Apache Spark Streaming vs Confluent comparison

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Apache Spark Streaming

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Executive SummaryUpdated on Dec 17, 2024

Confluent and Apache Spark Streaming are competing products in the realm of real-time data processing. Confluent seems to have the upper hand in terms of commercial support and integration capabilities, while Apache Spark Streaming has an edge with its robust processing power and versatility.

Features: Confluent provides a sturdy distributed streaming platform with features like Kafka Connect and KSQL for seamless real-time SQL querying. Its integration capabilities enhance data management significantly. Apache Spark Streaming, on the other hand, offers extensive processing capabilities that include machine learning libraries and complex event processing ability, making it a potent tool for data analytics.

Room for Improvement: Confluent could benefit from more competitive pricing options and a simplified learning process for new users. Enhancing support for open-source contributions might also add value. Apache Spark Streaming could improve through better enterprise-level support and a more straightforward deployment process. Additional user-friendly interfaces would further enhance its accessibility.

Ease of Deployment and Customer Service: Confluent is celebrated for its streamlined deployment model and superior enterprise-level support, which simplifies implementation in complex environments. Conversely, Apache Spark Streaming offers openness and flexibility but comes with a steeper learning curve and requires significant self-management during deployment.

Pricing and ROI: Confluent generally incurs higher upfront costs, justified by its support and integration services, promising significant ROI by diminishing operational complexities. Apache Spark Streaming is more cost-effective but may entail additional expenses for custom configurations and setups, delivering substantial ROI through its powerful processing capabilities.

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

Apache Spark Streaming vs. Confluent

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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

8th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Confluent

Ranking in Streaming Analytics

7th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 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 Confluent is 6.3%, down from 8.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Confluent	6.3%
Apache Spark Streaming	4.6%
Other	89.1%

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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PavanManepalli

AVP - Sr Middleware Messaging Integration Engineer at Wells Fargo

Has supported streaming use cases across data centers and simplifies fraud analytics with SQL-based processing

I recommend that Confluent should improve its solution to keep up with competitors in the market, such as Solace and other upcoming tools such as NATS. Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools. They need to improve in that direction by not only reducing costs but also providing better solutions for the problems customers face to avoid frustrations, whether through future enhancement requests or ensuring product stability. The cost should be worked on, and they should provide better solutions for customers. Solutions should focus on hierarchical topics; if a customer has different types of data and sources, they should be able to send them to the same place for analytics. Currently, Confluent requires everything to send to the same topic, which becomes very large and makes running analytics difficult. The hierarchy of topics should be improved. This part is available in MQ and other products such as Solace, but it is missing in Confluent, leading many in capital markets and trading to switch to Solace. In terms of stability, it is not the stability itself that needs improvement but rather the delivery semantics. Other products offer exactly-once delivery out of the box, whereas Confluent states it will offer this but lacks the knobs or levers for tuning configurations effectively. Confluent has hundreds of configurations that application teams must understand, which creates a gap. Users are often unaware of what values to set for better performance or to achieve exactly-once semantics, making it difficult to navigate through them. Delivery semantics also need to be worked on.

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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

"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."

"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."

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

"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."

"For Apache Spark Streaming, the feature I appreciated most is that it provides live data delivery; additionally, it provides the capability to send a larger amount of data in parallel."

More Apache Spark Streaming pros

"A person with a good IT background and HTML will not have any trouble with Confluent."

"One of the best features of Confluent is that it's very easy to search and have a live status with Jira."

"I would rate the scalability of the solution at eight out of ten. We have 20 people who use Confluent in our organization now, and we hope to increase usage in the future."

"The documentation process is fast with the tool."

"Their tech support is amazing; they are very good, both on and off-site."

"The most valuable is its capability to enhance the documentation process, particularly when creating software documentation."

"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."

"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."

More Confluent pros

Cons

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

"We don't have enough experience to be judgmental about its flaws."

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

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

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

"The debugging aspect could use some improvement."

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

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

More Apache Spark Streaming cons

"Areas for improvement include implementing multi-storage support to differentiate between database stores based on data age and optimizing storage costs."

"The pricing was high for our needs. We should not have to pay for features we do not use."

"The pricing model should include the ability to pick features and be charged for them only."

"Recently, there has been a lot of buzz about Confluent charging high fees while not offering features that match those of other tools."

"The beginner who doesn't know how to work on HTML will struggle as when you create spaces in the Confluent, if you want to have some meeting notes or anything else, you need to know HTML and which HTML tags to include."

"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."

"It could have more themes. The themes in the version I'm using are very limited; they offer two to three themes."

"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."

More Confluent cons

Pricing and Cost Advice

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

"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."

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

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

"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."

"On a scale from one to ten, where one is low pricing and ten is high pricing, I would rate Confluent's pricing at five. I have not encountered any additional costs."

"Confluent has a yearly license, which is a bit high because it's on a per-user basis."

"Confluence's pricing is quite reasonable, with a cost of around $10 per user that decreases as the number of users increases. Additionally, it's worth noting that for teams of up to 10 users, the solution is completely free."

"The solution is cheaper than other products."

"Confluent is an expensive solution as we went for a three contract and it was very costly for us."

"It comes with a high cost."

"Confluent is highly priced."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Outsourcing Company

13%

Comms Service Provider

Healthcare Company

Financial Services Firm

14%

Retailer

12%

Manufacturing Company

Computer Software 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	6
Midsize Enterprise	4
Large Enterprise	17

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...

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 ...

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...

What is your experience regarding pricing and costs for Confluent?

They charge a lot for scaling, which makes it expensive.

What needs improvement with Confluent?

What is your primary use case for Confluent?

The main use cases for Confluent are log aggregation and streaming. I'm familiar with Confluent stream processing with KSQL. KSQL helps in terms of data analytics strategies because if we are the d...

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Product Reports

Apache Spark Streaming

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Also Known As

Spark Streaming

No data available

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

Confluent offers scalable, open-source flexibility and seamless data replication, supported by strong cloud integration. Key features like Kafka Connect and real-time processing make it valuable for data streaming projects while ensuring high availability with a Multi-Region Cluster.

Confluent is a robust data streaming platform that enables efficient management and integration of real-time data pipelines. Its message-driven architecture and fault tolerance provide reliability, while a user-friendly dashboard and connectors support diverse data sources. Cloud integration reduces costs, and extensive documentation, plugins, and monitoring capabilities enhance collaboration and revision management. Despite some areas needing improvement, including security in the SaaS version and integration flexibility, Confluent remains a staple in industries requiring vast data processing and task automation.

What are Confluent's key features?

Scalability: Handles large volumes of data, ensuring continuous performance.
Kafka Connect: Enables seamless data integration across multiple systems.
Real-time Processing: Facilitates instant data insights for rapid decision-making.
Multi-Region Cluster: Maintains data availability across different geographical locations.
Cloud Integration: Reduces operational costs and enhances deployment flexibility.
User Dashboard: Simplifies management with an intuitive interface.

What benefits and ROI should users consider in reviews?

Cost-Efficiency: Improved processes reduce operational expenses.
Data Reliability: Ensures consistent and accurate data handling.
Increased Collaboration: Boosts team efficiency with comprehensive documentation and management tools.
Scalability: Supports business growth without compromising performance.
Integration Flexibility: Seamlessly connects with diverse data sources and systems.

Confluent is commonly implemented in finance, insurance, and software industries for applications like fraud detection, ETL tasks, and enterprise communication. It supports real-time data processing, project management, and task automation, often integrating with project management tools like Jira, providing valuable solutions for business processes.

Sample Customers

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

ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost

Apache Spark Streaming vs. Confluent