Apache Kafka on Confluent Cloud vs Apache Spark Streaming comparison

Confluent and Apache are both solutions in the Streaming Analytics category. Confluent is ranked #11 with an average rating of 8.5, while Apache is ranked #7 with an average rating of 7.9. Confluent holds a 0.1% mindshare in SA, compared to Apache’s 3.6% mindshare. Additionally, 100% of Confluent users are willing to recommend the solution, compared to 94% of Apache users who would recommend it.

Apache Kafka on Confluent C...

Read 14 Apache Kafka on Confluent Cloud reviews

1,042 Views
10 Comparison Views

100% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

1,291 Views
1,291 Comparison Views

94% willing to recommend

Apache Kafka on Confluent C...

Apache Spark Streaming

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Executive Summary

We performed a comparison between Apache Kafka on Confluent Cloud and Apache Spark Streaming based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Apache Kafka on Confluent Cloud vs. Apache Spark Streaming Report (Updated: September 2025).

Buyer's Guide

Apache Kafka on Confluent Cloud vs. Apache Spark Streaming

September 2025

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Helped 869,202 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 Kafka on Confluent C...

Ranking in Streaming Analytics

11th

Average Rating

8.6

Reviews Sentiment

3.7

Number of Reviews

Ranking in other categories

No ranking in other categories

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

Mindshare comparison

As of October 2025, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.1%, up from 0.0% compared to the previous year. The mindshare of Apache Spark Streaming is 3.6%, up from 3.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Apache Spark Streaming	3.6%
Apache Kafka on Confluent Cloud	0.1%
Other	96.3%

Streaming Analytics

Featured Reviews

FABIO LUIS VELLOSO DA SILVA

Partner at SouJava

Has enabled asynchronous communication and real-time data processing with strong performance

The valuable features with Apache Kafka on Confluent Cloud are the messaging and the asynchronous messages; it's the basic, not advanced usage. It's only to create clusters to receive and send messages. The point is the asynchronous messages and the scalability; it is important for us. To guarantee the compliance of the architecture and the patterns for the company, to provide scalability, and to guarantee the security to send the messages. The Kafka Streams API helps with real-time data transformations and aggregations. It's very fast and helps us to create the project, guarantee the message delivery, and the performance. It's a good experience with very impressive processing and a very impressive project and product.

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Himansu Jena

Sr Project Manager at Raj Subhatech

Efficient real-time data management and analysis with advanced features

There are various ways we can improve Apache Spark Streaming through best practices. The initial part requires attention to batch interval tuning, which helps small intervals in micro batches based on latency requirements and helps prevent back pressure. We can use data formats such as Parquet or ORC for storage that needs faster reads and leveraging feature predicate push-down optimizations. We can implement serialization which helps with any Kyro in terms of .NET or Java. We have boxing and unboxing serialization for XML and JSON for converting key-pair values stored in browser. We can also implement caching mechanisms for storing and recomputing multiple operations. We can use specified joins which help with smaller databases, and distributed joins can minimize users. We can implement project optimization memory for CPU efficiency, known as Tungsten. Additionally, load balancing, checkpointing, and schema evaluation are areas to consider based on performance and bottlenecks. We can use Bugzilla tools for tracking and Splunk to monitor the performance of process systems, utilization, and performance based on data frames or data sets.

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 state-saving feature is very much appreciated. It allows me to rewind a certain process if I see an error and then reprocess it."

"Confluent Cloud handles data volume pretty well."

"Some of the best features with Apache Kafka on Confluent Cloud are streaming and event capabilities, which are important due to scalability and resiliency."

"The product's installation phase is pretty straightforward for us since we know how to use it."

"Kafka provides handy properties that allow us to directly configure the data, whether to keep it or discard it after use."

"Apache Kafka on Confluent Cloud is more reliable and frequent to use compared to Apache Kafka."

"It's very fast and helps us to create the project, guarantee the message delivery, and the performance."

"In case of huge transactions on the web or mobile apps, it helps you capture real-time data and analyze it."

More Apache Kafka on Confluent Cloud pros

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

"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."

"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."

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

"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."

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

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

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

More Apache Spark Streaming pros

Cons

"Regarding real-time data usage, there were challenges with CDC (Change Data Capture) integrations. Specifically, with PyTRAN, we encountered difficulties. We recommended using our on-premises Kaspersky as an alternative to PyTRAN for that specific use case due to issues with CDC store configuration and log reading challenges with the iton components."

"There are some premium connectors, for example, available in Confluent, which you cannot access in the marketplace, so there are some limitations."

"Maybe in terms of Apache Kafka's integration with other Microsoft tools, our company faced some challenges."

"The clustering is a little hard for juniors and clients. It's suitable for senior engineers, but the configuration and clustering are very hard for juniors."

"The solution is expensive."

"There could be an in-built feature for data analysis."

"In terms of improvements, observability and monitoring are areas that could be enhanced. They are lacking in terms of observability and monitoring compared to other products."

"Some areas for improvement in Apache Kafka on Confluent Cloud include issues faced during migration with Kubernetes pods."

More Apache Kafka on Confluent Cloud cons

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

"The initial setup is quite complex."

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

"Monitoring is an area where they could definitely improve Apache Spark Streaming. When you have a streaming application, it generates numerous logs. After some time, the logs become meaningless because they're quite large and impossible to open."

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

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

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

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

More Apache Spark Streaming cons

Pricing and Cost Advice

"Regarding pricing, Apache Kafka on Confluent Cloud is not a cheap tool. The right use case would justify the cost. It might make sense if you have a high volume of data that you can leverage to generate value for the business. But if you don't have those requirements, there are likely cheaper solutions you could use instead."

"I think the pricing is fair, but Confluent requires a little bit more thinking because the price can go up really quickly when it comes to premium connectors."

"I consider that the product's price falls under the middle range category."

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

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

13%

Manufacturing Company

Educational Organization

Government

Computer Software Company

23%

Financial Services Firm

20%

Healthcare Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	3
Large Enterprise	6

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

Questions from the Community

What do you like most about Apache Kafka on Confluent Cloud?

Kafka and Confluent Cloud have proven to be cost-effective, especially when compared to other tools. In a recent BI integration program over the past year, we assessed multiple use cases spanning s...

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What needs improvement with Apache Kafka on Confluent Cloud?

If it were easier to configure clusters and had more straightforward configuration, high-level API abstraction in the APIs could improve it. The clustering is a little hard for juniors and clients....

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What is your primary use case for Apache Kafka on Confluent Cloud?

We need to send a lot of asynchronous messages in this project, and we use the middleware and Apache Kafka on Confluent Cloud to guarantee asynchronous messaging between the services. We use Apache...

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What do you like most about Apache Spark Streaming?

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

See all answers

What needs improvement with Apache Spark Streaming?

I believe the downsides of Apache Spark Streaming are that it primarily supports structured data. Currently, in my organization, we require thousands of transcripts that need to be handled during l...

See all answers

What is your primary use case for Apache Spark Streaming?

My use cases for Apache Spark Streaming were during my academics. During that time, I used Apache Spark Streaming to transmit data live from one source to another.

See all answers

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Product Reports

Buyer's Guide

Apache Kafka on Confluent Cloud

October 2025

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Buyer's Guide

Apache Spark Streaming

October 2025

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Also Known As

No data available

Spark Streaming

Overview

Apache Kafka on Confluent Cloud provides real-time data streaming with seamless integration, enhanced scalability, and efficient data processing, recognized for its real-time architecture, ease of use, and reliable multi-cloud operations while effectively managing large data volumes.

Apache Kafka on Confluent Cloud is designed to handle large-scale data operations across different cloud environments. It supports real-time data streaming, crucial for applications in transaction processing, change data capture, microservices, and enterprise data movement. Users benefit from features like schema registry and error handling, which ensure efficient and reliable operations. While the platform offers extensive connector support and reduced maintenance, there are areas requiring improvement, including better data analysis features, PyTRAN CDC integration, and cost-effective access to premium connectors. Migrating with Kubernetes and managing message states are areas for development as well. Despite these challenges, it remains a robust option for organizations seeking to distribute data effectively for analytics and real-time systems across industries like retail and finance.

What are the key features of Apache Kafka on Confluent Cloud?

Seamless Integration: Connects with many data sources seamlessly.
Enhanced Scalability: Easily scales to manage increasing data loads.
Schema Registry: Maintains compatibility and evolution of schemas.
Offset Management: Tracks consumer progress efficiently.
Error Handling: Offers mechanisms to manage inaccuracies effectively.
Connector Support: Extensive connectors for diverse third-party tools.

What benefits should users seek in reviews?

Reliability: Consistent performance in multi-cloud environments.
Ease of Use: Simplified operations and maintenance.
Developer Efficiency: Streamlined features that save developers time.
Data Volume Handling: Capable of managing large-scale data streaming.

In industries like retail and finance, Apache Kafka on Confluent Cloud is implemented to manage real-time location tracking, event-driven systems, and enterprise-level data distribution. It aids in operations that require robust data streaming, such as CDC, log processing, and analytics data distribution, providing a significant edge in data management and operational efficiency.

Confluent

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

Apache

Sample Customers

Information Not Available

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

Buyer's Guide

Apache Kafka on Confluent Cloud vs. Apache Spark Streaming

September 2025

Free Report: Apache Kafka on Confluent Cloud vs. Apache Spark Streaming

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