Apache Kafka on Confluent Cloud vs Apache Spark Streaming comparison

Apache Kafka on Confluent Cloud vs. Apache Spark Streaming

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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 Kafka on Confluent C...

Ranking in Streaming Analytics

10th

Average Rating

8.4

Reviews Sentiment

5.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

11th

Average Rating

8.0

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Apache Kafka on Confluent Cloud is 0.0%. The mindshare of Apache Spark Streaming is 3.1%, down from 3.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Ritik Varshney

Scholar Trainee/Project Engineer at Wipro Limited

Enhanced data streaming with reliable features and good analytics

Apache Kafka on Confluent Cloud provides an enhanced level of reliability and resources compared to Apache Kafka alone. It offers more features which are beneficial for our clients, including cluster linking, schema registry, error handling, and dead-letter queues. It significantly improves customer and publisher satisfaction, especially with topic integration and data streaming.

Read full review

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.

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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

"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 ship-to-shore and various Azure integrations. Our findings revealed that Confluent Kafka performed exceptionally well, standing out alongside Genesys and Azure Event Hubs. While these three are top contenders, the choice among other tools depends on the specific use case and project requirements. The customer initially used tools like SMQs, FITRA, and Stream for real-time data processing. However, after our recommendation, Confluent Cloud proved to be a superior choice, capable of replacing these three tools and simplifying their data infrastructure. This shift to a single tool, Confluent Cloud, streamlined their operations, making maintenance and management more efficient for their internal projects."

"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."

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

"Overall, I think it's a good experience. Apache Kafka can be quite complex and difficult to maintain on your own, so using Apache Kafka on Confluent Cloud makes it much easier to use it without worrying about setup and maintenance."

"Confluent Cloud handles data volume pretty well."

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

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

"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

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

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

"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."

"The solution is very stable and reliable."

"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."

"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."

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

More Apache Spark Streaming pros

Cons

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

"There's one thing that's a common use case, but I don't know why it's not covered in Kafka. When a message comes in, and another message with the same key arrives, the first version should be deleted automatically."

"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."

"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."

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

More Apache Kafka on Confluent Cloud cons

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

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

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

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

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

"The solution itself could be easier to use."

"The debugging aspect could use some improvement."

"The initial setup is quite complex."

More Apache Spark Streaming cons

Pricing and Cost Advice

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

"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 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."

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

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

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Top Industries

By visitors reading reviews

Financial Services Firm

14%

Manufacturing Company

Computer Software Company

Government

Computer Software Company

22%

Financial Services Firm

21%

Manufacturing Company

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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...

What needs improvement with Apache Kafka on Confluent Cloud?

I think what I would improve about the solution is the cost, mostly. From my standpoint, it's the cost. From an engineering perspective, it works really well. There's always room for improvement. O...

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

We find that the best features include using the CDC functionality with the connector to take the data from our SQL database and publish it to many consumers. Any changes enable us to easily publis...

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.

What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

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Product Reports

Apache Kafka on Confluent Cloud

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Streaming Analytics

Download Apache Spark Streaming product report

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.

Apache Kafka on Confluent Cloud vs. Apache Spark Streaming