Amazon Kinesis 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 #11 with an average rating of 8.3, while Apache is ranked #9 with an average rating of 7.9. Amazon Web Services (AWS) holds a 4.0% mindshare in SA, compared to Apache’s 4.7% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 94% of Apache users who would recommend it.

Amazon Kinesis

Read 29 Amazon Kinesis reviews

3,300 Views
3,300 Comparison Views

90% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,655 Views
2,655 Comparison Views

94% willing to recommend

Amazon Kinesis

Apache Spark Streaming

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 17, 2024

Apache Spark Streaming and Amazon Kinesis are both prominent contenders in the real-time data processing market. Despite the competitive pricing of Apache Spark Streaming, Amazon Kinesis is preferred due to its superior set of features.

Features: Apache Spark Streaming provides seamless integration with Hadoop ecosystems, fault tolerance, and scalability for complex analytics tasks. On the other hand, Amazon Kinesis offers extensive real-time analytics capabilities, customizable processing, and strong integration with the AWS ecosystem, making it highly adaptable for different use cases.

Room for Improvement: Apache Spark Streaming could enhance user-friendliness for those without extensive expertise, offer improved managed services, and simplify deployment to become more accessible. Amazon Kinesis might benefit from expanded documentation to assist beginners, enhanced integration with non-AWS services, and a reduction in potential costs associated with high data volumes.

Ease of Deployment and Customer Service: Amazon Kinesis simplifies deployment with a managed service model and provides robust customer support. In contrast, Apache Spark Streaming requires more manual configuration and expertise but offers extensive configuration flexibility.

Pricing and ROI: Apache Spark Streaming is cost-effective at the start due to its open-source nature but may incur higher operational costs over time with infrastructure management. Amazon Kinesis involves predictable monthly expenses that can lead to a higher ROI due to its ease of scaling and resource management.

To learn more, read our detailed Amazon Kinesis vs. Apache Spark Streaming Report (Updated: August 2026).

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

August 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 Kinesis

Ranking in Streaming Analytics

11th

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

9th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 4.0%, down from 7.6% compared to the previous year. The mindshare of Apache Spark Streaming is 4.7%, up from 3.1% 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 Kinesis	4.0%
Other	91.3%

Streaming Analytics

Featured Reviews

reviewer1480695

Director of Software Development at a tech vendor with 10,001+ employees

Has enabled real-time processing of critical event streams with seamless cloud integration

We are contemplating moving away from Amazon Kinesis primarily because of the cost. It is very useful, but if we write our own analytics and data processing pipeline, it would be much cheaper for us. The cost is a primary hindrance. That's why we are not using it widely. For our critical pipeline we are using it, but after that we are putting it in an S3 bucket. Other pipelines directly put the events in an S3 bucket and then process from there. There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete. The cost aspect is what we are really concerned about.

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Kuldeep Pal

Data Engineer at Walmart Global Tech

Efficient data handling empowers near-real-time fraud detection and robust recovery mechanisms

The positive impact from Apache Spark Streaming is its near real-time capability. It has a good ecosystem that provides good support. However, if you need purely real-time data, you would be going with Flink. Apache Spark Streaming is good for near-to-real-time data and requires less maintenance, which is beneficial for developers and companies. The new feature coming in Apache Spark Streaming 4 is continuous streaming. If continuous streaming becomes stable and performs comparably to Flink, then Apache Spark Streaming would be preferred everywhere due to its good maintenance and support system. While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable. Sometimes it fails, requiring numerous configurations such as checkpointing, watermarking, and other features. If you select a 10-minute window and the data arrives at the 30th minute, it sometimes loses data in between. You also have to apply back pressure when numerous messages are coming in. It requires constant monitoring and maintenance. I would say it is 90-95% reliable, but multiple configurations and frequent maintenance make it slightly less reliable. The continuous deployment feature being in beta phase could benefit everyone if released earlier.

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

"The major advantage with Amazon Kinesis is the availability, additionally the reliability is awesome, the replay is incredibly fast, and the ingesting, buffering, and processing of data are really fast."

"The integration between Amazon Kinesis and Lambda helps us significantly."

"The integration capabilities of the product are good."

"From my experience, one of the most valuable features is the ability to track silent events on endpoints. Previously, these events might have gone unnoticed, but now we can access them within the product range. For example, if a customer reports that their calls are not reaching the portal files, we can use this feature to troubleshoot and optimize the system."

"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."

"The scalability is pretty good, as one can have any number of nodes spawned or replicated on the primary so that any load can be handled, perhaps a few terabytes with ease in around 15 seconds."

"The product's initial setup phase is not difficult because we are using the tool on the cloud."

"Before Data Streams, we were using a couple of other solutions, including Talend and Pentaho, to move data around, and the ability to have one single flow of data from multiple consumers simplified our architecture a lot, saves us a lot of time, and will help us in the future when we have to build additional applications based on the same input data."

More Amazon Kinesis pros

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

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

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

"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."

"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."

"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."

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

"It is the most scalable tool that I have seen before."

More Apache Spark Streaming pros

Cons

"Could include features that make it easier to scale."

"The default limit that they have, which at the moment is 5,000 records per second seems too low."

"The tool should focus on having an alert system rather than having to use a third-party solution."

"We were charged high costs for the solution’s enhanced fan-out feature."

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

"For me, especially with video streams, there's sometimes a kind of delay when the data has to be pumped to other services. This delay could be improved in Kinesis, or especially the Kinesis Video Streams, which is being used for different use cases for Amazon Connect. With that improvement, a lot of other use cases of Amazon Connect integrating with third-party analytic tools would be easier."

"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."

"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."

More Amazon Kinesis cons

"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 debugging aspect could use some improvement."

"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 initial setup is quite complex."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

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

"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."

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

More Apache Spark Streaming cons

Pricing and Cost Advice

"I rate the product price a five on a scale of one to ten, where one is cheap, and ten is expensive."

"The fee is based on the number of hours the service is running."

"Amazon Kinesis pricing is sometimes reasonable and sometimes could be better, depending on the planning, so it's a five out of ten for me."

"The solution's pricing is fair."

"In general, cloud services are very convenient to use, even if we have to pay a bit more, as we know what we are paying for and can focus on other tasks."

"The product falls on a bit of an expensive side."

"I think for us, with Amazon Kinesis, if we have to set up our own Kafka or cluster, it will be very time-consuming. If one considers the aforementioned aspect, Amazon Kinesis is a cheap tool."

"It was actually a fairly high volume we were spending. We were spending about 150 a month."

More Amazon Kinesis 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."

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

10%

Manufacturing Company

Outsourcing Company

Financial Services Firm

16%

Comms Service Provider

Outsourcing Company

Marketing Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	10
Large Enterprise	10

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

Questions from the Community

What is your experience regarding pricing and costs for Amazon Kinesis?

Amazon Kinesis and Lambda pricing is competitive, but we noticed that scaling and large volumes could potentially increase costs significantly.

See all answers

What needs improvement with Amazon Kinesis?

See all answers

What is your primary use case for Amazon Kinesis?

We use Amazon Kinesis for stream processing. We get events from on-premise devices to the cloud. We get many device events and we have to process these events that are coming from the devices. To p...

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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

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Compared 5% of the time

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Compared 14% of the time

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Compared 11% of the time

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Compared 9% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

August 2026

Download Amazon Kinesis product report

Buyer's Guide

Apache Spark Streaming

August 2026

Download Apache Spark Streaming product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Spark Streaming

Overview

Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.

Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.

What are the key features of Amazon Kinesis?

Real-time Data Streaming: Enables live data processing and analytics.
Seamless AWS Integration: Ensures smooth operation with AWS services.
High Throughput: Consistent 2Mbps rate supports large volumes of data.
Auto-scaling: Automatically adjusts to data load.
Fault Tolerance: Maintains system reliability and data integrity.
Lambda Function Processing: Processes data efficiently using Lambda.

What benefits or ROI should users expect?

Scalable Data Management: Handles data influx effortlessly.
Efficiency: Streamlines data flows without impacting performance.
Cost-effectiveness: Delivers value for budget-conscious users.
Low Latency: Provides real-time insights and reactions.
Multi-language Support: Accommodates various development needs.

In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.

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

Zillow, Netflix, Sonos

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

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

August 2026

Free Report: Amazon Kinesis vs. Apache Spark Streaming

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