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Apache Beam

140
261
+ 1
14
Kafka Streams

265
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+ 1
0
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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.

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Pros of Apache Beam
Pros of Kafka Streams
  • 5
    Open-source
  • 5
    Cross-platform
  • 2
    Portable
  • 2
    Unified batch and stream processing
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    What companies use Apache Beam?
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    What tools integrate with Apache Beam?
    What tools integrate with Kafka Streams?
    What are some alternatives to Apache Beam and Kafka Streams?
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    Kafka
    Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
    Airflow
    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
    Google Cloud Dataflow
    Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.
    Apache Flink
    Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
    See all alternatives