Apache Flink vs Qlik Talend Cloud comparison

Apache and Qlik are both solutions in the Streaming Analytics category. Apache is ranked #4 with an average rating of 8.0, while Qlik is ranked #6 with an average rating of 7.8. Apache holds a 7.5% mindshare in SA, compared to Qlik’s 3.2% mindshare. Additionally, 95% of Apache users are willing to recommend the solution, compared to 89% of Qlik users who would recommend it.

Apache Flink

Read 19 Apache Flink reviews

4,917 Views
4,917 Comparison Views

95% willing to recommend

Qlik Talend Cloud

Read 56 Qlik Talend Cloud reviews

16,429 Views
1,479 Comparison Views

89% willing to recommend

Apache Flink

Qlik Talend Cloud

Comparison Buyer's Guide

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Executive SummaryUpdated on Feb 22, 2026

Qlik Talend Cloud and Apache Flink are two prominent players in the data integration and real-time analytics market. While Qlik Talend Cloud is noted for its wide array of connectors and customization options, Apache Flink has the upper hand with its advanced real-time data handling capabilities and low-latency processing.

Features: Qlik Talend Cloud provides real-time data integration, robust code customization options, and a comprehensive set of connectors. Its monitoring capabilities allow for advanced user interaction. Apache Flink offers stateful processing, inbuilt checkpointing, and supports precise data ordering and aggregation, excelling in complex streaming data scenarios.

Room for Improvement: Qlik Talend Cloud has stability issues, high memory usage, frequent system crashes, and users desire better technical support and documentation. Apache Flink struggles with limited Python integration, complex infrastructure setup, and could benefit from more robust machine learning libraries and improved documentation.

Ease of Deployment and Customer Service: Qlik Talend Cloud offers versatile deployment options across various environments with strong customer support, although deployment poses challenges. Apache Flink, being open source, offers flexibility in public and hybrid cloud environments with satisfactory support, but requires careful planning with greater guidance needed for deployment and scaling.

Pricing and ROI: Qlik Talend Cloud operates on a subscription model, proving cost-effective and is noted for rapid positive ROI. Apache Flink, as an open-source solution, removes licensing costs but can lead to expensive setups with complex configurations, providing strong returns in operational efficiency.

To learn more, read our detailed Apache Flink vs. Qlik Talend Cloud Report (Updated: June 2026).

Buyer's Guide

Apache Flink vs. Qlik Talend Cloud

June 2026

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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:

Categories and Ranking

Apache Flink

Ranking in Streaming Analytics

4th

Average Rating

7.8

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Qlik Talend Cloud

Ranking in Streaming Analytics

6th

Average Rating

8.0

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

Data Integration (7th), Data Quality (2nd), Data Scrubbing Software (1st), Master Data Management (MDM) Software (3rd), Cloud Data Integration (6th), Data Governance (9th), Cloud Master Data Management (MDM) (3rd), Integration Platform as a Service (iPaaS) (6th)

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Apache Flink is 7.5%, down from 14.4% compared to the previous year. The mindshare of Qlik Talend Cloud is 3.2%, up from 1.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Flink	7.5%
Qlik Talend Cloud	3.2%
Other	89.3%

Streaming Analytics

Featured Reviews

Sanjay Srivastava

Software Architect at IBM

Streaming workflows have improved data integration and support real-time pipelines across platforms

We are not using Apache Flink in its advanced window capabilities. We are using the Apache Flink job in Apache SeaTunnel, meaning we can write the code inside Apache SeaTunnel. Currently, we are moving; both solutions are there. We are doing it on-premises with the help of Kubernetes and OpenShift. The main reason why Apache Flink is better is that it has more functions, and being open source with easy code in Apache SeaTunnel helps us achieve that. Cost is a major issue. I would rate the stability of the product as an eight. For Apache Flink, the final point can be rated an eight. I can recommend Apache Flink to other users for streaming support, and I am recommending it. I would rate this review an eight overall.

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Herivao Jus Winny

IT Consultant at a tech services company with 201-500 employees

Has automated recurring data flows and improved accuracy in reporting

The best features of Talend Data Integration are its rich set of components that let you connect to almost any data design intuitive and its strong automation and scheduling capabilities. The TMap component is especially valuable because it allows flexible transformation, joins, and filtering in a single place. I also rely a lot on context variables to manage different environments like Dev, Test, and production, without changing the code. The error handling and logging tools are very helpful for monitoring and troubleshooting, which makes the workflow more reliable. Talend Data Integration has helped our company by automating and standardizing data processes. Before, many of these tasks were done manually, which took more time and often led to errors. With Talend Data Integration, we built automated pipelines that extract, clean, and load data consistently. This not only saves hours of manual effort, but also improves the accuracy and reliability of data. As a result, business teams had faster access to trustworthy information for reporting and decision making, which directly improved efficiency and productivity. Talend Data Integration has had a measurable impact on our organization. By automating daily data loading processes, we reduced manual effort by around three or four hours per day, which saved roughly 60 to 80 hours per month. We also improved data accuracy. Error rates dropped by more than 70% because validation rules were built into the jobs. In addition, reporting teams now receive fresh data at least 50% faster, which means they can make decisions earlier and with more confidence. Overall, Talend Data Integration has increased both efficiency and reliability in our data workflows.

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 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."

"Easy to deploy and manage."

"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 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."

"Allows us to process batch data, stream to real-time and build pipelines."

"Apache Flink is meant for low latency applications. You take one event opposite if you want to maintain a certain state. When another event comes and you want to associate those events together, in-memory state management was a key feature for us."

"We value this solution's intricate system because it comes with a state inside the mechanism and product, allowing us to process batch data, stream to real-time and build pipelines, and we do not need to process data from the beginning when we pause as we can continue from the same point where we stopped, helping us save time as 95% of our pipelines will now be on Amazon and we'll save money by saving time."

"This is truly a real-time solution."

More Apache Flink pros

"Talend Data Integration has had a measurable impact on our organization by automating daily data loading processes, reducing manual effort by around three or four hours per day, improving data accuracy with error rates dropping by more than 70%, and enabling reporting teams to receive fresh data at least 50% faster for earlier and more confident decision-making."

"It has definitely streamlined certain processes."

"It offers advanced features that allow you to create custom patterns and use regular expressions to identify data issues."

"Better control and flexibility to add/custom define features, to tailor to your needs by modifying its Java generated code."

"We’re also able to respond much more quickly to changes and demands from the business, as we can create and change jobs quickly and provide new data for reports within hours."

"We can develop our own code if we do not see the functionality we need."

"Talend Data Integration has positively impacted my organization because it is used for master management data, which I think most other components lack, but Talend Data Integration has that capability for using master management data as standard data."

"The most valuable feature is integration."

More Qlik Talend Cloud pros

Cons

"The solution could be more user-friendly."

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

"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."

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

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

"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."

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

"I am using the Python API and I have found the solution to be underdeveloped compared to others. There needs to be better integration with notebooks to allow for more practical development."

More Apache Flink cons

"I encountered scalability issues."

"NullPointerExceptions are going to be the death of me and are a big reason for our transition away from Talend. One day, it is fine with a 1000 blank rows, then the next day, it will find one blank cell and it breaks down."

"Lots of errors and system crashes during installation and IMHO we didn’t receive good enough technical support and guidance from Talend to solve the issues."

"I would like to sync a project and do an upload from that current version, and then from GitLab, be able to download the latest one."

"First, We faced problems with stability of the products."

"Qlik Talend Cloud could be improved with more advanced monitoring and flexible alerts, as well as better job performance visibility."

"Sometimes there are bugs which are unidentified and we have to follow-up with the Talend team to resolve them. In a critical situation, it takes time for them to update patches."

"The documentation from version to version could be more accurate."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"It's an open source."

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

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

"It's an open-source solution."

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

"We did not purchase a separate license for DQ. It is part of our data platform suite, and I believe it is well-priced."

"The price is on a per-user basis. It's a little more expensive than other tools. There aren't any additional costs beyond the standard licensing fee."

"The licensing cost is about 40,000 Euros a year."

"The tool is cheap."

"Moreover, the pricing structure stands out as highly competitive compared to other offerings in the market, making it a cost-effective choice for users."

"The price of the Talend Data Management Platform is reasonable. The other competing solutions are priced high. Gartner Magic Quadrant identified other solutions, such as Informatica, that are far more expensive."

"The product pricing is considered very good, especially compared to other data integration tools in the market."

"The pricing is a little higher than what I had expected, but it's comparable with I-PASS competitors."

More Qlik Talend Cloud pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Retailer

13%

Computer Software Company

Manufacturing Company

Financial Services Firm

15%

Comms Service Provider

Construction Company

Outsourcing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	21
Midsize Enterprise	12
Large Enterprise	20

Questions from the Community

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

What is your primary use case for Apache Flink?

I am working with Apache Flink, which is the tool we use for data integration. Apache Flink is for data, and we are working on the data integration project, not big data, using Apache Flink and Apa...

See all answers

What advice do you have for others considering Apache Flink?

See all answers

What needs improvement with Talend Data Quality?

I don't use the automated rule management feature in Talend Data Quality that much, so I cannot provide much feedback. I may not know what Talend Data Quality can improve for data quality. I'm not ...

See all answers

What is your primary use case for Talend Data Quality?

It is for consistency, mainly; data consistency and data quality are our main use cases for the product. Data consistency is the primary purpose we use it for, as we have written rules in Talend Da...

See all answers

What advice do you have for others considering Talend Data Quality?

Currently, I'm working with batch jobs and don't perform real-time data quality monitoring because of the large data volume. For real-time, we use a different product. I cannot provide details abou...

See all answers

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Product Reports

Buyer's Guide

Apache Flink

August 2026

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Buyer's Guide

Qlik Talend Cloud

July 2026

Download Qlik Talend Cloud product report

Also Known As

Flink

Talend Data Quality, Talend Data Management Platform, Talend MDM Platform, Talend Data Streams, Talend Data Integration, Talend Data Integrity and Data Governance

Overview

Apache Flink is a powerful open-source framework for stateful computations over data streams, designed for both real-time and batch processing. It efficiently handles massive volumes of data with low-latency responses, offering versatility for complex event processing scenarios.

Apache Flink excels in processing high-throughput data streams, enabling seamless state management across distributed applications. Users appreciate its robust features like stateful transformations and checkpointing, simplifying deployment in diverse environments. Though powerful, it poses challenges for beginners due to its complexity and limited documentation, requiring some prior experience to master. Its flexible integration with systems like Kafka and support for Kubernetes on AWS makes it suitable for demanding environments where quick, real-time analysis is essential.

What are the key features of Apache Flink?

Stateful Transformations: Allows complex stateful operations on data streams with precise handling.
Low Latency: Ensures real-time data processing with minimal delays.
Checkpointing: Provides efficient and reliable checkpointing for fault tolerance.
Kafka Integration: Easy integration with Kafka for seamless data ingestion and processing.
API Support: Provides robust APIs for diverse data processing needs.
Flexible Deployment: Offers options for deploying on-premise or in cloud environments.

What benefits should users look for?

Versatility: Supports both batch and stream processing in a unified model.
Community Support: Backed by an active community that continuously enhances its features.
Ease of Use: Simplifies the coding process compared to similar frameworks like Apache Storm.
Real-Time Analytics: Facilitates immediate insights and data-driven decision-making.

Organizations leverage Apache Flink primarily for real-time data processing in sectors such as retail, transportation, and telecommunications. By deploying on AWS with Kubernetes, companies can utilize it for data cleaning, generating customer insights, and providing swift real-time updates. It effectively manages millions of events per second, serving use cases like cab aggregations, map-making, and outlier detection in telecom networks, enabling seamless integration of streaming data with existing pipelines.

Apache

Qlik Talend Cloud provides robust data integration tools tailored for efficient management of large volumes, offering real-time data access, Java integration, and custom code capabilities for developers.

Qlik Talend Cloud is known for its extensive connectivity options, enabling seamless integration across different platforms, such as S3, Redshift, Oracle, and SQL Server. The central repository facilitates consistent metadata access throughout organizations, enhancing collaboration. Despite its strengths in advanced monitoring, automation, and user-friendly drag-and-drop interfaces, users face challenges with installation stability, technical support, documentation inconsistencies, and complexities in learning. Performance concerns also include multitasking limitations and excessive memory usage. The platform's licensing costs can be prohibitive for smaller companies, while demands for improved data governance and intuitive code management continue. Its applications in healthcare data parsing, ETL task automation, and diverse data platform integration demonstrate its utility, although there's a constant demand for better scalability and efficient transformations.

What are the key features?

Seamless Real-Time Data Access: Ensures that users can access the latest information across systems without delays for better decision-making.
Java Integration: Facilitates smooth incorporation of Java components into workflows, broadening development options.
Custom Code Development: Offers flexibility to develop tailored solutions to meet specific integration scenarios.
Vast Connectors: Enhances connectivity between multiple platforms, supporting comprehensive data integration strategies.
Central Repository: Allows for uniform metadata access across departments, promoting cohesive data management practices.
Automation and Monitoring: Streamlines workflows with advanced automation and monitoring, reducing manual intervention and errors.

What benefits and ROI should users evaluate?

Improved Data Quality: Tools for data matching and error handling ensure high-quality, reliable data for analytics and reporting.
Efficient Workflow Streamlining: Drag-and-drop interfaces and automation features simplify complex workflows, enhancing operational efficiency.
Enhanced Business Insights: By integrating diverse data sources, businesses can derive deeper insights and ensure compliance through better data management.
Cost Management: Although some users find licensing expensive, comprehensive feature sets aimed at improving data processes can justify costs.
Community Support: Users benefit from active community forums that provide assistance, although technical support also necessitates enhancements.

In specialized industries like healthcare, users leverage Qlik Talend Cloud for data integration and transformation, aiding in compliance and analytics. Compatibility with cloud and on-premises systems ensures adaptability to complex data tasks, facilitating business application development. Organizations focus on enhanced data ingestion and quality checks for comprehensive solutions.

Qlik

Sample Customers

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

Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group

Buyer's Guide

Apache Flink vs. Qlik Talend Cloud

June 2026

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