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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Henrique Fsantos

Data & Analytics Engineer at PicPay

Automated data pipelines have improved analytics workflows and provide trusted reporting data

The best features of Qlik Talend Cloud are its connectors to many systems, databases, APIs, and files, including both SQL and NoSQL databases. The scheduling feature allows me to run jobs automatically in the cloud, and the built-in data quality tools help clean and check data. The reusable components enable me to reuse jobs and parts, which saves time, making it easy to build good data pipelines. The feature that has made the biggest difference for me in Qlik Talend Cloud is the scheduling and automation, which helps me run ETL jobs automatically without manual work. This saves time, reduces errors, and makes the pipelines more stable. In my daily work, I can trust that the data is updated on time and ready for reports and analytics. Qlik Talend Cloud has had a positive impact on my organization by automating data integration processes, reducing manual errors, and ensuring reliable and up-to-date data for reports and business decisions. This has improved operational stability, productivity, and trust in the data. Since using Qlik Talend Cloud, we have reduced manual work, resulting in approximately 20% to 30% time savings in data load processes. We have also seen a reduction in operational errors, mainly due to automation and the data quality checks, and a clear improvement in data reliability since the pipelines became more standardized and monitored.

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Quotes from Members

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

Pros

"It is the most scalable tool that I have seen before."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"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."

"By integrating Apache Spark Streaming, the data freshness rate, and latency have significantly improved from 24-hour batch processing to less than one minute, facilitating faster communication to downstream systems, aiding marketing campaigns."

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

More Apache Spark Streaming pros

"The solution enables robust data matching, merging, survivorship, and Data Stewardship that can be a part of data quality workflows or true master data management."

"Before, when we wanted solutions and REST APIs, we handed it over to an external company, so it took time, back and forth and all that, but now, with Qlik Talend Cloud, when we want a service, we create it, and in at most one or two days the service is in production and usable, so we save a lot of time."

"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."

"This product speeds up the unit testing and QA for specific test scenarios. As a result, the development output quality can be evaluated and adjusted."

"We are still in the process of evaluating them, but I love the way things look in Talend, and I'm all for Talend."

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

"Qlik Talend Cloud has had a positive impact on my organization by automating data integration processes, reducing manual errors, and ensuring reliable and up-to-date data for reports and business decisions."

"Talend has allowed us to systematically organise/structure and deploy our staged ETL transformation processes from Development into production, we have tracked our data quality efforts during our runs and supplied comprehensive feedback during our development."

More Qlik Talend Cloud pros

Cons

"The solution itself could be easier to use."

"We would like to have the ability to do arbitrary stateful functions in Python."

"The initial setup is quite complex."

"When dealing with various data types including COBOL, Excel, JSON, video, audio, and MPG files, challenges can arise with incomplete or missing values."

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

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

"Integrating event-level streaming capabilities could be beneficial."

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

More Apache Spark Streaming cons

"The tool's technical support needs to be better. It doesn't have a local data center but pushes everything to the cloud. They need to check in with customers to see if they're happy and how well the solutions work. They need to assign a customer success manager for the accounts they sell."

"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."

"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."

"The ability to change the code when debugging the JavaScript could be improved."

"Finding assistance with issues can be spotty. With Python, there are literally millions of open source answers which are recent and apply to the version that we are using."

"The performance is one area that Talend Data Quality could improve in because large volumes take a lot of time."

"Processing large volumes of data sometimes consumes a lot of resources."

"Not enough material is available for beginners."

More Qlik Talend Cloud cons

Pricing and Cost Advice

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

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

"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."

"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."

"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."

"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."

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

"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."

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

Marketing Services Firm

Financial Services Firm

14%

Outsourcing Company

12%

Comms Service Provider

11%

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...

See all answers

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

October 2026

Download Qlik Talend Cloud product report

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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