Airflow is the almost de-facto standard job orchestration tool that is used in the production stage. But moving your job from the development stage in other tools to the production stage in Airflow is usually a big pain for lots of users. A major reason is due to the environment inconsistency between the development environment and the production environment. Apache Zeppelin is a web-based notebook that is integrated seamlessly with lots of popular big data engines, such as Spark, Flink, Hive, Presto and etc. So it is very suitable for the development stage. In this talk, I will talk about the seamless integration between Airflow & Zeppelin, so that you can develop your big data job in Zeppelin efficiently and move to Airflow easily without caring too much about issues caused by the environment inconsistency.

Get your ticket for this workshop Tensorflow Extended (TFX) can run machine learning pipelines on Airflow, but all the steps are run by default in the same workers where the Airflow DAG is running. This can lead to an excessive usage of resources, and breaks the assumption that Airflow is a scheduler; it becomes also the data processing platform. In this session, we will see how to use TFX with third party services, on top of Google Cloud Platform. The data processing steps can be run in Dataflow, Spark, Flink and other runners (parallelizing the processing of data and scaling up to petabytes), and the training steps can be run in Vertex or other external services. After this workshop, you will have learnt how to externalize any TFX heavyweight computing outside Airflow, while maintaining Airflow as the orchestrator for your machine learning pipelines.