Spark, Analytics Engineering & Data Contracts
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10 lessons3 levels1 capstone project
Scale analytical data work with distributed processing and analytics engineering: Spark/PySpark, transformations, partitioning, dbt-style modeling, semantic layers, tests, documentation, contracts and reliable producer-consumer interfaces.
BASIC
- Data Engineer Roadmap 2026 — Codebasics (25 min selected)
Maps the modern data-engineering stack and where Spark/analytics engineering fit. - Star Schema / Kimball — Data Modeling Tutorial (Medium)
Build the dimensional-modeling foundation for analytics engineering. - Data Workflow Orchestration — Mastering Apache Airflow (Long)
Connect transformations and contracts to repeatable production pipelines.
INTERMEDIATE
- Data Fabric Explained — IBM Technology
Apply Spark, Analytics Engineering & Data Contracts through the adjacent capability: Intelligent Data Platform. - Intro to the Lakehouse Platform — Databricks
Apply Spark, Analytics Engineering & Data Contracts through the adjacent capability: Intelligent Data Platform. - What Is MLOps? — IBM Technology
Apply Spark, Analytics Engineering & Data Contracts through the adjacent capability: MLOps & Model Serving.
ADVANCED
- Quality, Lineage, Privacy and Trust — Data Governance
Extend Spark, Analytics Engineering & Data Contracts into senior/production practice through Data Quality, Governance & Lineage. - Microsoft Fabric Overview — John Savill
Extend Spark, Analytics Engineering & Data Contracts into senior/production practice through Intelligent Data Platform. - End-to-End Databricks Data Engineering Project
Extend Spark, Analytics Engineering & Data Contracts into senior/production practice through Data Engineering.