Data Engineering Professional — Study Guide

A visual, exam-aligned study guide for the Data Engineering Professional certification. Six modules cover pipeline foundations and orchestration, ingestion and CDC, distributed processing with Spark, dimensional warehousing and SCDs, the data lake and lakehouse, and data quality, governance and DataOps — with Mermaid diagrams, comparison tables, worked examples, code blocks, key takeaways and self-checks.

Key facts

• Course price: $195 (one-time payment)
• Field: Data Engineering & Analytics
• Prepares for: Data Engineering Professional
• 6 modules · 18 lessons · about 4 hours of lessons
• Self-paced, online, illustrated lessons

Course outline

1. Pipelines & Orchestration — ETL vs ELT, batch vs streaming, and orchestrating idempotent, backfillable DAGs.
   • ETL vs ELT
   • Batch vs Streaming
   • Orchestration with DAGs
2. Data Ingestion — Files, APIs, connectors and Change Data Capture — getting data in reliably.
   • Ingestion Patterns: Files, APIs & Connectors
   • Change Data Capture (CDC)
   • Incremental & Idempotent Loads
3. Distributed Processing & Spark — MapReduce, RDDs vs DataFrames, lazy evaluation, shuffles and partitioning.
   • From MapReduce to Spark
   • RDDs, DataFrames, Lazy Evaluation
   • Shuffles, Partitioning & Joins
4. Dimensional Data Warehousing — Kimball facts and dimensions, star schemas, SCD types and OLTP vs OLAP.
   • Facts, Dimensions & the Star Schema
   • Slowly Changing Dimensions (SCD)
   • OLTP vs OLAP
5. Data Lakes & the Lakehouse — Object storage, Parquet/ORC, Delta/Iceberg, schema evolution and the medallion architecture.
   • Data Lakes & File Formats
   • The Lakehouse: Delta & Iceberg
   • The Medallion Architecture
6. Quality, Governance & DataOps — Data quality testing, observability, lineage, governance and cost/performance tuning.
   • Data Quality, Testing & Observability
   • Governance, Lineage & Data Contracts
   • DataOps, Cost & Performance