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Data Engineering Zoomcamp

Syllabus

Taking the course

Self-paced mode

All the materials of the course are freely available, so 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

2022 Cohort

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:

Syllabus

Week 1: Introduction & Prerequisites

  • 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

Week 2: Data ingestion

  • Data Lake
  • Workflow orchestration
  • Setting up Airflow locally
  • Ingesting data to GCP with Airflow
  • Ingesting data to local Postgres with Airflow
  • Moving data from AWS to GCP (Transfer service)
  • Homework

More details

Week 3: Data Warehouse

  • Data Warehouse
  • BigQuery
  • Partitoning and clustering
  • BigQuery best practices
  • Internals of BigQuery
  • Integrating BigQuery with Airflow
  • BigQuery Machine Learning

More details

Week 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
  • Visualising the data with google data studio and metabase

More details

Week 5: Batch processing

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

More details

Week 6: Streaming

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

More details

Week 7, 8 & 9: Project

Putting everything we learned to practice

  • Week 7 and 8: working on your own project
  • Week 9: reviewing your peers

More details

Overview

Architecture diagram

Technologies

  • Google Cloud Platform (GCP): Cloud-based auto-scaling platform by Google
    • Google Cloud Storage (GCS): Data Lake
    • BigQuery: Data Warehouse
  • Terraform: Infrastructure-as-Code (IaC)
  • Docker: Containerization
  • SQL: Data Analysis & Exploration
  • Airflow: Pipeline Orchestration
  • dbt: Data Transformation
  • Spark: Distributed Processing
  • Kafka: Streaming

Prerequisites

To get 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

Tools

For this course you'll need to have the following software installed on your computer:

  • Docker and Docker-Compose
  • Python 3 (e.g. via Anaconda)
  • Google Cloud SDK
  • Terraform

See Week 1 for more details about installing these tools

FAQ

  • Q: I registered, but haven't received a confirmation email. Is it normal? A: Yes, it's normal. It's not automated. But you will receive an email eventually
  • Q: At what time of the day will it happen? A: Office hours will happen on Mondays at 17:00 CET. But everything will be recorded, so you can watch it whenever it's convenient for you
  • Q: Will there be a certificate? A: Yes, if you complete the project
  • Q: I'm 100% not sure I'll be able to attend. Can I still sign up? A: Yes, please do! You'll receive all the updates and then you can watch the course at your own pace.
  • Q: Do you plan to run a ML engineering course as well? A: Glad you asked. We do :)
  • Q: I'm stuck! I've got a technical question! A: Ask on Slack! And check out the student FAQ; many common issues have been answered already. If your issue is solved, please add how you solved it to the document. Thanks!

Our friends

Big thanks to other communities for helping us spread the word about the course:

Check them out - they are cool!