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Apache Beam vs Kafka Streams: What are the differences?
Apache Beam: A unified programming model. It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments; Kafka Streams: A client library for building applications and microservices. It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
Apache Beam can be classified as a tool in the "Workflow Manager" category, while Kafka Streams is grouped under "Stream Processing".
Handshake, Skry, Inc., and Reelevant are some of the popular companies that use Apache Beam, whereas Kafka Streams is used by Doodle, Bottega52, and Scout24. Apache Beam has a broader approval, being mentioned in 9 company stacks & 4 developers stacks; compared to Kafka Streams, which is listed in 7 company stacks and 5 developer stacks.
I have to build a data processing application with an Apache Beam stack and Apache Flink runner on an Amazon EMR cluster. I saw some instability with the process and EMR clusters that keep going down. Here, the Apache Beam application gets inputs from Kafka and sends the accumulative data streams to another Kafka topic. Any advice on how to make the process more stable?
So, you are using Apache Beam and Apache Flink to read from an input kafka topic, apply some transformations to the input and then write to another output kafka topic? it looks like that this is a solution for kafka-streams framework, isn't?. if the process is not very stable, it is probably because you don't have the right amount of memory for these processes, or you don't have enough dedicated cores for it.
Investigate using the Confluent platform's control-center tool, look at logs, examine process exceptions, focus on caused by.
Unless you have a great need to use Apache Flink's supposedly better real-time data streaming capabilities, stick with kafka-streams to do that task. Then look into doing the same with Beam and Flink, but when you have it, you can measure if you really have a big performance improvement when reading and writing to kafka topics. I honestly doubt it.
Pros of Apache Beam
- Open-source5
- Cross-platform5
- Portable2
- Unified batch and stream processing2
- Nhat0
