Apache Flink vs TIBCO Streaming comparison

Apache and TIBCO are both solutions in the Streaming Analytics category. Apache is ranked #3 with an average rating of 8.0, while TIBCO is ranked #24. Apache holds a 11.3% mindshare in SA, compared to TIBCO’s 1.2% mindshare. Additionally, 95% of Apache users are willing to recommend the solution, compared to 100% of TIBCO users who would recommend it.

Apache Flink

Read 19 Apache Flink reviews

3,681 Views
3,681 Comparison Views

95% willing to recommend

TIBCO Streaming

Read 1 TIBCO Streaming review

426 Views
349 Comparison Views

100% willing to recommend

Apache Flink

TIBCO Streaming

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Nov 30, 2025

Apache Flink and TIBCO Streaming compete in real-time data processing. Flink is often preferred for scalability and data processing, while TIBCO Streaming stands out in enterprise integration and usability.

Features: Apache Flink offers advanced event-driven programming, state management, and large-scale data processing. TIBCO Streaming provides seamless connectivity with enterprise systems, sophisticated real-time analytics, and is favored in integration-heavy environments.

Ease of Deployment and Customer Service: Apache Flink provides flexible deployment but may require significant configuration with customer support varying due to reliance on community engagement. TIBCO Streaming offers robust customer support and easier setup within enterprise ecosystems, making it more adaptable for structured deployment and support.

Pricing and ROI: Apache Flink as an open-source solution presents lower initial setup costs with potentially higher ROI through reduced licensing fees. TIBCO Streaming, though more expensive, delivers value with integrated enterprise capabilities and support services, enhancing ROI through operational efficiencies.

To learn more, read our detailed Streaming Analytics Report (Updated: January 2026).

Buyer's Guide

Streaming Analytics

January 2026

Download the complete report

Helped 881,707 peers since 2012

Review summaries and opinions

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

Categories and Ranking

Apache Flink

Ranking in Streaming Analytics

3rd

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

TIBCO Streaming

Ranking in Streaming Analytics

24th

Average Rating

8.0

Number of Reviews

Ranking in other categories

Complex Event Processing (CEP) (1st)

Mindshare comparison

As of February 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 11.3%, down from 12.1% compared to the previous year. The mindshare of TIBCO Streaming is 1.2%, up from 0.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Flink	11.3%
TIBCO Streaming	1.2%
Other	87.5%

Streaming Analytics

Featured Reviews

Aswini Atibudhi

Distinguished AI Leader at Walmart Global Tech at Walmart

Enables robust real-time data processing but documentation needs refinement

Apache Flink is very powerful, but it can be challenging for beginners because it requires prior experience with similar tools and technologies, such as Kafka and batch processing. It's essential to have a clear foundation; hence, it can be tough for beginners. However, once they grasp the concepts and have examples or references, it becomes easier. Intermediate users who are integrating with Kafka or other sources may find it smoother. After setting up and understanding the concepts, it becomes quite stable and scalable, allowing for customization of jobs. Every software, including Apache Flink, has room for improvement as it evolves. One key area for enhancement is user-friendliness and the developer experience; improving documentation and API specifications is essential, as they can currently be verbose and complex. Debugging and local testing pose challenges for newcomers, particularly when learning about concepts such as time semantics and state handling. Although the APIs exist, they aren't intuitive enough. We also need to simplify operational procedures, such as developing tools and tuning Flink clusters, as these processes can be quite complex. Additionally, implementing one-click rollback for failures and improving state management during dynamic scaling while retaining the last states is vital, as the current large states pose scaling challenges.

Read full review

MichaelKogan

Head of Data and Analytics Solutions at a tech vendor with 201-500 employees

Good stability and scalability with the capability to combine with Spotfire

The ability for StreamBase to combine with Spotfire is its most valuable aspect. The ability to handle data in motion and the entirety of data at the same time is very good. If you don't integrate the two together it's just a monitoring tool, but if they're combined together it becomes a powerful analytical tool. When you can combine live data from streaming data sources with standing data from the customer's primary database then you can calculate some KPIs or thresholds based on the previous information. We can do data science, machine learning methods or just clever queries. You can view all this online. If a KPI hits a redline, you can send alerts, which is the solution's most functional feature.

Read full review

Quotes from Members

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

Pros

"The setup was not too difficult."

"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."

"Another feature is how Flink handles its radiuses. It has something called the checkpointing concept. You're dealing with billions and billions of requests, so your system is going to fail in large storage systems. Flink handles this by using the concept of checkpointing and savepointing, where they write the aggregated state into some separate storage. So in case of failure, you can basically recall from that state and come back."

"The ease of usage, even for complex tasks, stands out."

"It is user-friendly and the reporting is good."

"This is truly a real-time solution."

"Apache Flink allows you to reduce latency and process data in real-time, making it ideal for such scenarios."

"The event processing function is the most useful or the most used function. The filter function and the mapping function are also very useful because we have a lot of data to transform. For example, we store a lot of information about a person, and when we want to retrieve this person's details, we need all the details. In the map function, we can actually map all persons based on their age group. That's why the mapping function is very useful. We can really get a lot of events, and then we keep on doing what we need to do."

More Apache Flink pros

"The ability for StreamBase to combine with Spotfire is its most valuable aspect. The ability to handle data in motion and the entirety of data at the same time is very good. If you don't integrate the two together it's just a monitoring tool, but if they're combined together it becomes a powerful analytical tool."

Cons

"There is room for improvement in the initial setup process."

"There is a learning curve. It takes time to learn."

"The TimeWindow feature is a bit tricky. The timing of the content and the windowing is a bit changed in 1.11. They have introduced watermarks. A watermark is basically associating every data with a timestamp. The timestamp could be anything, and we can provide the timestamp. So, whenever I receive a tweet, I can actually assign a timestamp, like what time did I get that tweet. The watermark helps us to uniquely identify the data. Watermarks are tricky if you use multiple events in the pipeline. For example, you have three resources from different locations, and you want to combine all those inputs and also perform some kind of logic. When you have more than one input screen and you want to collect all the information together, you have to apply TimeWindow all. That means that all the events from the upstream or from the up sources should be in that TimeWindow, and they were coming back. Internally, it is a batch of events that may be getting collected every five minutes or whatever timing is given. Sometimes, the use case for TimeWindow is a bit tricky. It depends on the application as well as on how people have given this TimeWindow. This kind of documentation is not updated. Even the test case documentation is a bit wrong. It doesn't work. Flink has updated the version of Apache Flink, but they have not updated the testing documentation. Therefore, I have to manually understand it. We have also been exploring failure handling. I was looking into changelogs for which they have posted the future plans and what are they going to deliver. We have two concerns regarding this, which have been noted down. I hope in the future that they will provide this functionality. Integration of Apache Flink with other metric services or failure handling data tools needs some kind of update or its in-depth knowledge is required in the documentation. We have a use case where we want to actually analyze or get analytics about how much data we process and how many failures we have. For that, we need to use Tomcat, which is an analytics tool for implementing counters. We can manage reports in the analyzer. This kind of integration is pretty much straightforward. They say that people must be well familiar with all the things before using this type of integration. They have given this complete file, which you can update, but it took some time. There is a learning curve with it, which consumed a lot of time. It is evolving to a newer version, but the documentation is not demonstrating that update. The documentation is not well incorporated. Hopefully, these things will get resolved now that they are implementing it. Failure is another area where it is a bit rigid or not that flexible. We never use this for scaling because complexity is very high in case of a failure. Processing and providing the scaled data back to Apache Flink is a bit challenging. They have this concept of offsetting, which could be simplified."

"Apache Flink should improve its data capability and data migration."

"The technical support from Apache is not good; support needs to be improved. I would rate them from one to ten as not good."

"PyFlink is not as fully featured as Python itself, so there are some limitations to what you can do with it."

"In terms of stability with Flink, it is something that you have to deal with every time. Stability is the number one problem that we have seen with Flink, and it really depends on the kind of problem that you're trying to solve."

"We have a machine learning team that works with Python, but Apache Flink does not have full support for the language."

More Apache Flink cons

"The solution should be more user-friendly for developers. Right now, you need a strong programmer to tune the solution. It's complicated product. I'm not sure if it can be done via self-service with BI. Because I'm from BI, I want more simplicity."

Pricing and Cost Advice

"Apache Flink is open source so we pay no licensing for the use of the software."

"It's an open source."

"This is an open-source platform that can be used free of charge."

"The solution is open-source, which is free."

"It's an open-source solution."

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

881,707 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

20%

Retailer

12%

Computer Software Company

10%

Manufacturing Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	12

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. ...

See all answers

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?

Apache could improve Apache Flink by providing more functionality, as they need to fully support data integration. The connectors are still very few for Apache Flink. There is a lack of functionali...

See all answers

Ask a question

Earn 20 points

Comparisons

Spring Cloud Data Flow vs Apache Flink

Compared 14% of the time

Databricks vs Apache Flink

Compared 12% of the time

Confluent vs Apache Flink

Compared 10% of the time

Amazon Kinesis vs Apache Flink

Compared 9% of the time

Qlik Talend Cloud vs Apache Flink

Compared 3% of the time

More Apache Flink Competitors

Software AG Apama vs TIBCO Streaming

Compared 25% of the time

Apache Kafka vs TIBCO Streaming

Compared 19% of the time

IBM Operational Decision Manager vs TIBCO Streaming

Compared 14% of the time

Starburst Galaxy vs TIBCO Streaming

Compared 13% of the time

More TIBCO Streaming Competitors

Product Reports

Buyer's Guide

Apache Flink

February 2026

Download Apache Flink product report

Buyer's Guide

Streaming Analytics

January 2026

Download TIBCO Streaming product report

Also Known As

Flink

TIBCO Streambase CEP

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.”

Apache

TIBCO Streaming enables real-time data processing and complex event handling with exceptional throughput, facilitating immediate insights and actions. It's designed to help businesses process high-speed data for rapid decision-making and enhanced operational efficiency.

TIBCO Streaming is a real-time analytics platform designed for the fast-paced demands of modern businesses, allowing enterprises to manage streams of data effortlessly. By providing a flexible and scalable environment, it specializes in data ingestion, analysis, and response, transforming data streams into actionable insights. Its architecture supports continuous querying and real-time pattern detection, ensuring that businesses can respond promptly to emerging opportunities or threats.

What are the key features of TIBCO Streaming?

Real-time Processing: Handles massive data streams with minimal latency.
Complex Event Processing: Identifies and reacts to patterns in streaming data.
Scalability: Easily adapts to fluctuating data volumes.
Integration: Seamlessly connects with cloud and on-premise systems.
Visual Authoring: Simplifies the creation of streaming applications.

What benefits and ROI can be expected?

Increased Responsiveness: Faster decision-making reduces downtime and improves service delivery.
Operational Efficiency: Automates data processing workflows, saving time and resources.
Cost Reduction: Minimizes infrastructure overhead through efficient resource utilization.
Improved Compliance: Enhances data governance capabilities with real-time monitoring.

In finance, TIBCO Streaming aids in the detection of fraudulent transactions by processing live data streams to spot anomalies. In telecommunications, it improves network performance monitoring by generating alerts from live data feeds, optimizing service quality for users. Its adaptability ensures it meets industry-specific demands, enhancing business outcomes across diverse sectors.

TIBCO

Sample Customers

LogRhythm, Inc., Inter-American Development Bank, Scientific Technologies Corporation, LotLinx, Inc., Benevity, Inc.

Blendtec, Brembo, CargoSmart, Hunt Oil, Autodata, Bank of Montreal

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Apache and others in Streaming Analytics. Updated: January 2026.

DOWNLOAD NOW

881,707 professionals have used our research since 2012.

See our list of best Streaming Analytics vendors.

We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.