Amazon MSK vs Apache Spark Streaming comparison

Amazon Web Services (AWS) and Apache are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #6 with an average rating of 7.2, while Apache is ranked #8 with an average rating of 7.9. Amazon Web Services (AWS) holds a 5.1% mindshare in SA, compared to Apache’s 3.9% mindshare. Additionally, 93% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 94% of Apache users who would recommend it.

Amazon MSK

Read 14 Amazon MSK reviews

2,255 Views
2,210 Comparison Views

93% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

1,268 Views
1,268 Comparison Views

94% willing to recommend

Amazon MSK

Apache Spark Streaming

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 17, 2024

Apache Spark Streaming and Amazon MSK compete in the realm of data processing solutions, focusing on streaming capabilities. Apache Spark Streaming seems to have the upper hand in processing and flexibility, while Amazon MSK is advantageous for seamless AWS integration.

Features: Apache Spark Streaming is notable for its powerful data processing capabilities, supporting complex analytics and transformations. Its scalability is a significant advantage, especially for real-time analytics. Amazon MSK offers strong integration within the AWS ecosystem, providing a reliable, scalable platform for real-time data streaming through open-source Kafka. This integration is particularly beneficial for organizations utilizing other AWS services.

Room for Improvement: Apache Spark Streaming could improve in terms of deployment complexity, which often requires detailed configuration and infrastructure management. Its integration capabilities with non-open-source ecosystems could also be enhanced. On the other hand, Amazon MSK may benefit from increased processing flexibility and the ability to handle more complex data transformations. The service could improve in supporting a broader range of integrations outside the AWS ecosystem, offering more versatility.

Ease of Deployment and Customer Service: Apache Spark Streaming's deployment can be complex, often requiring significant technical expertise, which can impact rapid deployment and operational efficiency. Amazon MSK simplifies deployment with managed Kafka services, reducing operational overhead. While both offer substantial support, some find Amazon MSK's AWS documentation and support channels more robust.

Pricing and ROI: Apache Spark Streaming may involve higher initial setup costs due to infrastructure requirements, potentially affecting short-term ROI. However, its flexibility supports cost-effective large-scale data processing over time. Amazon MSK presents a more predictable cost model, aligned with AWS services, which is appealing for businesses seeking predictable expenses and faster ROI despite potentially higher upfront costs. Apache Spark Streaming may deliver better returns for intensive data processing tasks long-term.

To learn more, read our detailed Amazon MSK vs. Apache Spark Streaming Report (Updated: December 2025).

Buyer's Guide

Amazon MSK vs. Apache Spark Streaming

December 2025

Download the complete report

Helped 881,082 peers since 2012

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

Amazon MSK

Ranking in Streaming Analytics

6th

Average Rating

7.2

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

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

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Amazon MSK is 5.1%, down from 8.4% compared to the previous year. The mindshare of Apache Spark Streaming is 3.9%, up from 3.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Amazon MSK	5.1%
Apache Spark Streaming	3.9%
Other	91.0%

Streaming Analytics

Featured Reviews

SYED SHAAZ

Co-Founder & CTO at Photios AI

Improved data streaming and integration challenges prompt search for alternatives

The integration capabilities of Amazon MSK are not very flexible. If you have your own self-managed Kafka, that helps significantly because you can set up configurations. We are considering self-managed Kafka since our product is only one year old. The Kafka integrations are fine, but the configurations are an issue. The only issue with Amazon MSK that we are facing is the configurations. There are preset configurations and limited configurations that we can set for our unique use case. The product could improve by allowing us to set different configurations. I would also like to see Amazon MSK improve in the area of connectors. We are considering Confluent Cloud because they have many more connectors. They have KSQL DB and governance features. It is slightly costlier, but Confluent offers more flexibility with their connectors.

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.

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Amazon MSK has significantly improved our organization by building seamless integration between systems."

"It provides installations, scaling, and other functionalities straight out of the box."

"It is a stable product."

"Amazon MSK's scalability is very good."

"It offers good stability."

"Amazon MSK has contributed positively to our real-time analytics capabilities because Fortis's dashboards have dashboard health that needs to be maintained, user logs that need to be maintained, and usage tracking."

"Amazon MSK's separation of concerns and ease of creating and deploying new features are highly valuable. It just requires to assign them to the topic, and then anyone who needs to consume these messages can do so directly from Amazon MSK. This separation of concerns makes it very convenient, especially for new feature development, as developers can easily access the messages they need without having to deal with complex server communications or protocol setups."

"The most valuable feature of Amazon MSK is the integration."

More Amazon MSK pros

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

"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."

"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."

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

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

"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."

"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."

More Apache Spark Streaming pros

Cons

"It should be more flexible, integration-wise."

"The cost of using Amazon MSK is high, which is a significant disadvantage, as the increase in cloud costs by 50% to 60% does not justify the savings."

"The configuration seems a little complex and the documentation on the product is not available."

"It does not autoscale. Because if you do keep it manually when you add a note to the cluster and then you register it, then it is scalable, but the fact that you have to go and do it, I think, makes it, again, a bit of some operational overhead when managing the cluster."

"It would be really helpful if Amazon MSK could provide a single installation that covers all the servers."

"The product's schema support needs enhancement. It will help enhance integration with many kinds of languages of programming languages, especially for environments using languages like .NET."

"We need to create connectors in Amazon MSK, but there are no default connectors in AWS for that purpose."

"The downside of Amazon MSK is that when I needed to make a small change in the MSK configuration, it wasn't possible. I had to remove and drop all the clusters and recreate them again, which is complicated in a production environment."

More Amazon MSK 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."

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

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

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

"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."

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

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

More Apache Spark Streaming cons

Pricing and Cost Advice

"The price of Amazon MSK is less than some competitor solutions, such as Confluence."

"The platform has better pricing than one of its competitors."

"When you create a complete enterprise-driven architecture that is deployable on an enterprise scale, I would say that the prices of Amazon MSK and Confluent Platform become comparable."

"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."

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

See which vendors are best for you

Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.

See recommendations

881,082 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

22%

Computer Software Company

12%

Manufacturing Company

Construction Company

Computer Software Company

22%

Financial Services Firm

21%

University

Healthcare Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	7
Large Enterprise	4

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

Questions from the Community

What do you like most about Amazon MSK?

Amazon MSK has significantly improved our organization by building seamless integration between systems.

See all answers

What needs improvement with Amazon MSK?

See all answers

What is your primary use case for Amazon MSK?

We are recently working with Amazon MSK at Fortis, where we have multiple dashboards in our revenue intelligence platform. We are streaming data from different apps into those dashboards. The data ...

See all answers

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.

See all answers

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...

See all answers

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 ...

See all answers

Comparisons

Azure Stream Analytics vs Amazon MSK

Compared 23% of the time

Apache Flink vs Amazon MSK

Compared 13% of the time

Confluent vs Amazon MSK

Compared 11% of the time

Aiven Platform vs Amazon MSK

Compared 9% of the time

Databricks vs Amazon MSK

Compared 3% of the time

More Amazon MSK Competitors

Azure Stream Analytics vs Apache Spark Streaming

Compared 13% of the time

Confluent vs Apache Spark Streaming

Compared 9% of the time

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 9% of the time

Apache Flink vs Apache Spark Streaming

Compared 9% of the time

EVAM Event Stream Processing (ESP) Platform vs Apache Spark Streaming

Compared 3% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Amazon MSK

January 2026

Download Amazon MSK product report

Buyer's Guide

Apache Spark Streaming

January 2026

Download Apache Spark Streaming product report

Also Known As

Amazon Managed Streaming for Apache Kafka

Spark Streaming

Overview

Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that enables you to build and run applications that use Apache Kafka to process streaming data. Amazon MSK provides the control-plane operations, such as those for creating, updating, and deleting clusters.

Amazon Web Services (AWS)

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

Apache

Sample Customers

Expedia, Intuit, Royal Dutch Shell, Brooks Brothers

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

Buyer's Guide

Amazon MSK vs. Apache Spark Streaming

December 2025

Free Report: Amazon MSK vs. Apache Spark Streaming

Find out what your peers are saying about Amazon MSK vs. Apache Spark Streaming and other solutions. Updated: December 2025.

DOWNLOAD NOW

881,082 professionals have used our research since 2012.

See our Amazon MSK vs. Apache Spark Streaming report.

See our list of best Streaming Analytics vendors.

We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.