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Apache Flink VS Apache Spark

Compare Apache Flink VS Apache Spark and see what are their differences


Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Apache Flink Landing Page
Apache Flink Landing Page
Apache Spark Landing Page
Apache Spark Landing Page

Apache Flink details

Categories
Big Data Web Framework Databases
Website flink.apache.org  

Apache Spark details

Categories
Databases Big Data Big Data Analytics Big Data Infrastructure
Website spark.apache.org  

Apache Flink videos

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Apache Spark videos

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • - What's New in Apache Spark 3.0.0
  • - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Apache Flink and Apache Spark)
33
33%
67%
67
19
19%
81%
81
100
100%
0%
0
0
0%
100%
100

Social recommendations and mentions

We have tracked the following product recommendations or mentions on Reddit and HackerNews. They can help you identify which product is more popular and what people think of it.

Apache Flink mentions

  • 7 Real-Time Data Streaming Tools You Should Consider On Your Next Project
    Apache Flink is another popular open source distributed data streaming engine that performs stateful computations over bounded and unbounded data streams. This framework is written in Scala and Java and is ideal for complex data stream computations. - Source: dev.to / 28 days ago
  • Data Driven Development for Complex Systems, Part 3
    This post continues a series on data-driven development best practices. These are specifically made for software systems with highly complicated integration points, but are applicable to many different situations. This series uses stream processing with Apache Flink for the examples. - Source: dev.to / 10 days ago

Apache Spark mentions

  • Unit testing your PySpark library
    In software development we often unit test our code (hopefully). And code written for Spark is no different. So here I want to run through an example of building a small library using PySpark and unit testing it. I'm using Visual Studio Code as my editor here, mostly because I think it's brilliant, but other editors are available. - Source: dev.to / 21 days ago

What are some alternatives?

When comparing Apache Flink and Apache Spark, you can also consider the following products

Spring Framework - The Spring portfolio has many projects, including Spring Framework, Spring IO Platform, Spring Cloud, Spring Boot, Spring Data, Spring Security...

Hadoop - Open-source software for reliable, scalable, distributed computing

Spark - Spark helps you take your inbox under control. Instantly see what’s important and quickly clean up the rest. Spark for Teams allows you to create, discuss, and share email with your colleagues

Hive - Seamless project management and collaboration for your team.

Grails - An Open Source, full stack, web application framework for the JVM

Hortonworks - Hadoop-Related

User reviews

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