Amazon Kinesis vs Apache Spark Streaming comparison

Amazon Web Services (AWS) and Apache are both solutions in the Streaming Analytics category. Amazon Web Services (AWS) is ranked #2 with an average rating of 7.8, while Apache is ranked #10 with an average rating of 8.0. Amazon Web Services (AWS) holds a 7.1% mindshare in SA, compared to Apache’s 3.6% mindshare. Additionally, 89% 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 28 Amazon Kinesis reviews

3,185 Views
3,185 Comparison Views

89% willing to recommend

Apache Spark Streaming

Read 16 Apache Spark Streaming reviews

1,359 Views
1,359 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: July 2025).

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

July 2025

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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

2nd

Average Rating

8.0

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of September 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 7.1%, down from 11.4% compared to the previous year. The mindshare of Apache Spark Streaming is 3.6%, up from 3.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Market Share Distribution
Product	Market Share (%)
Amazon Kinesis	7.1%
Apache Spark Streaming	3.6%
Other	89.3%

Streaming Analytics

Featured Reviews

Rajni Kumar Jha

Senior Software Engineer at JPMorgan Chase & Co.

Used for media streaming and live-streaming data

It is not compulsory to use Amazon Kinesis. If you don't want to use the data streaming, you can use just the Kinesis data firehose. Using the Kinesis data firehose is compulsory because we can't store all chats and recordings in Amazon S3 without it. When a call comes in the Amazon Kinesis instance, it will go to Data Streams if we use it. Otherwise, it will go to the Kinesis data firehose, where we need to define the S3 bucket path, and it will go to Amazon S3. So, without the Kinesis data firehose, we can't store all the chats and recordings in Amazon S3. Using Amazon Kinesis totally depends upon the user's requirements. If you want to use live streaming for the data lake or data analyst team, you need to use Amazon Kinesis. If you don't want to use it, you can directly use the Kinesis data firehose, which will be stored in Amazon S3. Overall, I rate the solution an eight out of ten.

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Himansu Jena

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.

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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."

"Amazon Kinesis's main purpose is to provide near real-time data streaming at a consistent 2Mbps rate, which is really impressive."

"One of the best features of Amazon Kinesis is the multi-partition."

"The most valuable feature of Amazon Kinesis is real-time data streaming."

"The most valuable feature is that it has a pretty robust way of capturing things."

"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 management and analytics are valuable features."

"Setting Amazon Kinesis up is quick and easy; it only takes a few minutes to configure the necessary settings and start using it."

More Amazon Kinesis pros

"It's the fastest solution on the market with low latency data on data transformations."

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

"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."

"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."

"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."

"For Apache Spark Streaming, the feature I appreciated most is that it provides live data delivery; additionally, it provides the capability to send a larger amount of data in parallel."

"By integrating Apache Spark Streaming, the data freshness rate, and latency have significantly improved from 24-hour batch processing to less than one minute, facilitating faster communication to downstream systems, aiding marketing campaigns."

More Apache Spark Streaming pros

Cons

"There could be valid data in Kinesis that is not being processed, which affects stability. Although it rarely happens, this issue has been observed in many deployments, making it not completely stable."

"Lacks first in, first out queuing."

"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."

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

"I suggest integrating additional features, such as incorporating Amazon Pinpoint or Amazon Connect as bundled offerings, rather than deploying them as separate services."

"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. You can't just come into it blind and with little to no experience."

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

More Amazon Kinesis cons

"It was resource-intensive, even for small-scale applications."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

"We don't have enough experience to be judgmental about its flaws."

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

"While it is reliable, there are some issues with Apache Spark Streaming as it is not 100% reliable."

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

"When dealing with various data types including COBOL, Excel, JSON, video, audio, and MPG files, challenges can arise with incomplete or missing values."

More Apache Spark Streaming cons

Pricing and Cost Advice

"Under $1,000 per month."

"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."

"The tool's pricing is cheap."

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

"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."

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

More Amazon Kinesis pricing and cost advice

"Spark is an affordable solution, especially considering its open-source nature."

"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."

"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

16%

Manufacturing Company

10%

Comms Service Provider

Computer Software Company

22%

Financial Services Firm

21%

Healthcare Company

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	2
Large Enterprise	7

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.

See all answers

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?

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...

See all answers

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.

See all answers

What needs improvement with Apache Spark Streaming?

See all answers

What is your primary use case for Apache Spark Streaming?

I use Apache Spark Streaming for GIS (Graphical Information System), satellite imaging processing, image processing, longitude, latitude, and predicting electricity, road, and transformations in th...

See all answers

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Compared 9% of the time

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Compared 8% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

September 2025

Download Amazon Kinesis product report

Buyer's Guide

Apache Spark Streaming

September 2025

Download Apache Spark Streaming product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Spark Streaming

Overview

Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.

Amazon Web Services (AWS)

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

Apache

Sample Customers

Zillow, Netflix, Sonos

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

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

July 2025

Free Report: Amazon Kinesis vs. Apache Spark Streaming

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