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Apache Beam vs Kafka Streams: What are the differences?

Key Differences between Apache Beam and Kafka Streams

Apache Beam and Kafka Streams are two popular frameworks used for building real-time stream processing applications. While both offer similar functionalities, there are some key differences between them that set them apart.

  1. Programming Model: Apache Beam provides a unified programming model that allows developers to write their stream processing logic in a language-agnostic manner using a set of APIs. On the other hand, Kafka Streams is a library that requires developers to write code in Java or Scala, tightly coupling the application logic with the specific language.

  2. Flexibility: Apache Beam offers more flexibility in terms of compatibility with various execution engines and data processing backends. It supports multiple execution engines like Apache Flink, Apache Spark, and Google Cloud Dataflow, making it easier to switch between different environments. In contrast, Kafka Streams is tightly integrated with the Apache Kafka ecosystem and is limited to running on the Kafka Streams API.

  3. Scalability: Apache Beam architecture allows for horizontal scalability by distributing processing across multiple machines, making it suitable for handling large-scale data processing workloads. Kafka Streams, on the other hand, is designed to run on a single Kafka Streams processing cluster, limiting its scalability compared to Apache Beam.

  4. Event Time Processing: Apache Beam provides built-in support for event time processing, allowing developers to handle out-of-order events and perform windowing operations based on event timestamps. Kafka Streams, on the other hand, lacks native support for event time processing, requiring developers to implement custom logic for handling out-of-order events.

  5. Ecosystem Integration: Apache Beam integrates with various data processing and storage systems, including Apache Hadoop, Apache Hive, and many cloud platforms. This allows for seamless integration with existing data infrastructure and enables developers to leverage the capabilities of these systems. Kafka Streams, on the other hand, is tightly integrated with the Apache Kafka ecosystem, making it well-suited for building stream processing applications that directly consume and produce data from Kafka topics.

  6. Ease of Use: Apache Beam's unified programming model and rich set of abstractions make it easier for developers to write complex stream processing applications. It provides a higher level of abstraction, simplifying the development process and reducing the amount of boilerplate code needed. Kafka Streams, while powerful, requires more low-level coding and understanding of the Kafka Streams API, making it slightly more complex to work with.

In Summary, Apache Beam offers a language-agnostic, scalable, and flexible framework for stream processing, with built-in support for event time processing and integration with various data systems. On the other hand, Kafka Streams provides a more tightly integrated, lower-level library specifically designed for building stream processing applications within the Kafka ecosystem.

Advice on Apache Beam and Kafka Streams

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?

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Replies (1)
Alonso Isidoro Roman
Ingeniero de software, bigdata architect · | 4 upvotes · 3.6K views

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.

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    What tools integrate with Apache Beam?
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    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.
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