Batch Processing with Apache Beam 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
- 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
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
This course is for all levels. You do not need any previous knowledge of Apache Beam or Cloud Dataflow.
Who this course is for:
- Data Engineers
- Aspiring Data Engineers
- Python developers interested in Apache Beam
Alexandra is a Google Cloud Certified Data Engineer & Architect and Apache Airflow Contributor.
She has experience with large-scale data science and engineering projects. She spends her time building data pipelines using Apache Airflow and Apache Beam and creating production ready Machine Learning pipelines with Tensorflow.
Alexandra was a speaker at Serverless Days London 2019 and presented at the Tensorflow London meetup.