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

11th

Average Rating

8.0

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Confluent

Ranking in Streaming Analytics

3rd

Average Rating

8.2

Reviews Sentiment

6.7

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 Spark Streaming is 3.1%, down from 3.7% compared to the previous year. The mindshare of Confluent is 8.3%, down from 10.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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."

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

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

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

"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."

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

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

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

More Apache Spark Streaming pros

"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."

"The most valuable feature of Confluent is the wide range of features provided. They're leading the market in this category."

"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."

"The documentation process is fast with the tool."

"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 tools."

"The benefit is escaping email communication. Sometimes people ignore emails or put them into spam, but with Confluence, everyone sees the same text at the same time."

"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."

More Confluent pros

Cons

"The debugging aspect could use some improvement."

"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."

"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."

"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."

"The solution itself could be easier to use."

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

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

More Apache Spark Streaming cons

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

"It could have more integration with different platforms."

"It would help if the knowledge based documents in the support portal could be available for public use as well."

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

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

"It could be improved by including a feature that automatically creates a new topic and puts failed messages."

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

"Confluent has a good monitoring tool, but it's not customizable."

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."

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

"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."

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

"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."

"Confluent is an expensive solution."

"Confluent is highly priced."

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

More Confluent pricing and cost advice

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Top Industries

By visitors reading reviews

Computer Software Company

23%

Financial Services Firm

20%

Comms Service Provider

University

Financial Services Firm

18%

Computer Software Company

14%

Manufacturing Company

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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?

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.

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

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Streaming Analytics

August 2025

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April 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