Apache Spark Streaming vs Qlik Talend Cloud comparison

Why PeerSpot?

Apache and Qlik are both solutions in the Streaming Analytics category. Apache is ranked #7 with an average rating of 7.9, while Qlik is ranked #6 with an average rating of 7.8. Apache holds a 4.6% mindshare in SA, compared to Qlik’s 3.4% mindshare. Additionally, 94% of Apache users are willing to recommend the solution, compared to 89% of Qlik users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

3,138 Views
3,138 Comparison Views

94% willing to recommend

Qlik Talend Cloud

Read 56 Qlik Talend Cloud reviews

19,499 Views
1,755 Comparison Views

89% willing to recommend

Apache Spark Streaming

Qlik Talend Cloud

Comparison Buyer's Guide

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Executive SummaryUpdated on Feb 22, 2026

Qlik Talend Cloud and Apache Spark Streaming are competing in the data integration and real-time analytics category. Apache Spark Streaming seems to have the upper hand due to its cost-effectiveness and robust real-time processing capabilities, which are well-suited for handling data streams.

Features: Qlik Talend Cloud provides a vast set of tools for data integration and real-time operations, supported by a central repository for metadata sharing. It offers a wide range of connectors that facilitate custom coding and integration with diverse data sources. Apache Spark Streaming, on the other hand, is known for its real-time analytics and low-latency processing, supporting multiple languages and offering seamless integration with other services for scalable operations.

Room for Improvement: Qlik Talend Cloud faces challenges with memory usage, installation difficulties, and high licensing costs, along with stability and performance inconsistencies. Apache Spark Streaming, while strong in real-time functionality, can improve its user interface and event handling capabilities, with the complexity of its initial setup being a notable concern.

Ease of Deployment and Customer Service: Qlik Talend Cloud supports deployment across on-premises, hybrid, and cloud environments, though its installation process is complex. Its customer service is generally responsive, although support experiences can vary based on system familiarity. Apache Spark Streaming adapts well to both cloud and on-premises deployments but is noted for the complexity of its setup. Customer support feedback is mixed, with some users desiring more technical guidance.

Pricing and ROI: Qlik Talend Cloud is subscription-based and perceived as costly by some users, yet it delivers significant ROI by automating processes. Apache Spark Streaming, as an open-source solution, is highly praised for its cost-effectiveness and flexibility, offering excellent ROI through its ease of use and efficiency, although some users might require additional support services.

To learn more, read our detailed Apache Spark Streaming vs. Qlik Talend Cloud Report (Updated: September 2026).

Buyer's Guide

Apache Spark Streaming vs. Qlik Talend Cloud

September 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 Spark Streaming

Ranking in Streaming Analytics

7th

Average Rating

7.8

Reviews Sentiment

6.4

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 (8th), Data Quality (2nd), Data Scrubbing Software (1st), Master Data Management (MDM) Software (2nd), Cloud Data Integration (7th), Data Governance (9th), Cloud Master Data Management (MDM) (3rd), Integration Platform as a Service (iPaaS) (7th)

Mindshare comparison

As of October 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 4.6%, up from 3.6% compared to the previous year. The mindshare of Qlik Talend Cloud is 3.4%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Qlik Talend Cloud	3.4%
Apache Spark Streaming	4.6%
Other	92.0%

Streaming Analytics

Featured Reviews

Khoa Dang Le

Principal AI Engineer at IMT Solutions

Have faced challenges with complex data handling and seek smoother integration for machine learning workflows

I find the fault tolerance feature beneficial because I use it for serving data from a landing area. I understand all of the structures we have for Spark SQL, Spark Streaming, and MLlib. The ability of Apache Spark Streaming to handle out-of-order data using watermarking and windowing is something we use in our pipeline. Nearly 50% of our usage is based on that because we use it for landing data, and we appreciate that we can work with it. The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage. The fast storage capability is crucial because Apache Spark replaces Hadoop's MapReduce, allowing us to manage our data more efficiently.

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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 solution is very stable and reliable."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"As an open-source solution, using it is basically free."

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"The solution is better than average and some of the valuable features include efficiency and stability."

"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."

"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."

More Apache Spark Streaming pros

"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."

"Flexibility is a key feature I appreciate about Talend Data Integration, especially the integration of Java within it and the ease of integrating with multiple source repositories such as GitHub and Bitbucket."

"It reduces the QA effort immensely by handling most of the test scenarios in a reusable way."

"The process of upgrading the software is quite easy."

"The Talend software is very simple to install."

"It meets our operational and analytical data integration needs by providing us with a high performing set of tools to access, transform and integrate data in real time."

"Qlik Talend Cloud positively impacted my organization by enabling businesses to accelerate data decisions very easily and reducing execution time."

"The best features Qlik Talend Cloud offers include the fact that it is built on Java, which gives me the chance to customize my requirements and write my own Java code to achieve my logic."

More Qlik Talend Cloud pros

Cons

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

"It was resource-intensive, even for small-scale applications."

"In terms of improvement, the UI could be better."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

"The initial setup is quite complex."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

More Apache Spark Streaming cons

"Due to using the open-source version of Talend Data Integration, which lacks a scheduler, our current approach involves developing jobs in Talend, exporting them as Java packages, and utilizing an external scheduler, such as Windows Scheduler, to manage the scheduling process."

"If we encounter issues, it’s most likely when using the Talend Open Studio. The studio can be slow, get stuck, or crash. But again, it can be caused by the resources of your machine or your connection with the repository. If we encounter issues with the Studio we restart the Studio. In emergencies, we create and use a new workspace."

"I'd be interested in seeing the running of Python programs and transformations from within the studio itself."

"Not enough material is available for beginners."

"SQL for displaying underlying data in non-match results does not work."

"In redundancy analysis, the query is failing to bring non-matched records. This query is an internal script. There is no way (that I know of) to fix this syntax error for future runs."

"There are no concurrent licenses, they only have seat licenses on cloud. That's the whole challenge. For example, if in any project your headcount increases or decreases, you do not have that concurrence and you have a seat license, you run into challenges because you have to procure a few more licenses for getting the job done."

"When we upgraded to Version 6.4.1, we tried using a GIT repository instead of a SVN repository. After a few incidents where things disappeared and changes were not saved, we decided to go back to a SVN repository."

More Qlik Talend Cloud cons

Pricing and Cost Advice

"People pay for Apache Spark Streaming as a service."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"I was using the open-source community version, which was self-hosted."

"Spark is an affordable solution, especially considering its open-source nature."

"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."

"I have been using the open-source version."

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

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

"License renewal is on a yearly basis."

"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 licensing cost for the Talend MDM Platform is paid yearly, but I'm unable to give you the figure. I would rate its price as four out of five because it's on the cheaper side. I'm not aware of any extra costs in addition to the standard licensing fees for the Talend MDM Platform."

"It is cheaper than Informatica. Talend Data Quality costs somewhere between $10,000 to $12,000 per year for a seat license. It would cost around $20,000 per year for a concurrent license. It is the same for the whole big data solution, which comes with Talend DI, Talend DQ, and TDM."

More Qlik Talend Cloud pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

14%

Comms Service Provider

Healthcare Company

Financial Services Firm

14%

Outsourcing Company

12%

Comms Service Provider

10%

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

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

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

See all answers

What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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 Spark Streaming

October 2026

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Buyer's Guide

Qlik Talend Cloud

September 2026

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Also Known As

Spark Streaming

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

Overview

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

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

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

Aliaxis, Electrocomponents, M¾NCHENER VEREIN, The Sunset Group

Buyer's Guide

Apache Spark Streaming vs. Qlik Talend Cloud

September 2026

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