• 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

• Week 1: Introduction & Prerequisites
• Week 2: Data ingestion
• Week 3: Data Warehouse
• Week 4: Analytics Engineering
• Week 5: Batch processing
• Week 6: Streaming
• Week 7, 8 & 9: Project

• Start: 16 January 2023 (Monday) at 18:00 CET
• Registration link: https://airtable.com/shr6oVXeQvSI5HuWD
• Subscribe to our public Google Calendar (it works from Desktop only)

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

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

Note: NYC TLC changed the format of the data we use to parquet. But you can still access the csv files here.

• 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

• Data Lake
• Workflow orchestration
• Introduction to Prefect
• ETL with GCP & Prefect
• Parametrizing workflows
• Prefect Cloud and additional resources
• Homework

More details

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

More details

• 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

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

More details

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

More details

Putting everything we learned to practice

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

More details

• 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

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.

• Ankush Khanna (https://linkedin.com/in/ankushkhanna2)
• Sejal Vaidya (https://linkedin.com/in/vaidyasejal)
• Victoria Perez Mola (https://www.linkedin.com/in/victoriaperezmola/)
• Alexey Grigorev (https://linkedin.com/in/agrigorev)

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

• 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!

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

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