, Knowledge Store curation (descriptions, synonyms, example SQL, instructions), benchmarks, Unity Catalog governance |
| Engineer track (notebooks) | LLM API basics → structured output → tool use → agentic loop → MCP server — a from-scratch text-to-SQL agent showing what Genie abstracts away |
Related open source: db-agent — text-to-SQL AI agent with cross-platform memory, S3 Vectors, and knowledge files (AAAI-25 workshop project).
| Chapter | Topic |
|---|---|
| 01 | Vibe Coding: FastAPI app with AI prompting |
| 02 | Backend & Databases: CRUD API + MongoDB |
| 03 | Testing: TDD cycle with AI assistance |
| 04 | Security: API key auth + JWT |
| 05 | Infrastructure: Terraform on AWS |
| 06 | Cloud & CI/CD: Lambda, API Gateway, S3, GitHub Actions |
| 07 | React: frontend, components, full-stack integration |
4 deployable projects: Task Tracker · Notice Board · URL Bookmark Saver · Architecture Diagram
Labs 1-3 build on each other. Labs 4-6 are standalone.
| Lab | Topic |
|---|---|
| Lab 1 | S3, Glue Crawler, Glue ETL (PySpark), Athena |
| Lab 2 | Event-driven ingestion: S3 to SQS to Lambda |
| Lab 3 | Data governance: Lake Formation row/column/tag-based access control |
| Lab 4 | Redshift Serverless, federated query from Aurora RDS |
| Lab 5 | Change Data Capture: Oracle/Postgres to DMS to S3 or Oracle/Postgres target |
| Lab 6 | OpenSearch: ingestion, search, and dashboards |
BeCloudReady is a Databricks Registered Partner that builds and delivers cloud workshops for engineering teams. We run community workshops at TorontoAI (10K+ members).
| Cloud Workshops | Per-student AWS / Azure / GCP / Databricks sandboxes, region-locked, namespace-scoped, teardown-clean |
| AI & GPU Labs | H100 / A100 cohorts on neo-cloud (Lambda Labs, Shadeform, RunPod): 30-70% cheaper than hyperscaler on-demand |
| Sales Demo Environments | Reproducible demo stacks for SE teams and partner programs |
Need a workshop for your team? → becloudready.com/workshops · Book a call
Every resource created in a workshop must carry these tags. The nightly cleanup workflow reads them to decide what to delete.
| Tag | Example | Purpose |
|---|---|---|
workshop |
aws-data-lake |
Which lab |
date |
dd-mmm-yyy |
When the cohort ran 26-Jul-2026 |
autodelete |
true (default) |
Set to false to protect a resource |
All Terraform modules in this repo apply these tags via local.common_tags. Resources created manually (via console or CLI) must be tagged manually.
To protect a resource from nightly deletion, add AutoDelete = false. Everything else tagged Environment = workshop is deleted at 3 AM EST.
See terraform/tags.tf for the shared locals block and tools/nightly-cleanup.py for the cleanup logic.
Found a bug or a gap in a published lab? Issues and PRs are welcome. Have a lab that fits one of the tracks above? Reach out before opening a PR.
Apache License 2.0. See LICENSE.