Data Engineering Zoomcamp

• Register in DataTalks.Club's Slack
• Join the #course-data-engineering channel
• Join the course Telegram channel with announcements
• The videos are published on DataTalks.Club's YouTube channel in the course playlist
• Frequently asked technical questions

Syllabus

• 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
• Project

Taking the course

2025 Cohort

• Start: 13 January 2025
• Registration link: https://airtable.com/shr6oVXeQvSI5HuWD
• Materials specific to the cohort: cohorts/2025/

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

Syllabus

We encourage Learning in Public

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

Project

Putting everything we learned to practice

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

More details

Overview

Prerequisites

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.

Instructors

• Ankush Khanna
• Victoria Perez Mola
• Alexey Grigorev
• Luis Oliveira
• Michael Shoemaker

Past instructors:

• Sejal Vaidya
• Irem Erturk

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:

• Follow these recommendations when asking for help
• Read the DataTalks.Club community guidelines

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 alexey@datatalks.club