Data Engineering: Pipelines, Orchestration & Reliability
The data engineering layer beneath dbt. How data actually gets into the warehouse, how pipelines are orchestrated, and how to keep them reliable. Ingestion patterns (batch, CDC, streaming), tools (Fivetran, Airbyte, Meltano), orchestration (Airflow, Dagster, Prefect), data quality and observability, streaming basics (Kafka, Pub/Sub), Spark for DE, and lakehouse file formats (Parquet, Iceberg). Six end-to-end pipeline cases. Builds on the SQL, dbt, and Data Modeling courses; completes the DE track.
3 weeks
4 modules, 32 lessons
4
~12 hours
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Course Curriculum
The Modern Data Engineering LandscapeFREE PREVIEW
What data engineering actually means in 2026; the modern data stack from source to serving; batch vs streaming vs hybrid; ETL vs ELT and why ELT won; lakehouse file formats (Parquet, Iceberg, Delta).
Ingestion and OrchestrationFREE PREVIEW
Ingestion patterns (batch, CDC, streaming); managed tools (Fivetran, Airbyte, Meltano) and when to build vs buy; Apache Airflow deep dive; modern orchestrators (Dagster, Prefect); scheduling, backfills, and idempotency.
Reliability and Production PatternsFREE PREVIEW
Data quality testing (Great Expectations, dbt tests); pipeline observability (Monte Carlo, OpenLineage, Datadog); streaming basics (Kafka, Pub/Sub, Kinesis); Spark for batch and structured streaming.
Production Capstone: End-to-End Pipeline PlatformFREE PREVIEW
Design and implement an end-to-end production data platform integrating ingestion, dbt modeling, orchestration, and data quality testing.
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