Flowman — A Declarative ETL Framework powered by Apache Spark

Don’t reinvent the wheel by writing more boilerplate code. Focus on critical business logic and delegate the tricky details to a clever tool.

Kaya Kupferschmidt
9 min readJun 2, 2023
Photo by Murat Onder on Unsplash

Introduction

Apache Spark is a powerful framework for building flexible and scalable data processing applications. Spark offers a relatively simple yet powerful API that provides the capabilities of classic SQL SELECT statements in a clean and flexible API. At the same time, Spark can access all kinds of data sources, such as object stores in the cloud (S3, ABS, …), relational databases via JDBC and much more via custom connectors (Kafka, HBase, MongoDB, Cassandra, …). This wide range of connectors, coupled with the extensive support for data transformations, supports both simple data ingestion, pure data transformation or any combination such as ETL and ELT — Sparks flexibility supports very different types of workflows to fit your exact needs.

But in the end, a framework for a data processing application is not enough. You still need to build an application on top of Apache Spark that includes all the business logic and lots of boilerplate code. In addition, some important data-related topics fall outside the scope of Apache Spark, such as robust…

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Kaya Kupferschmidt

Freelance Big Data and Machine Learning expert at dimajix.