No more typing reviews! Try our Samantha, our new voice AI agent.

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
9th
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 April 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 5.2%, down from 9.0% compared to the previous year. The mindshare of Apache Spark Streaming is 3.9%, up from 2.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics Mindshare Distribution
ProductMindshare (%)
Amazon Kinesis5.2%
Apache Spark Streaming3.9%
Other90.9%
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

"Amazon Kinesis is easy to get started with, provides good documentation, and has a multilang daemon interface that makes it programming-language agnostic."
"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."
"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 most valuable feature of Amazon Kinesis is real-time data streaming."
"I find data Firehose and data streams are much more intelligent than other streaming solutions."
"We have been able to drop our costs for ingesting data by about 60 to 70%."
"Its scalability is very high. There is no maintenance and there is no throughput latency. I think data scalability is high, too. You can ingest gigabytes of data within seconds or milliseconds."
"The solution works well in rather sizable environments."
"The solution is better than average and some of the valuable features include efficiency and stability."
"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"Spark Streaming is critical, quite stable, full-featured, and scalable."
"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, as developers can build APIs easily for a code-steaming pipeline and the solution has an ecosystem of integration with other stock services."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"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."
 

Cons

"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."
"The solution has a two-minute maximum time delay for live streaming, which could be reduced."
"In order to do a successful setup, the person handling the implementation needs to know the solution very well."
"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."
"Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes."
"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."
"We would like to have the ability to do arbitrary stateful functions in Python."
"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."
"We would like to have the ability to do arbitrary stateful functions in Python."
"Integrating event-level streaming capabilities could be beneficial."
"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."
 

Pricing and Cost Advice

"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 fee is based on the number of hours the service is running."
"The product falls on a bit of an expensive side."
"It was actually a fairly high volume we were spending. We were spending about 150 a month."
"Amazon Kinesis is an expensive solution."
"The tool's entry price is cheap. However, pricing increases with data volume."
"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."
"People pay for Apache Spark Streaming as a service."
"I was using the open-source community version, which was self-hosted."
"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."
report
Use our free recommendation engine to learn which Streaming Analytics solutions are best for your needs.
885,667 professionals have used our research since 2012.
 

Top Industries

By visitors reading reviews
Computer Software Company
15%
Financial Services Firm
13%
Manufacturing Company
6%
Construction Company
5%
Financial Services Firm
21%
Computer Software Company
9%
Comms Service Provider
8%
Marketing Services Firm
7%
 

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 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 ...
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...
 

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: March 2026.
885,667 professionals have used our research since 2012.