We will be offering hands-on workshops on how to use Apache Beam in different scenarios and with different infrastructure.

  • Format & duration: We will have 1 hour and 2 hour workshops. You can expect the 2 hour workshops to go more in depth and allow time for participants to go through the labs, while the 1 hour workshops will have a quicker pace.
  • Capacity: Capacity is limited to 60 participants per workshop. We will enable a wait list and see if it is necessary to open more groups. Please register for a workshop only if you are really committed to participating in it.

Workshop: First steps with Apache Beam - Writing portable pipelines using Java, Python, Go, Kotlin

Date: Aug-24 16:40-18:40 UTC
Instructor(s): Austin Bennett

This workshop is for participants who are getting started with Apache Beam. We will get hands-on writing Beam pipelines, as well as discuss the fundamentals of Beam programming model and SDKs.

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Workshop: Building big data pipelines for deep learning

Date: Aug-24 20:00-21:00 UTC
Instructor(s): Jenny Lu

We will work through an end-to-end example of using the Python Beam SDK to build maintainable, reliable, and scalable production pipelines for deep learning.

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Workshop: Implement a streaming data pipeline with Google Cloud Dataflow

Date: Aug-25 16:00-18:00 UTC
Instructor(s): Reza Rokni, David Sabater Dinter & Wei Hsia

We will implement a Google Cloud Dataflow pipeline through a series of labs where we will explore functionalities like auto-scaling, monitoring and optimization.

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Workshop: Using Conda on Apache beam - there is nothing your cant do with your workers

Date: Aug-25 20:00-21:00 UTC
Instructor(s): Eila Arich-Landkof

Learn how to install Conda on your worker machine, personalize your workers’ environment and integrate with your Apache Beam pipeline.

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Workshop: Build a Unified Batch and Stream Processing Pipeline with Apache Beam on AWS

Date: Aug-26 16:00-18:00 UTC
Instructor(s): Steffen Hausmann, Karthi Thyagarajan & Rajan Pattel

We will explore an end to end example that combines batch and streaming aspects in one uniform Beam pipeline, and deploy it to a fully managed environment with Amazon Kinesis Data Analytics.

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Live demo: An Interactive Introduction to Apache Beam

Date: Aug-26 20:00-21:00 UTC
Instructor(s): Ning Kang & Sam Rohde

We will introduce Interactive Beam by presenting Jupyter notebooks with examples using publicly available real world data.

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Workshop: Writing custom components for TensorFlow Extended with Apache Beam

Date: Aug-27 20:00-21:00 UTC
Instructor(s): Hannes Hapke & Timo Cornelius Metzger

Step-by-step walkthrough of how to write custom TFX components with Apache Beam to customize your ML pipelines beyond the standard components and tailor the components for their ML pipelines.

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