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Amazon Kinesis vs Apache Spark Streaming 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

Amazon Kinesis
Ranking in Streaming Analytics
2nd
Average Rating
8.0
Reviews Sentiment
7.0
Number of Reviews
29
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
17
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of January 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 5.6%, down from 9.3% 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
ProductMarket Share (%)
Amazon Kinesis5.6%
Apache Spark Streaming3.9%
Other90.5%
Streaming Analytics
 

Featured Reviews

CD
AWS Cloud Architect at a healthcare company with 10,001+ employees
Real-time streaming and seamless integration enhance workloads with room for competitive pricing improvements
Amazon Kinesis is easy to get started with, provides good documentation, and has a multilang daemon interface that makes it programming-language agnostic. The throughput is convenient for processing volumes out of the box and does not require complex configurations. It also provides auto-scaling with different partition keys into various shards. Lambda's scalability, seamless integration with other AWS services, and support for multiple programming languages are very beneficial.
Himansu Jena - PeerSpot reviewer
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.

Quotes from Members

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

Pros

"What turns out to be most valuable is its integration with Lambda functions because you can process the data as it comes in. As soon as data comes, you'll fire a Lambda function to process a trench of data."
"The solution works well in rather sizable environments."
"One of the best features of Amazon Kinesis is the multi-partition."
"There is no problem with the tool's stability."
"The integration capabilities of the product are good."
"Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."
"The solution's technical support is flawless."
"The integration between Amazon Kinesis and Lambda helps us significantly."
"The solution is better than average and some of the valuable features include efficiency and stability."
"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."
"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."
"The main benefits of Apache Spark Streaming include cost savings, time savings, and efficiency improvements about data storage."
"The solution is very stable and reliable."
"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."
"As an open-source solution, using it is basically free."
 

Cons

"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Kinesis can be expensive, especially when dealing with large volumes of data."
"Lacks first in, first out queuing."
"Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes."
"The services which are described in the documentation could use some visual presentation because for someone who is new to the solution the documentation is not easy to follow or beginner friendly and can leave a person feeling helpless."
"One thing that would be nice would be a policy for increasing the number of Kinesis streams because that's the one thing that's constant. You can change it in real time, but somebody has to change it, or you have to set some kind of meter. So, auto-scaling of adding and removing streams would be nice."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"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."
"In terms of improvement, the UI could be better."
"One improvement I would expect is real-time processing instead of micro-batch or near real-time."
"The initial setup is quite complex."
"The solution itself could be easier to use."
"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."
"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."
 

Pricing and Cost Advice

"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."
"The tool's pricing is cheap."
"The tool's entry price is cheap. However, pricing increases with data volume."
"Amazon Kinesis is an expensive solution."
"It was actually a fairly high volume we were spending. We were spending about 150 a month."
"Under $1,000 per month."
"The product falls on a bit of an expensive side."
"The fee is based on the number of hours the service is running."
"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."
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Top Industries

By visitors reading reviews
Computer Software Company
17%
Financial Services Firm
15%
Manufacturing Company
7%
Comms Service Provider
5%
Computer Software Company
22%
Financial Services Firm
21%
University
7%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business8
Midsize Enterprise10
Large Enterprise9
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise2
Large Enterprise7
 

Questions from the Community

What do you like most about Amazon Kinesis?
Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive.
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.
What needs improvement with Amazon Kinesis?
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 u...
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?
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...
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 ...
 

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis
Spark Streaming
 

Overview

 

Sample Customers

Zillow, Netflix, Sonos
UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
Find out what your peers are saying about Amazon Kinesis vs. Apache Spark Streaming and other solutions. Updated: December 2025.
881,114 professionals have used our research since 2012.