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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
7.1
Number of Reviews
28
Ranking in other categories
No ranking in other categories
Apache Spark Streaming
Ranking in Streaming Analytics
11th
Average Rating
8.0
Reviews Sentiment
7.4
Number of Reviews
12
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of August 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 7.6%, down from 12.1% compared to the previous year. The mindshare of Apache Spark Streaming is 3.1%, down from 3.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Prabin Silwal - PeerSpot reviewer
Pipeline setup is very simple
I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs. Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.
Venkata Phaneendra Reddy Janga - PeerSpot reviewer
Improved data latency and integration with diverse data sources enables robust real-time processing
The best feature of Apache Spark Streaming is that it's built upon the Spark SQL engine. This is easy for someone coming from a SQL background to work with real-time data, even if they are new to real-time processing. They can quickly get started using the Spark SQL engine. Another valuable feature is that we can control many aspects such as the configuration of the engine, memory management, and have a checkpointing mechanism that allows us to manually start or restart jobs from a specific point. This is particularly useful for restoring messages of a Kafka topic from a specific date and time using the checkpointing mechanism. The integration with Spark's ecosystems such as MLlib and GraphX has significant potential, although I have not worked on that part as we focus mainly on data engineering. We can handle late-arriving data with Apache Spark Streaming. Sometimes aggregation results might be missed if data arrives out of order, but features such as windowing allow us to manage out-of-order data by specifying a watermark time. Recently released mechanisms to query the state make it easier to handle data programmatically.

Quotes from Members

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

Pros

"I find almost all features valuable, especially the timing and fast pace movement."
"I have worked in companies that build tools in-house. They face scaling challenges."
"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."
"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."
"The most valuable feature is that it has a pretty robust way of capturing things."
"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."
"Great auto-scaling, auto-sharing, and auto-correction features."
"Amazon Kinesis has improved our ROI."
"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."
"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."
"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."
"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 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."
"The solution is better than average and some of the valuable features include efficiency and stability."
 

Cons

"Could include features that make it easier to scale."
"Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes."
"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."
"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."
"Lacks first in, first out queuing."
"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."
"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."
"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."
"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."
"In terms of improvement, the UI could be better."
"We don't have enough experience to be judgmental about its flaws."
"The solution itself could be easier to use."
"One improvement I would expect is real-time processing instead of micro-batch or near real-time."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"It was resource-intensive, even for small-scale applications."
 

Pricing and Cost Advice

"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 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 is an expensive solution."
"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."
"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 tool's entry price is cheap. However, pricing increases with data volume."
"Under $1,000 per month."
"Spark is an affordable solution, especially considering its open-source nature."
"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."
"People pay for Apache Spark Streaming as a service."
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Top Industries

By visitors reading reviews
Computer Software Company
18%
Financial Services Firm
17%
Manufacturing Company
10%
Educational Organization
5%
Computer Software Company
22%
Financial Services Firm
21%
University
5%
Manufacturing Company
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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?
Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes. Also, the KCL library's documentation could be improved to better explain the configuration parameters...
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?
We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...
What is your primary use case for Apache Spark Streaming?
We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.
 

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: July 2025.
865,164 professionals have used our research since 2012.