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Data Engineering Fundamentals

ETL, warehouses, lakehouses, and the modern data stack from scratch.

Beginner0.9 hours10 lessons
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What You'll Learn

  • ✓Define data engineering and contrast it with software engineering and analytics
  • ✓Choose between batch and streaming for a given workload
  • ✓Apply ETL and ELT correctly, and know when each fits
  • ✓Use Airflow, dbt, and modern orchestrators to build reliable pipelines
  • ✓Compare data warehouses (Snowflake, BigQuery, Redshift, Synapse) for analytics workloads
  • ✓Understand data lakes and the lakehouse architecture (Delta, Iceberg, Hudi)
  • ✓Apply Apache Spark and distributed processing fundamentals
  • ✓Stream events with Kafka, Kinesis, and Pub/Sub
  • ✓Operate data quality, governance, and lineage at scale
  • ✓Pick the right cloud data platform for your workload

Prerequisites

  • •Basic SQL
  • •Basic Python recommended

Course Curriculum

Module 1: Foundations

  1. 01What Is Data Engineering?5 min
  2. 02Batch vs Streaming5 min

Module 2: Pipelines

  1. 01ETL vs ELT5 min
  2. 02Orchestration: Airflow, dbt, and Friends5 min

Module 3: Storage

  1. 01Data Warehouses5 min
  2. 02Data Lakes and Lakehouses5 min

Module 4: Processing

  1. 01Spark and Distributed Compute5 min
  2. 02Streaming: Kafka, Kinesis, Pub/Sub5 min

Module 5: Production

  1. 01Data Quality and Governance5 min
  2. 02Cloud Data Platforms Compared6 min

Practice for the Real Exam

After completing this course, test yourself with exam-style practice questions.

Practice DP 203 QuestionsPractice AWS DEA C01 QuestionsPractice GCP PDE Questions