Amazon MSK vs Apache Spark Streaming comparison

Why PeerSpot?

Amazon Web Services (AWS) and Apache are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #13 with an average rating of 7.0, while Apache is ranked #5 with an average rating of 7.9. Amazon Web Services (AWS) holds a 3.8% mindshare in SA, compared to Apache’s 4.7% 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

2,960 Views
2,842 Comparison Views

93% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

3,367 Views
3,367 Comparison Views

94% willing to recommend

Amazon MSK

Apache Spark Streaming

Comparison Buyer's Guide

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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: September 2026).

Buyer's Guide

Amazon MSK vs. Apache Spark Streaming

September 2026

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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

Amazon MSK

Ranking in Streaming Analytics

13th

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

5th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Streaming Analytics category, the mindshare of Amazon MSK is 3.8%, down from 5.5% compared to the previous year. The mindshare of Apache Spark Streaming is 4.7%, up from 3.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Spark Streaming	4.7%
Amazon MSK	3.8%
Other	91.5%

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

"I have Amazon MSK integrated with other AWS services such as S3 and Lambda."

"The solution's technical support was helpful."

"It is a stable product."

"MSK has already launched ZooKeeper and the cluster, and this is included as an out-of-the-box feature, which is very convenient."

"Overall, it is very cost-effective based on the workflow."

"The scalability and usability are quite remarkable."

"My advice to others is this solution is straightforward and the integration is flawless."

"It offers good stability."

More Amazon MSK pros

"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."

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

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

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

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

"The solution is very stable and reliable."

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

More Apache Spark Streaming pros

Cons

"One of the reasons why we prefer Kafka is because the support is a little bit difficult to manage with Amazon MSK."

"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."

"MSK should be easier to integrate with other solutions. It should be more flexible, integration-wise."

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

"Horizontal scale-out is actually not easy, making it an area where improvements are required."

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

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

"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."

More Amazon MSK cons

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

"The debugging aspect could use some improvement."

"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 initial setup is quite complex."

"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 would like to have the ability to do arbitrary stateful functions in Python."

More Apache Spark Streaming cons

Pricing and Cost Advice

"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."

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

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

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

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

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Manufacturing Company

11%

Construction Company

Comms Service Provider

Outsourcing Company

14%

Financial Services Firm

14%

Comms Service Provider

Construction Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	8
Large Enterprise	5

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

Questions from the Community

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 advice do you have for others considering Amazon MSK?

We are working with Amazon MSK product, Managed Streaming for Apache Kafka. The main benefits I have seen from using Amazon MSK is that as a B2B enterprise client, we have many SLAs to fulfill. Tra...

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

What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

See all answers

Comparisons

Azure Stream Analytics vs Amazon MSK

Compared 12% of the time

Confluent vs Amazon MSK

Compared 9% of the time

Aiven Platform vs Amazon MSK

Compared 9% of the time

Amazon Kinesis vs Amazon MSK

Compared 9% of the time

Cloudera DataFlow vs Amazon MSK

Compared 6% of the time

More Amazon MSK Competitors

Confluent vs Apache Spark Streaming

Compared 11% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 11% of the time

Databricks vs Apache Spark Streaming

Compared 10% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

Amazon Kinesis vs Apache Spark Streaming

Compared 7% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Amazon MSK

October 2026

Download Amazon MSK product report

Buyer's Guide

Apache Spark Streaming

October 2026

Download Apache Spark Streaming product report

Also Known As

Amazon Managed Streaming for Apache Kafka

Spark Streaming

Overview

Amazon MSK offers seamless AWS integration, simplifying development and operation. It supports efficient data streaming and ensures cost-effective scalability without additional setup needs.

Amazon MSK stands out for its effortless creation, deployment, and access to new features without complex VPC configurations. Automating scalability, it demands minimal intervention, making it ideal for high-volume workflows. Developers benefit from real-time analytics, event sourcing, and log ingestion, aiding in dashboard maintenance and user log tracking. However, integration challenges exist as some face inflexibility, intricate configurations, and plugin development difficulties. Schema validation, connector variety, and complex update processes lead some to seek alternatives. Noteworthy for order data streaming, transaction tracking in retail and banking, and other real-time data applications, Amazon MSK remains attractive despite high cost concerns.

What are Amazon MSK's key features?

Seamless AWS Integration: No additional setup or VPC needed.
Automatic Scalability: Minimal intervention thanks to automatic configurations.
Real-time Analytics: Supports event sourcing and log ingestion.
Cost-effective: Tailored for high-volume workflows.
Encryption: Secure data during migrations.

What benefits should users consider when evaluating Amazon MSK?

Efficiency: Effortless deployment and development support.
Scalability: Automated processes reduce operational overhead.
Cost Management: Financially viable for larger data volumes.
Security: High level of encryption during data transfers.
Data Handling: Effective for streaming, integration, and real-time processing.

In retail and banking, Amazon MSK facilitates order data streaming and transaction tracking. Its capabilities in supporting CDC pipelines, high-volume data management, and asynchronous processes make it favorable for integrating systems, streaming IoT data, and managing dashboard flows. Challenges in integration and configuration persist, nudging users to explore different options in certain contexts.

Amazon Web Services (AWS)

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

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

September 2026

Free Report: Amazon MSK vs. Apache Spark Streaming

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