Engineering & data · About 2 hours
AI for Data Engineering
Build a small ingestion and transformation pipeline with checks that make bad data visible.
Your practical result
A small working pipeline, a rejected-record sample and a data-quality validation report.
The outline below is built around this result. Final tools and setup instructions will be confirmed with the session details before registration opens.
Who this is for
Data engineers and technical analysts comfortable with basic scripting and tabular data.
What you’ll practice
- Describe source fields, transformations and expected output before generating code.
- Use AI assistance to build a pipeline with explicit data-quality checks.
- Verify valid rows, rejected rows and repeat-run behavior.
Your two-hour workshop outline
A guided demonstration, a substantial build exercise, and time to check the result. Times are approximate and include discussion.
- 0–10 min
Define the data contract
Read a small synthetic source and specify a clean output schema.
- 10–30 min
Demonstrate the pipeline
Trace ingestion, transformation, validation and rejected-record handling.
- 30–45 min
Choose quality rules
Define required fields, allowed values and duplicate behavior.
- 45–85 min
Build the pipeline
Use AI assistance to implement transformations and visible failure handling.
- 85–105 min
Test dirty data
Run valid, malformed and duplicate records; compare counts and inspect rejected rows.
- 105–120 min
Save the evidence
Package the pipeline, sample output and a short validation report.
Before you attend
- Basic scripting, CSV or JSON familiarity and an understanding of tabular data.
- Use the supplied synthetic dataset and complete the starter-project setup check.
Tools and access
The language runtime and local data tools will be specified before registration. Cloud subscriptions or production-system access are not assumed.
Delivery platform, location or joining instructions will be published with each confirmed session. Use practice material you have permission to share.
Your instructor
Elliott Lamboy
Elliott teaches these Texas AI Consulting workshops. The session is designed around a guided example, your own practice, and a review of the work produced.
Questions before you join
Do I need a cloud data platform?
The outline is for a small prepared lab. The exact tools and setup will be disclosed before registration; no particular cloud account is currently promised.
When is the next session, and what does it cost?
Dates and fees are coming soon. The planned window is Friday, 11 a.m.–1 p.m. America/Chicago. Ask about availability below; an inquiry does not reserve a seat.
What will be confirmed before I register?
The session date, fee, capacity, delivery platform, exact software requirements, and cancellation/refund terms will be available before registration opens. Recording or replay access is not currently promised.
Can my team request a private workshop?
Yes. Choose “A private group workshop” in the inquiry form and describe your goal or group size. Elliott can discuss availability and fit.
Ask about this workshop
Interested in the next date or a session for your team? Tell Elliott what you’re looking for.