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Kafka Streams vs Samza: What are the differences?

Introduction: Apache Kafka Streams and Apache Samza are both distributed stream processing frameworks that provide developers with the capability to process and analyze real-time data streams. While they have similarities, there are key differences between Kafka Streams and Samza that distinguish them in terms of use cases and features.

  1. Architecture: Kafka Streams is a client library that allows developers to build stream processing applications that directly interface with Apache Kafka. In contrast, Samza is a full-fledged stream processing framework that provides a distributed processing engine, job coordination, and fault tolerance capabilities. The architecture of Samza is more suitable for complex processing requirements and high-throughput scenarios.

  2. Integration with External Systems: Kafka Streams is tightly integrated with Apache Kafka, making it easier to build stream processing applications with data streams stored in Kafka topics. On the other hand, Samza offers more flexibility in terms of integration with external systems through its pluggable input/output system. This allows developers to connect Samza applications with various data sources and sinks beyond Kafka.

  3. State Management: Kafka Streams provides built-in state management capabilities that allow developers to store and query state within the stream processing application. In comparison, Samza requires developers to manage states explicitly by providing custom state stores or leveraging external storage systems like Apache HBase or Apache Cassandra. This difference affects the complexity and scalability of stateful stream processing applications.

  4. Processing Model: Kafka Streams leverages the concept of message timestamps to provide event-time processing semantics, allowing developers to handle out-of-order data events efficiently. In contrast, Samza's processing model is based on processing-time semantics by default, making it easier to implement real-time processing tasks that do not require event-time considerations. The choice of processing model impacts the consistency and correctness of stream processing results.

  5. Deployment and Scalability: Kafka Streams applications can be deployed as standalone Java applications or as microservices within a larger ecosystem. This flexibility simplifies deployment and scaling of stream processing tasks. In contrast, Samza applications need to be deployed on a cluster managed by Apache YARN, which provides centralized resource management and scalability capabilities. This difference affects the operational complexity and resource utilization efficiency of stream processing deployments.

  6. Community and Ecosystem: Kafka Streams benefits from the wide adoption of Apache Kafka and a vibrant community that actively contributes to its development and support. This results in a rich ecosystem of tools, libraries, and resources for Kafka Streams users. While Samza also has an active community, it may have a smaller user base compared to Kafka Streams, leading to differences in available resources and community support.

In Summary, Apache Kafka Streams and Apache Samza differ in their architecture, integration capabilities, state management, processing models, deployment options, and community ecosystems, catering to diverse stream processing requirements.

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What is Kafka Streams?

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.

What is Samza?

It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

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Jun 24 2020 at 4:42PM

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What are some alternatives to Kafka Streams and Samza?
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
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.
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.
Apache Beam
It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
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.
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