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: December 2025).

Apache Spark Streaming vs. Confluent

December 2025

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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

5th

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.9%, up from 3.2% compared to the previous year. The mindshare of Confluent is 6.8%, down from 8.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Confluent	6.8%
Apache Spark Streaming	3.9%
Other	89.3%

Streaming Analytics

Featured Reviews

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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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

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

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

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

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

"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."

"It's the fastest solution on the market with low latency data on data transformations."

"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."

More Apache Spark Streaming pros

"Confluent facilitates the messaging tasks with Kafka, streamlining our processes effectively."

"The biggest benefit of Confluent as a tool is that it is a distributed platform that provides more durability and stability."

"The documentation process is fast with the tool."

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

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

"With Confluent Cloud we no longer need to handle the infrastructure and the plumbing, which is a concern for Confluent. The other advantage is that all portfolios have access to the data that is being shared."

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

"We mostly use the solution's message queues and event-driven architecture."

More Confluent pros

Cons

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

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

"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."

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

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

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

"The debugging aspect could use some improvement."

"The initial setup is quite complex."

More Apache Spark Streaming cons

"One area we've identified that could be improved is the governance and access control to the Kafka topics. We've found some limitations, like a threshold of 10,000 rules per cluster, that make it challenging to manage access at scale if we have many different data sources."

"It could have more integration with different platforms."

"Confluent's price needs improvement."

"there is room for improvement in the visualization."

"There is no local support team in Saudi Arabia."

"It could have more themes. They should also have more reporting-oriented plugins as well. It would be great to have free custom reports that can be dispatched directly from Jira."

"They should remove Zookeeper because of security issues."

"The product should integrate tools for incorporating diagrams like Lucidchart. It also needs to improve its formatting features. We also faced issues while granting permissions."

More Confluent cons

Pricing and Cost Advice

"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."

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

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

"The pricing model of Confluent could improve because if you have a classic use case where you're going to use all the features there is no plan to reduce the features. You should be able to pick and choose basic services at a reduced price. The pricing was high for our needs. We should not have to pay for features we do not use."

"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."

"The solution is cheaper than other products."

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

"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."

"It comes with a high cost."

"Confluent is an expensive solution."

"You have to pay additional for one or two features."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

22%

Financial Services Firm

21%

University

Healthcare Company

Financial Services Firm

17%

Computer Software Company

11%

Retailer

Manufacturing 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	16

Questions from the Community

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?

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 do you like most about Confluent?

I find Confluent's Kafka Connectors and Kafka Streams invaluable for my use cases because they simplify real-time data processing and ETL tasks by providing reliable, pre-packaged connectors and to...

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?

Azure Stream Analytics vs Apache Spark Streaming

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Compared 14% of the time

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Compared 9% of the time

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Compared 8% of the time

Databricks vs Confluent

Compared 6% of the time

Elastic Search vs Confluent

Compared 2% of the time

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Product Reports

Download Apache Spark Streaming product report

Apache Spark Streaming

January 2026

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January 2026

Also Known As

Spark Streaming

No data available

Overview

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

Apache

Confluent is an enterprise-ready, full-scale streaming platform that enhances Apache Kafka.

Confluent has integrated cutting-edge features that are designed to enhance these tasks:

Speed up application development and connectivity
Enable transformations through stream processing
Streamline business operations at scale
Adhere to strict architectural standards

Confluent is a more complete distribution of Kafka in that it enhances the integration possibilities of Kafka by introducing tools for managing and optimizing Kafka clusters while providing methods for making sure the streams are secure. Confluent supports publish-and-subscribe as well as the storing and processing of data within the streams. Kafka is easier to operate and build thanks to Confluent.

Confluent's software is available in three different varieties:

A free, open-source streaming platform that makes it simple to start using real-time data streams
An enterprise-grade version of the product with more administrative, ops, and monitoring tools
A premium cloud-based version.

Confluent Advantage Features

Confluent has many valuable key features. Some of the most useful ones include:

Multi-language
- Clients: C++, Python, Go, and .NET
- REST proxy: Can connect to Kafka from any connected network device
- Admin REST APIs: RESTful interface for performing administrator operations

Pre-built ecosystem
- Connectors: More than 100 supported connectors, including S3, Elastic, HDFS, JDBC
- MQTT proxy: Gain access to Kafka from MQTT gateways and devices
- Schema registry: Centralized database to guarantee data compatibility

Streaming database
- ksqlDB: Materialized views and real-time stream processing

GUI management
- Control panel: GUI for scalable Kafka management and monitoring
- Health+: Smart alerts and cloud-based control centers

DevOps automation that is flexible
- Confluent for Kubernetes: Complete API to deploy on Kubernetes
- Automated Ansible deployment on non-containerized environments

Dynamic performance
- Self-balancing clusters: Automated partition re-balancing across brokers in the cluster
- Tiered storage: Older Kafka data offloading to object storage with transparent access

Security that is enterprise-grade
- Role-based access control: Granular user/group access authorization
- Audit logs that are structured: Logs of user actions kept in dedicated Kafka topics
- Secret protection: Sensitive information is encrypted

Global resilience
- Linking clusters: A real-time, highly reliable, and consistent bridge across on-premises and cloud environments
- Multiple-region clusters: Single Kafka cluster with automated client failover distributed across multiple data centers
- Replicator: Asynchronous replication that is based on the Kafka Connect framework

Support
- Round the clock enterprise support from Kafka experts

Reviews from Real Users

Confluent stands out among its competitors for a number of reasons. Two major ones are its robust enterprise support and its open source option. PeerSpot users take note of the advantages of these features in their reviews:

Ravi B., a solutions architect at a tech services company, writes of the solution, “KSQL is a valuable feature, as is the Kafka Connect framework for connecting to the various source systems where you need not write the code. We get great support from Confluent because we're using the enterprise version and whenever there's a problem, they support us with fine-tuning and finding the root cause.”

Amit S., an IT consultant, notes, “The biggest benefit is that it is open source. You have the flexibility of opting or not opting for enterprise support, even though the tool itself is open source.” He adds, “The second benefit is it's very modern and built on Java and Scala. You can extend the features very well, and it doesn't take a lot of effort to do so.”

Sample Customers

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

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