Try our new research platform with insights from 80,000+ expert users

Apache Spark Streaming vs Confluent comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 17, 2024

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
8.0
Reviews Sentiment
7.4
Number of Reviews
11
Ranking in other categories
No ranking in other categories
Confluent
Ranking in Streaming Analytics
4th
Average Rating
8.2
Reviews Sentiment
6.7
Number of Reviews
23
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of July 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.6%, down from 3.7% compared to the previous year. The mindshare of Confluent is 8.3%, down from 10.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Oscar Estorach - PeerSpot reviewer
Versatile and flexible when dealing with large-scale data streams
What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.
Gustavo-Barbosa Dos Santos - PeerSpot reviewer
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.

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."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"Spark Streaming is critical, quite stable, full-featured, and scalable."
"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 was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"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 platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"Kafka Connect framework is valuable for connecting to the various source systems where code doesn't need to be written."
"It is also good for knowledge base management."
"Confluence's greatest asset is its user-friendly interface, coupled with its remarkable ability to seamlessly integrate with a vast range of other solutions."
"The design of the product is extremely well built and it is highly configurable."
"The solution can handle a high volume of data because it works and scales well."
"The most valuable is its capability to enhance the documentation process, particularly when creating software documentation."
"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."
"Our main goal is to validate whether we can build a scalable and cost-efficient way to replicate data from these various sources."
 

Cons

"Integrating event-level streaming capabilities could be beneficial."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"The solution itself could be easier to use."
"We would like to have the ability to do arbitrary stateful functions in Python."
"The debugging aspect could use some improvement."
"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."
"In terms of improvement, the UI could be better."
"It could have more integration with different platforms."
"I am not very impressed by Confluent. We continuously face issues, such as Kafka being down and slow responses from the support team."
"It would help if the knowledge based documents in the support portal could be available for public use as well."
"The Schema Registry service could be improved. I would like a bigger knowledge base of other use cases and more technical forums. It would be good to have more flexible monitoring features added to the next release as well."
"They should remove Zookeeper because of security issues."
"Confluence could improve the server version of the solution. However, most companies are going to the cloud."
"We continuously face issues, such as Kafka being down and slow responses from the support team."
"It could be more user-friendly and centralized. A way to reduce redundancy would be helpful."
 

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."
"People pay for Apache Spark Streaming as a service."
"Spark is an affordable solution, especially considering its open-source nature."
"I was using the open-source community version, which was self-hosted."
"The solution is cheaper than other products."
"You have to pay additional for one or two features."
"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 highly priced."
"It comes with a high cost."
"Confluent is expensive, I would prefer, Apache Kafka over Confluent because of the high cost of maintenance."
"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."
"Confluent is an expensive solution as we went for a three contract and it was very costly for us."
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
860,825 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Financial Services Firm
27%
Computer Software Company
22%
Manufacturing Company
5%
Healthcare Company
4%
Financial Services Firm
18%
Computer Software Company
16%
Manufacturing Company
6%
Insurance Company
5%
 

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...
 

Also Known As

Spark Streaming
No data available
 

Overview

 

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
ING, Priceline.com, Nordea, Target, RBC, Tivo, Capital One, Chartboost
Find out what your peers are saying about Apache Spark Streaming vs. Confluent and other solutions. Updated: June 2025.
860,825 professionals have used our research since 2012.