Apache Flink Reviews

Name: Apache Flink
Brand: Apache
Rating: 4 (15 reviews)

Vendor: Apache

3.8 out of 5

15 reviews
93% willing to recommend

155 followers

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Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.

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Apache Flink is the #5 ranked solution in Streaming Analytics tools. PeerSpot users give Apache Flink an average rating of 7.6 out of 10. Apache Flink is most commonly compared to Databricks: Apache Flink vs Databricks. Apache Flink is popular among the large enterprise segment, accounting for 71% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 21% of all views.

Helped 768,246 peers since 2012

Featured reviews

Ilya Afanasyev

Senior Software Development Engineer at Yahoo!

Aug 3, 2022

We predominantly use this solution on-premises but intend to migrate to some management services. Our primary use case for this solution is maintaining some pipelines which process data. We are currently integrating some pipelines from Pig to Spark and Pig to Flink We value this solution's…

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PrashantVaghela

Principal Engineer at InnovAccer Inc.

Nov 20, 2023

One of the ways to interact with Flink is through a tool called PipeLINK for writing Flink code, and it doesn't require you to use Python directly. While it does offer a Python-like syntax called PyFlink. PyFlink is a subset of Python that is specifically designed for writing Flink code. It provides a simpler and more accessible way to write Flink code compared to using the Java or Scala APIs. PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it. So, this is an area for improvement. However, it is a good choice for users who are not familiar with Java or Scala.

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Armando Becerril

Partner / Head of Data & Analytics at Kueski

Mar 3, 2021

One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there. I am also looking for more possibilities in terms of what can be implemented in containers and not in Kubernetes. I think our architecture would work really great with more options available to us in this sense. Finally, it's a challenge to find people with the appropriate skills for using Flink. There are a lot of people who know what should be done better in big data systems, but there are still very few people with Flink capabilities.

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Apache Flink market share

As of March 2024, the market share of Apache Flink in the Streaming Analytics category stands at 8.9%, marking a decrease of 5.4% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Streaming Analytics

Key learnings from peers

Last updated Feb 11, 2024

Valuable Features

"The product helps us to create both simple and complex data processing tasks. Over time, it has facilitated integration and navigation across multiple data sources tailored to each client's needs. We use Apache Flink to control our clients' installations."
"It provides us the flexibility to deploy it on any cluster without being constrained by cloud-based limitations."
"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

Room for Improvement

"Apache Flink should improve its data capability and data migration."
"There is room for improvement in the initial setup process."
"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

Pricing

"It's an open source."
"It's an open-source solution."
"Apache Flink is open source so we pay no licensing for the use of the software."

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Apache Flink Buyer's Guide for additional reliable information.

Review data by company size

By reviewers

By visitors reading reviews

Top industries

By visitors reading reviews

Financial Services Firm

21%

Computer Software Company

16%

Retailer

Manufacturing Company

Comms Service Provider

Healthcare Company

Educational Organization

Media Company

Insurance Company

University

Construction Company

Energy/Utilities Company

Real Estate/Law Firm

Recreational Facilities/Services Company

Transportation Company

Government

Legal Firm

Logistics Company

Wholesaler/Distributor

Performing Arts

Non Profit

Hospitality Company

Outsourcing Company

Consumer Goods Company

Pharma/Biotech Company

Compare Apache Flink with alternative products

Learn more about Apache Flink

Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.

Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.

Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.

Apache Flink Features

Distributed execution of streaming programs on clusters of computers
Support for multiple data sources and sinks: this includes Hadoop file systems, databases, and other data sources
Streaming SQL query engine with support for windowing functions
Low latency query execution in milliseconds
Runs in a distributed fashion: it can be deployed on multiple machines or nodes to increase performance and reliability of data processing pipelines.
Powerful API that supports both batch and streaming applications
Runs on clusters of commodity hardware with minimal configuration
Can be integrated with other technologies, such as Apache Spark for complex data mining

Apache Flink Benefits

Ease of use: Flink has an intuitive API and provides high-level abstractions for handling data streams. Even beginners in the field can work with the platform with ease.

Fault tolerance: Flink can automatically detect and recover from failures in the system.

Scalability: Flink scales to thousands of nodes. It can run on clusters of any size and the user does not have to worry about managing the cluster.

Reviews from Real Users

Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:

The head of data and analytics at a computer software company notes, “The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis.”

Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”

Apache Flink was previously known as Flink.