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: July 2025).

Apache Spark Streaming vs. Confluent

July 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

10th

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

3rd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.6%, up from 3.5% compared to the previous year. The mindshare of Confluent is 8.4%, down from 9.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Confluent	8.4%
Apache Spark Streaming	3.6%
Other	88.0%

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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Gustavo-Barbosa Dos Santos

Software Architect at C&A Brasil

Has good technical support services and a valuable feature for real-time data streaming

Implementing Confluent's schema registry has significantly enhanced our organization's data quality assurance. It helps us understand the various requirements of multiple customers and validates the information for different versions. We can automate the tasks using Confluent Kafka. Thus, it guarantees us the data quality and maintains the integrity of message contracts.

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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

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

"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."

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

"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."

"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."

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

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

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

More Apache Spark Streaming pros

"The client APIs are the most valuable feature."

"The solution can handle a high volume of data because it works and scales well."

"The most valuable feature that we are using is the data replication between the data centers allowing us to configure a disaster recovery or software. However, is it's not mandatory to use and because most of the features that we use are from Apache Kafka, such as end-to-end encryption. Internally, we can develop our own kind of product or service from Apache Kafka."

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

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

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

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

"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."

More Confluent pros

Cons

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

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

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

"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."

"The solution itself could be easier to use."

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

"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

"We continuously face issues, such as Kafka being down and slow responses from the support team."

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

"The formatting aspect within the page can be improved and more powerful."

"In Confluent, there could be a few more VPN options."

"It requires some application specific connectors which are lacking. This needs to be added."

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

"I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team."

More Confluent cons

Pricing and Cost Advice

"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."

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

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

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

"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."

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

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

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

"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."

"It comes with a high cost."

"Regarding pricing, I think Confluent is a premium product, but it's hard for me to say definitively if it's overly expensive. We're still trying to understand if the features and reduced maintenance complexity justify the cost, especially as we scale our platform use."

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

23%

Financial Services Firm

21%

Healthcare Company

University

Financial Services Firm

18%

Computer Software Company

14%

Retailer

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	2
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	6
Midsize Enterprise	4
Large Enterprise	15

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?

What is your primary use case for Apache Spark Streaming?

I use Apache Spark Streaming for GIS (Graphical Information System), satellite imaging processing, image processing, longitude, latitude, and predicting electricity, road, and transformations in th...

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?

I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team. The lack of easy access to the Confluent support team is also a...

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Product Reports

Download Apache Spark Streaming product report

Apache Spark Streaming

September 2025

Download Confluent product report

September 2025

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