Apache Beam vs Kafka Streams

Overview

Apache Beam

Stacks183

Followers361

Votes14

Kafka Streams

Stacks404

Followers478

Votes0

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.

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.
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.
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.
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.
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.
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.

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Advice on Apache Beam, Kafka Streams

Balaji

Jun 23, 2020

Needs adviceon

Apache Beam

Amazon EMR

Kafka

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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Detailed Comparison

Apache Beam	Kafka Streams
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.	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.
Statistics
Stacks 183	Stacks 404
Followers 361	Followers 478
Votes 14	Votes 0
Pros & Cons
Pros 5 Cross-platform 5 Open-source 2 Unified batch and stream processing 2 Portable	No community feedback yet

What are some alternatives to Apache Beam, Kafka Streams?

Airflow

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Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Confluent

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Luigi

It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

Unito

Build and map powerful workflows across tools to save your team time. No coding required. Create rules to define what information flows between each of your tools, in minutes.

KSQL

KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.

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Related Comparisons

Apache Beam vs Kafka Streams: What are the differences?

Key Differences between Apache Beam and Kafka Streams

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.
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.
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.
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.
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.
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.

Apache Beam vs Kafka Streams

Overview

Apache Beam vs Kafka Streams: What are the differences?