Apache Storm vs KSQL

Overview

Apache Storm

Stacks207

Followers282

Votes25

GitHub Stars6.7K

Forks4.1K

KSQL

Stacks57

Followers126

Votes5

GitHub Stars256

Forks1.0K

Apache Storm vs KSQL: What are the differences?

Apache Storm: Distributed and fault-tolerant realtime computation. 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; 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.

Apache Storm and KSQL can be categorized as "Stream Processing" tools.

Apache Storm and KSQL are both open source tools. Apache Storm with 5.81K GitHub stars and 3.94K forks on GitHub appears to be more popular than KSQL with 2.4K GitHub stars and 498 GitHub forks.

Spotify, Twitter, and Yelp are some of the popular companies that use Apache Storm, whereas KSQL is used by Doodle, Landoop, and FREE NOW (formerly mytaxi). Apache Storm has a broader approval, being mentioned in 57 company stacks & 64 developers stacks; compared to KSQL, which is listed in 3 company stacks and 7 developer stacks.

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Detailed Comparison

Apache Storm	KSQL
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.	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.
Storm integrates with the queueing and database technologies you already use;Simple API;Scalable;Fault tolerant;Guarantees data processing;Use with any language;Easy to deploy and operate;Free and open source	Real-time; Kafka-native; Simple constructs for building streaming apps
Statistics
GitHub Stars 6.7K	GitHub Stars 256
GitHub Forks 4.1K	GitHub Forks 1.0K
Stacks 207	Stacks 57
Followers 282	Followers 126
Votes 25	Votes 5
Pros & Cons
Pros 10 Flexible 6 Easy setup 4 Event Processing 3 Clojure 2 Real Time	Pros 3 Streamprocessing on Kafka 2 SQL syntax with windowing functions over streams 0 Easy transistion for SQL Devs
Integrations
No integrations available	Kafka

What are some alternatives to Apache Storm, KSQL?

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.

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 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

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

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

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.

Faust

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.

Samza

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

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

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.