DB
Databricks Masterclass
Most popularEight structured weeks from Spark/PySpark foundations to a production Lakehouse you built yourself — Delta Lake, Lakeflow, Unity Catalog, streaming and CI/CD.
Course catalog
Online training courses in data engineering, cloud and AI
Nine live, instructor-led tracks of 90–120 hours each, taught in small batches, recorded for life, and built around three portfolio projects. Filter below or compare them side by side.
All masterclasses
9 courses
PS
PySpark Masterclass
Core skillThe deepest PySpark course we teach — internals, tuning, testing and streaming, not just the DataFrame API.
AWS
AWS Data Engineering Masterclass
Certification alignedNine structured weeks from PySpark foundations to production AWS pipelines — Glue, Lambda, Step Functions, EMR, Redshift, MSK/Kinesis, Airflow and Databricks.
AZ
Azure Data Engineering Masterclass
Fabric updatedTen structured weeks from PySpark foundations to production Azure pipelines — ADF, ADLS Gen2, Databricks, Delta Lake, Event Hubs, Airflow and Microsoft Fabric.
GCP
GCP Data Engineering Masterclass
Certification alignedBigQuery-first data engineering on Google Cloud, with real Beam pipelines and a Composer-orchestrated platform you build yourself.
AI
Generative AI Masterclass
Highest demandEngineering-grade GenAI: you will ship a RAG system and an agent that survive evaluation, cost review and a security audit.
A full engineering course built around one model family — the API, the protocol, the agent patterns and the production economics.
DO
Multi-Cloud DevOps Masterclass
Career switcher friendlyOne toolchain, three clouds. Build a real platform: IaC, GitOps, Kubernetes, observability and security — not a tutorial pipeline.
SF
Snowflake & dbt Masterclass
Analytics trackLearn the warehouse and the transformation layer together, the way modern data teams actually build.
No course matches that search. Try “Spark”, “Kafka”, “Airflow”, “RAG” or ask us directly.
Compare the tracks
| Course | Duration | Level | Certification alignment | Fee |
|---|---|---|---|---|
| Databricks | 120 hrs | Beginner to Advanced | Databricks Certified Data Engineer Associate + Professional | |
| PySpark | 100 hrs | Beginner to Advanced | Databricks Certified Associate Developer for Apache Spark | |
| AWS Data Engineering | 90 hrs | Beginner to Advanced | AWS Certified Data Engineer – Associate (DEA-C01) | |
| Azure Data Engineering | 100 hrs | Beginner to Advanced | DP-700 Fabric Data Engineer (primary) · DP-203 concepts covered | |
| GCP Data Engineering | 100 hrs | Beginner to Advanced | Google Cloud Professional Data Engineer | |
| Generative AI | 100 hrs | Intermediate to Advanced | Databricks Generative AI Engineer Associate · Azure AI Engineer (AI-102) concepts | |
| Claude & Agentic AI | 100 hrs | Intermediate to Advanced | Course completion certificate · portfolio of 3 shipped agents | |
| Multi-Cloud DevOps | 100 hrs | Beginner to Advanced | HashiCorp Terraform Associate · CKA concepts · AZ-400 concepts | |
| Snowflake & dbt | 100 hrs | Beginner to Advanced | SnowPro Core · dbt Analytics Engineering concepts |
Not sure which track to start with?
- New to data engineering? Start with PySpark, then Databricks.
- Your company is on AWS / Azure / GCP? Take that cloud's track first — each one includes Airflow and Kafka.
- Already a data engineer? Generative AI is the fastest-appreciating skill on this list right now.
- Coming from BI or SQL? Snowflake & dbt is the shortest route to an analytics engineering role.
- Want to own your infrastructure? Add Multi-Cloud DevOps to any track.