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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

"There is no problem with the tool's stability."
"The most valuable feature is that it has a pretty robust way of capturing things."
"The scalability is pretty good."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us."
"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."
"What I like about Amazon Kinesis is that it's very effective for small businesses. It's a well-managed solution with excellent reporting. Amazon Kinesis is also easy to use, and even a novice developer can work with it, versus Apache Kafka, which requires expertise."
"Great auto-scaling, auto-sharing, and auto-correction features."
"The solution is very stable and reliable."
"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 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."
"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."
"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."
"It's the fastest solution on the market with low latency data on data transformations."
"As an open-source solution, using it is basically free."
 

Cons

"It would be beneficial if Amazon Kinesis provided document based support on the internet to be able to read the data from the Kinesis site."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"Lacks first in, first out queuing."
"We were charged high costs for the solution’s enhanced fan-out feature."
"Kinesis Data Analytics needs to be improved somewhat. It's SQL based data but it is not as user friendly as MySQL or Athena tools."
"Snapshot from the the from the the stream of the data analytic I have already on the cloud, do a snapshot to not to make great or to get the data out size of the web service. But to stop the process and restart a few weeks later when I have more data or more available of the client teams."
"Amazon Kinesis should improve its limits."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"Integrating event-level streaming capabilities could be beneficial."
"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."
"One improvement I would expect is real-time processing instead of micro-batch or near real-time."
"We would like to have the ability to do arbitrary stateful functions in Python."
"In terms of improvement, the UI could be better."
"The solution itself could be easier to use."
"The debugging aspect could use some improvement."
 

Pricing and Cost Advice

"Under $1,000 per month."
"It was actually a fairly high volume we were spending. We were spending about 150 a month."
"The tool's entry price is cheap. However, pricing increases with data volume."
"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."
"The pricing depends on the use cases and the level of usage. If you wanted to use Kinesis for different use cases, there's definitely a cheaper base cost involved. However, it's not entirely cheap, as different use cases might require different levels of Kinesis usage."
"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."
"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."
"Spark is an affordable solution, especially considering its open-source nature."
"I was using the open-source community version, which was self-hosted."
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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.
881,176 professionals have used our research since 2012.