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Apache Flink vs IBM Streams comparison

Apache and IBM are both solutions in the Streaming Analytics category. Apache is ranked #5 with an average rating of 7.6, while IBM is ranked #18 with an average rating of 7.0. Apache holds a 12.1% mindshare in SA, compared to IBM’s 0.8% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 100% of IBM users who would recommend it.
Apache Flink
Read 16 Apache Flink reviews
  • 6,723 Views
  • 4,716 Comparison Views
94% willing to recommend
IBM Streams
Read 5 IBM Streams reviews
  • 503 Views
  • 415 Comparison Views
100% willing to recommend
 

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

IBM Streams and Apache Flink are both prominent products in the real-time stream processing category. IBM Streams has the upper hand in pricing and support, while Apache Flink leads with superior advanced features and data handling capabilities.

Features: IBM Streams provides powerful real-time analytics, seamless integration with IBM's ecosystem, and excels in managing complex data workflows. Apache Flink delivers outstanding performance in real-time data processing, supports event-driven applications, and offers flexibility in handling complex data flows, making it a competitive choice.

Room for Improvement: IBM Streams could benefit from enhancing its adaptability to non-IBM environments, improving scalability features, and expanding third-party integration capabilities. Apache Flink might improve by addressing the steep learning curve, enhancing documentation, and expanding commercial customer support to rival that of IBM Streams.

Ease of Deployment and Customer Service: Deploying IBM Streams is straightforward, supported by IBM’s extensive customer service network, which ensures efficient setup and operation. Apache Flink features a simpler deployment model backed by a strong community, though it lacks the depth of commercial support provided by IBM Streams.

Pricing and ROI: IBM Streams leads in competitive pricing, offering significant savings and a proven ROI in long-term use. Apache Flink, while not the lowest in initial setup cost, provides substantial ROI through its scalability and performance efficiency, justifying its price with advanced processing capabilities.

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Buyer's Guide
Apache Flink vs. IBM Streams
December 2024
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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Customer Service

Sentiment score
6.3
Users primarily depend on Apache Flink's community support and resources, occasionally opting for paid services for critical issues.
Sentiment score
7.8
IBM Streams customer support is praised for excellent tech support, proactive service, and a strong professional forum.
 

Scalability Issues

No sentiment score available
Sentiment score
9.0
IBM Streams is scalable and efficient for large data, supporting government and finance needs with low latency and efficient clustering.
 

Stability Issues

No sentiment score available
Sentiment score
5.8
IBM Streams is stable, enterprise-class software with reliable operators, though IDE crashes and external interface issues can occur.
 

Room For Improvement

IBM Streams needs enhancements in debugging, Python support, machine learning tools, dynamic scaling, and connector availability to reduce custom solutions.
 

Valuable Features

IBM Streams offers scalable, real-time analytics with Java, diverse connectors, and toolkits, benefiting healthcare and time series data processing.
 

Categories and Ranking

Apache Flink
Ranking in Streaming Analytics
5th
Average Rating
7.6
Reviews Sentiment
6.9
Number of Reviews
16
Ranking in other categories
No ranking in other categories
IBM Streams
Ranking in Streaming Analytics
18th
Average Rating
8.2
Reviews Sentiment
7.2
Number of Reviews
5
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the Streaming Analytics category, the mindshare of Apache Flink is 12.1%, up from 10.8% compared to the previous year. The mindshare of IBM Streams is 0.8%, down from 1.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Sunil Morya - PeerSpot reviewer
Easy to deploy and manage; lacking simple integration with Amazon products
The issue we had with Flink was that when you had to refer the schema into the input data stream, it had to be done directly into code. The XLS format where the schema is stored, had to be stored in Python. If the schema changes, you have to redeploy Flink because the basic tasks and jobs are already running. That's one disadvantage. Another was a restriction with Amazon's CloudFormation templates which don't allow for direct deployment in the private subnet. You have to deploy into the public subnet and then from the Amazon console, specify a different private subnet that requires a lot of settings. In general, the integration with Amazon products was not good and was very time-consuming. I'd like to think that has changed.
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Ahmed_Emad - PeerSpot reviewer
A solution for data pipelines but has connector limitations
We have used Kafka for seven years. IBM streams gives you many OOTB features that can boost the time-to-market, especially when it comes to reporting and monitoring for example. Confluent is recognized as one of the leaders in this space and the main reason for this is related to the complete vision of the platform also the large number of connectors. This gives the edge and competitive advatnage.
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Top Industries

By visitors reading reviews
Financial Services Firm
23%
Computer Software Company
17%
Manufacturing Company
6%
Healthcare Company
5%
Financial Services Firm
32%
Computer Software Company
20%
Healthcare Company
5%
Retailer
4%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about Apache Flink?
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. ...
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What is your experience regarding pricing and costs for Apache Flink?
The solution is expensive. I rate the product’s pricing a nine out of ten, where one is cheap and ten is expensive.
See all answers
What needs improvement with Apache Flink?
There are more libraries that are missing and also maybe more capabilities for machine learning. It could have a friendly user interface for pipeline configuration, deployment, and monitoring.
See all answers
What is your experience regarding pricing and costs for IBM Streams?
The solution’s licenses pricing is different from one region to another region. I rate the solution’s pricing a seven out of ten.
See all answers
What needs improvement with IBM Streams?
the limited number of connectors. This shall be overcome with work-arounds or eventually buying additional connectors to complete the solution.
See all answers
What is your primary use case for IBM Streams?
We use the solution for data pipeline by modernizing the traditional ETL jobs done through advanced streaming. Another use case is building the g2g streaming platform, which facilitates data exchan...
See all answers
 

Comparisons

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Compared 22% of the time
Azure Stream Analytics vs Apache Flink Logo
Azure Stream Analytics vs Apache Flink
Compared 14% of the time
Amazon Kinesis vs Apache Flink Logo
Amazon Kinesis vs Apache Flink
Compared 11% of the time
SAP Event Stream Processor vs Apache Flink Logo
SAP Event Stream Processor vs Apache Flink
Compared 1% of the time
More Apache Flink Competitors
Confluent vs IBM Streams Logo
Confluent vs IBM Streams
Compared 44% of the time
Amazon MSK vs IBM Streams Logo
Amazon MSK vs IBM Streams
Compared 19% of the time
Apache NiFi vs IBM Streams Logo
Apache NiFi vs IBM Streams
Compared 15% of the time
More IBM Streams Competitors
 

Product Reports

Buyer's Guide
Apache Flink
December 2024
Apache Flink reportDownload Apache Flink product report
Buyer's Guide
Streaming Analytics
December 2024
IBM Streams reportDownload IBM Streams product report
 

Also Known As

Flink
IBM InfoSphere Streams
 

Learn More

Apache
IBM
 

Overview

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.

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.”

IBM Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of data stream sources. The solution can handle very high data throughput rates, up to millions of events or messages per second. Streams helps you analyze data in motion, simplify development of streaming applications, and extend the value of existing systems.
 

Sample Customers

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.
Globo TV, All England Lawn Tennis Club, CenterPoint Energy, Consolidated Communications Holdings, Darwin Ecosystem, Emory University Hospital, ICICI Securities, Irish Centre for Fetal and Neonatal Translational Research (INFANT), Living Roads, Mobileum, Optibus, Southern Ontario Smart Computing Innovation Platform (SOSCIP), University of Alberta, University of Montana, University of Ontario Institute of Technology, Wimbledon 2015
Buyer's Guide
Apache Flink vs. IBM Streams
December 2024
Free Report: Apache Flink vs. IBM Streams
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