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Data Engineering: Pipelines, Orchestration & Reliability

Intermediate

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.

Data Engineering: Pipelines, Orchestration & Reliability

Duration

4 weeks

Content

4 modules, 32 lessons

Students

2

Total Time

~7 hours

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Course Curriculum

1

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).

80 min8 lessons

What Data Engineering Actually Is (2026 view)

Recap — The Modern Data Stack From Source to Serving

Batch vs Streaming vs Hybrid

Recap — Choosing the Right Processing Model

ETL vs ELT — Why ELT Won

Recap — Designing ELT for the Modern Warehouse

File Formats & the Lakehouse (Parquet, Iceberg, Delta)

Recap — Why Open Table Formats Matter

2

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.

110 min10 lessons

Data Ingestion Patterns (Batch, CDC, Streaming)

Recap — Picking the Right Ingestion Pattern

Managed Ingestion Tools (Fivetran, Airbyte, Meltano)

Recap — When to Build vs Buy Ingestion

Apache Airflow — The Workhorse Orchestrator

Recap — Writing Maintainable Airflow DAGs

Modern Orchestrators (Dagster, Prefect)

Recap — When to Use Which Orchestrator

Scheduling, Backfills, and Idempotency

Recap — Building Pipelines That Survive Re-runs

3

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.

80 min8 lessons

Data Quality Testing (Great Expectations, dbt Tests)

Recap — Building a Data Quality Discipline

Pipeline Observability (OpenLineage, Monte Carlo, Datafold)

Recap — Observability for Data Pipelines

Streaming Basics (Kafka, Pub/Sub, Kinesis)

Recap — Streaming Without Drowning

Spark for Data Engineers

Recap — When Spark Belongs in Your Stack

4

Live DE CasesFREE PREVIEW

Six end-to-end pipeline walkthroughs: build an ingest→dbt→dashboard pipeline; design CDC from OLTP to warehouse; add data quality + alerting; convert batch to near-real-time; refactor a flaky pipeline to idempotent; migrate to an open lakehouse on Iceberg.

145 min6 lessons

Case 1 — Build an End-to-End Pipeline (Ingest → dbt → Dashboard)

Case 2 — Postgres CDC to Warehouse (Real-Time Replication)

Case 3 — Data Quality Failure Investigation

Case 4 — Batch to Streaming Migration

Case 5 — Idempotency Refactor

Case 6 — Lakehouse Migration (Move Data to Iceberg)

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