data-engineering-zoomcamp icon indicating copy to clipboard operation
data-engineering-zoomcamp copied to clipboard

Free Data Engineering course!

Data Engineering Zoomcamp


  • Module 1: Containerization and Infrastructure as Code
  • Module 2: Workflow Orchestration
  • Workshop 1: Data Ingestion
  • Module 3: Data Warehouse
  • Module 4: Analytics Engineering
  • Module 5: Batch processing
  • Module 6: Streaming
  • Workshop 2: Stream Processing with SQL
  • Project

Taking the course

2024 Cohort

  • Start: 15 January 2024 (Monday) at 17:00 CET
  • Registration link:
  • Cohort folder with homeworks and deadlines
  • Launch stream with course overview

Self-paced mode

All the materials of the course are freely available, so that you can take the course at your own pace

  • Follow the suggested syllabus (see below) week by week
  • You don't need to fill in the registration form. Just start watching the videos and join Slack
  • Check FAQ if you have problems
  • If you can't find a solution to your problem in FAQ, ask for help in Slack


Note: NYC TLC changed the format of the data we use to parquet. In the course we still use the CSV files accessible here.

Module 1: Containerization and Infrastructure as Code

  • Course overview
  • Introduction to GCP
  • Docker and docker-compose
  • Running Postgres locally with Docker
  • Setting up infrastructure on GCP with Terraform
  • Preparing the environment for the course
  • Homework

More details

Module 2: Workflow Orchestration

  • Data Lake
  • Workflow orchestration
  • Workflow orchestration with Mage
  • Homework

More details

Workshop 1: Data Ingestion

  • Reading from apis
  • Building scalable pipelines
  • Normalising data
  • Incremental loading
  • Homework

More details

Module 3: Data Warehouse

  • Data Warehouse
  • BigQuery
  • Partitioning and clustering
  • BigQuery best practices
  • Internals of BigQuery
  • BigQuery Machine Learning

More details

Module 4: Analytics engineering

  • Basics of analytics engineering
  • dbt (data build tool)
  • BigQuery and dbt
  • Postgres and dbt
  • dbt models
  • Testing and documenting
  • Deployment to the cloud and locally
  • Visualizing the data with google data studio and metabase

More details

Module 5: Batch processing

  • Batch processing
  • What is Spark
  • Spark Dataframes
  • Spark SQL
  • Internals: GroupBy and joins

More details

Module 6: Streaming

  • Introduction to Kafka
  • Schemas (avro)
  • Kafka Streams
  • Kafka Connect and KSQL

More details

Workshop 2: Stream Processing with SQL

More details


Putting everything we learned to practice

  • Week 1 and 2: working on your project
  • Week 3: reviewing your peers

More details



To get the most out of this course, you should feel comfortable with coding and command line and know the basics of SQL. Prior experience with Python will be helpful, but you can pick Python relatively fast if you have experience with other programming languages.

Prior experience with data engineering is not required.


Past instructors:

Asking for help in Slack

The best way to get support is to use DataTalks.Club's Slack. Join the #course-data-engineering channel.

To make discussions in Slack more organized:

Supporters and partners

Thanks to the course sponsors for making it possible to run this course

Do you want to support our course and our community? Please reach out to [email protected]

Star History

Star History Chart