Easy to follow, hands-on introduction to batch data processing in Python
What you'll learn
- Core concepts of the Apache Beam framework
- How to design a pipeline in Apache Beam
- How to install Apache Beam locally
- How to build a real-world ETL pipeline in Apache Beam
- How to read and write CSV data from Apache Beam
- How to apply built-in and custom transformations on a dataset
- How to deploy your pipeline to Cloud Dataflow on Google Cloud
Requirements
- Python programming experience
- Having an idea of distributed data processing e.g. You have used Spark before
- Having Conda (or other Virtual Environment Manager) installed on your machine
Description
Apache Beam is an open-source programming model for defining large scale ETL, batch and streaming data processing pipelines. It is used by companies like Google, Discord and PayPal.
In this course you will learn Apache Beam in a practical manner, with every lecture comes a full coding screencast. By the end of the course you'll be able to build your own custom batch data processing pipeline in Apache Beam.
This course includes 20 concise bite-size lectures and a real-life coding project that you can add to your Github portfolio! You're expected to follow the instructor and code along with her.
You will learn:
- How to install Apache Beam on your machine
- Basic and advanced Apache Beam concepts
- How to develop a real-world batch processing pipeline
- How to define custom transformation steps
- How to deploy your pipeline on Cloud Dataflow
Who this course is for:
- Data Engineers
- Aspiring Data Engineers
- Python developers interested in Apache Beam
