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Kafka Streams

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Kafka Streams vs KSQL: What are the differences?

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; KSQL: Open Source Streaming SQL for Apache Kafka. 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.

Kafka Streams and KSQL can be categorized as "Stream Processing" tools.

KSQL is an open source tool with 2.37K GitHub stars and 493 GitHub forks. Here's a link to KSQL's open source repository on GitHub.

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Streamprocessing on Kafka
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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 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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What companies use Kafka Streams?

What companies use KSQL?

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What tools integrate with Kafka Streams?

What tools integrate with KSQL?

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Kafka

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What are some alternatives to Kafka Streams and KSQL?

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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Kafka Streams vs KSQL

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What is Kafka Streams?

What is KSQL?

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What companies use Kafka Streams?

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What tools integrate with Kafka Streams?

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