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If this never completes: start PostgreSQL, check DATABASE_URL and DIRECT_URL in .env, run npx prisma db push. Optional: set SKIP_RUNTIME_CATALOG_SEED=1 to skip heavy catalog seed while debugging.

Data Engineering

Learn data engineering from SQL and ETL to data warehousing, streaming, and enterprise data platforms.

12 modules • 320 lessons

1SQL Fundamentals

BEGINNER

Queries, joins, aggregations, and basic data manipulation.

2Data Modeling Basics

BEGINNER

ER diagrams, normalization, and schema design.

31Warehouse Lab for customer 360 view #4
32Data Engineering Playbook #5

3ETL Introduction

BEGINNER

Extract, transform, load concepts and first pipelines.

55Data Pipeline for customer 360 view #1
56Sprint: improve lineage tracking #2
57ETL Checkpoint #3
58Warehouse Lab for user behavior tracking #4
59Data Engineering Playbook #5
60Data Pipeline for recommendation engine #6
61Sprint: increase data discoverability #7
62ETL Checkpoint #8

4Data Warehousing

INTERMEDIATE

Star schemas, dimensional modeling, and warehouse design.

81Data Pipeline for e-commerce analytics #1
82Sprint: reduce pipeline latency #2
83ETL Checkpoint #3
84Warehouse Lab for recommendation engine #4
85Data Engineering Playbook #5
86Data Pipeline for inventory management #6
87Sprint: reduce storage costs #7
88ETL Checkpoint #8

5Batch Processing

INTERMEDIATE

Spark, Hadoop, and large-scale batch jobs.

108Data Pipeline for recommendation engine #1
109Sprint: optimize query performance #2
110ETL Checkpoint #3
111Warehouse Lab for customer 360 view #4
112Data Engineering Playbook #5
113Data Pipeline for e-commerce analytics #6
114Sprint: improve data quality #7
115ETL Checkpoint #8

6Streaming Fundamentals

INTERMEDIATE

Kafka, event streams, and real-time ingestion.

135Data Pipeline for customer 360 view #1
136Sprint: improve lineage tracking #2
137ETL Checkpoint #3
138Warehouse Lab for user behavior tracking #4
139Data Engineering Playbook #5
140Data Pipeline for recommendation engine #6
141Sprint: increase data discoverability #7
ETL Checkpoint #8

7Data Quality and Governance

ADVANCED

Validation, lineage, and data catalog practices.

161Data Pipeline for e-commerce analytics #1
162Sprint: reduce pipeline latency #2
163ETL Checkpoint #3
164Warehouse Lab for recommendation engine #4
165Data Engineering Playbook #5
166Data Pipeline for inventory management #6
167Sprint: reduce storage costs #7
ETL Checkpoint #8

8Orchestration and Scheduling

ADVANCED

Airflow, Dagster, and pipeline automation.

188Data Pipeline for recommendation engine #1
189Sprint: optimize query performance #2
190ETL Checkpoint #3
191Warehouse Lab for customer 360 view #4
192Data Engineering Playbook #5
193Data Pipeline for e-commerce analytics #6
194Sprint: improve data quality #7
ETL Checkpoint #8

9Cloud Data Platforms

ADVANCED

Snowflake, BigQuery, Redshift, and Databricks.

215Data Pipeline for customer 360 view #1
216Sprint: improve lineage tracking #2
217ETL Checkpoint #3
218Warehouse Lab for user behavior tracking #4
219Data Engineering Playbook #5
220Data Pipeline for recommendation engine #6
221Sprint: increase data discoverability #7
222ETL Checkpoint #8

10Data Mesh and Fabric

PROFESSIONAL

Domain ownership, self-serve analytics, and federated governance.

241Data Pipeline for e-commerce analytics #1
242Sprint: reduce pipeline latency #2
243ETL Checkpoint #3
244Warehouse Lab for recommendation engine #4
245Data Engineering Playbook #5
246Data Pipeline for inventory management #6
247Sprint: reduce storage costs #7
248ETL Checkpoint #8

11ML Ops and Data

PROFESSIONAL

Feature stores, model pipelines, and production ML data.

268Data Pipeline for recommendation engine #1
269Sprint: optimize query performance #2
270ETL Checkpoint #3
271Warehouse Lab for customer 360 view #4
272Data Engineering Playbook #5
273Data Pipeline for e-commerce analytics #6
274Sprint: improve data quality #7
275ETL Checkpoint #8

12Enterprise Data Strategy

PROFESSIONAL

Data strategy, platform scaling, and organizational design.

295Data Pipeline for customer 360 view #1
296Sprint: improve lineage tracking #2
297ETL Checkpoint #3
298Warehouse Lab for user behavior tracking #4
299Data Engineering Playbook #5
300Data Pipeline for recommendation engine #6
301Sprint: increase data discoverability #7
ETL Checkpoint #8

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