Apache Beam is an open-sourced, unified model for implementing stream and batch data processing jobs for any pipeline runners and in multiple languages. Together with other members of the community, our committers proudly contribute to the Apache Beam project.
If you’re already using Apache Beam services but need to adjust them to your needs, we got you! We can write IOs (connectors) for your data source, or create tests for your pipelines using our Beam testing framework.
We’ll review your existing Beam pipelines, deliver recommendations, and guide you through the process.
We’ll use our expertise to help your open-source project grow and get the momentum it deserves.
We can incorporate Beam in your project, e.g. write a backend for a real-time app.
We have 3
...who make the list of 100
most active contributors
in the project.
Polidea delivered a testing framework that would allow Beam users to check if their solution is correct and performant—which could help assess which parts of the Apache Beam code need fixing or optimizing—and make an easy estimation of which Beam’s runner/IO/filesystem to use with Apache Beam. Running tests in simulated environments would clarify early on which approach might potentially generate the least amount of bugs and problems, as well as save the engineering team’s time and money.
What is Apache Beam used for?
Apache Beam is a data processing framework used to process data in batch and streaming ways on various processing engines such as Dataflow, Flink, Spark, Samza. In Apache Beam it’s possible to write your code in SDK of your choice such as Java, Python or Go.
What data processing engines are supported by Apache Beam?
Apache Flink, Apache Nemo, Apache Samza, Apache Spark, Google Cloud Dataflow, and Hazelcast Jet. Apart from that, you can always execute your code locally for testing and debugging purposes.
What is the role of Apache Beam in GCP?
The history of Apache Beam started when Google donated the Cloud Dataflow SDK to the Apache Software Foundation. Now, Apache Beam is the only way for executing data processing pipelines within the Google Cloud Dataflow ecosystem.
Is there any difference between writing batch and streaming jobs?
Since Apache Beam’s model is unified, you can use one API for writing both batch and streaming jobs. This perk sets Beam apart from other similar solutions, where often batch and streaming jobs have different APIs.
What is the biggest advantage of using Apache Beam?
None of the existing data processing engines has been chosen as a default standard in the industry. In the face of that, Apache Beam is a safe choice for new projects, because it makes a potential transition to a different data processing engine a lot easier.