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  4. Stream Processing
  5. Faust vs KSQL

Faust vs KSQL

OverviewComparisonAlternatives

Overview

KSQL
KSQL
Stacks57
Followers126
Votes5
GitHub Stars256
Forks1.0K
Faust
Faust
Stacks26
Followers80
Votes0
GitHub Stars6.8K
Forks536

Faust vs KSQL: What are the differences?

Introduction

When it comes to stream processing, Faust and KSQL are two popular choices among developers. Both tools provide a platform for processing real-time data efficiently. However, there are key differences between Faust and KSQL that developers should be aware of before choosing one for their projects.

  1. Implementation Language: Faust is implemented in Python, making it a popular choice among Python developers who are already familiar with the language. On the other hand, KSQL is built using Java, which might appeal more to developers with experience in Java programming.

  2. Performance: Faust is known for its high-performance capabilities, as it is designed to handle high-throughput stream processing tasks efficiently. In contrast, KSQL might not be as performant as Faust in certain scenarios, especially when dealing with large volumes of data.

  3. Ecosystem Integration: Faust integrates seamlessly with the Kafka ecosystem, allowing developers to leverage its full potential for stream processing tasks. KSQL, on the other hand, is specifically designed to work with Kafka, offering native integration and compatibility with Kafka streams.

  4. Flexibility: Faust provides developers with more flexibility in terms of customizing stream processing workflows, thanks to its Python-based DSL. On the contrary, KSQL has a more rigid syntax and might not offer as much flexibility for customized stream processing tasks.

  5. Community Support: Faust has a growing community of users and contributors, providing ample resources and support for developers. KSQL, being part of the Confluent Platform, also benefits from a strong community of Kafka users but might have a more specialized user base.

  6. Learning Curve: Faust's Python-based DSL makes it easier for Python developers to get started with stream processing, reducing the learning curve for new users. In comparison, KSQL, with its Java-based syntax, might require more effort from developers to familiarize themselves with the tool.

In Summary, Faust and KSQL differ in terms of implementation language, performance, ecosystem integration, flexibility, community support, and learning curve, making them suitable for different use cases based on developer preferences and project requirements.

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Detailed Comparison

KSQL
KSQL
Faust
Faust

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.

It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.

Real-time; Kafka-native; Simple constructs for building streaming apps
Stream processing; Event processing; Build high performance distributed systems; Real-time data pipelines
Statistics
GitHub Stars
256
GitHub Stars
6.8K
GitHub Forks
1.0K
GitHub Forks
536
Stacks
57
Stacks
26
Followers
126
Followers
80
Votes
5
Votes
0
Pros & Cons
Pros
  • 3
    Streamprocessing on Kafka
  • 2
    SQL syntax with windowing functions over streams
  • 0
    Easy transistion for SQL Devs
No community feedback yet
Integrations
Kafka
Kafka
Python
Python
Flask
Flask
Django
Django
Pandas
Pandas
PyTorch
PyTorch
NumPy
NumPy
NLTK
NLTK
SQLAlchemy
SQLAlchemy

What are some alternatives to KSQL, Faust?

Apache NiFi

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.

Apache Storm

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

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

Heron

Heron

Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.

Kafka Streams

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.

Kapacitor

Kapacitor

It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.

Redpanda

Redpanda

It is a streaming platform for mission critical workloads. Kafka® compatible, No Zookeeper®, no JVM, and no code changes required. Use all your favorite open source tooling - 10x faster.

Samza

Samza

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

Benthos

Benthos

It is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads.

Amazon WorkSpaces Streaming Protocol

Amazon WorkSpaces Streaming Protocol

It is a cloud-native streaming protocol that enables a consistent user experience when accessing your end user’s WorkSpaces across global distances and unreliable networks. It also enables additional features such as the beta feature of bi-directional video. As a cloud-native protocol, it delivers feature and performance enhancements without manual updates on your WorkSpaces.

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