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 #11 with an average rating of 8.3, while Apache is ranked #9 with an average rating of 7.9. Amazon Web Services (AWS) holds a 4.0% mindshare in SA, compared to Apache’s 4.7% mindshare. Additionally, 90% 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 29 Amazon Kinesis reviews

3,300 Views
3,300 Comparison Views

90% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,655 Views
2,655 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: August 2026).

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

August 2026

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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

11th

Average Rating

8.0

Reviews Sentiment

7.0

Number of Reviews

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

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 4.0%, down from 7.6% compared to the previous year. The mindshare of Apache Spark Streaming is 4.7%, up from 3.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics Mindshare Distribution
Product	Mindshare (%)
Apache Spark Streaming	4.7%
Amazon Kinesis	4.0%
Other	91.3%

Streaming Analytics

Featured Reviews

reviewer1480695

Director of Software Development at a tech vendor with 10,001+ employees

Has enabled real-time processing of critical event streams with seamless cloud integration

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 us. The cost is a primary hindrance. That's why we are not using it widely. For our critical pipeline we are using it, but after that we are putting it in an S3 bucket. Other pipelines directly put the events in an S3 bucket and then process from there. There is no lack of functions in Amazon Kinesis. Functionality-wise, we feel it's complete. The cost aspect is what we are really concerned about.

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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.

Read full review

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 feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature."

"The scalability is pretty good."

"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."

"I like the ease of use and how we can quickly get the configurations done, making it pretty straightforward and stable."

"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 integration capabilities of the product are good."

"The integration between Amazon Kinesis and Lambda helps us significantly."

More Amazon Kinesis pros

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

"It is the most scalable tool that I have seen before."

"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."

"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 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."

More Apache Spark Streaming pros

Cons

"Kinesis is good for Amazon Cloud but not as suitable for other cloud vendors."

"When we had some of those slow downs, we used AWS support and I can't say that we had a great experience and they resolved the issues, but they looked into some of the flow downs and ultimately we just decided there was nothing we could do."

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

"Amazon Kinesis should improve its limits."

"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 default limit that they have, which at the moment is 5,000 records per second seems too low."

"AI processing or cleaning up data would be nice since I don't think it is a feature in Amazon Kinesis right now."

"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."

More Amazon Kinesis cons

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

"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."

"The downside is when you have this the other way around in the columns, it becomes really hard to use."

"The initial setup is quite complex."

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

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

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

More Apache Spark Streaming cons

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."

"Under $1,000 per month."

"The solution's pricing is fair."

"The product falls on a bit of an expensive side."

"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."

"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."

"Amazon Kinesis is an expensive solution."

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."

"People pay for Apache Spark Streaming as a service."

"I was using the open-source community version, which was self-hosted."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

10%

Manufacturing Company

Construction Company

Financial Services Firm

16%

Outsourcing Company

Comms Service Provider

Marketing Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

Questions from the Community

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.

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What needs improvement with Amazon Kinesis?

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What is your primary use case for Amazon Kinesis?

We use Amazon Kinesis for stream processing. We get events from on-premise devices to the cloud. We get many device events and we have to process these events that are coming from the devices. To p...

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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...

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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 ...

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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...

See all answers

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Compared 10% of the time

PubSub+ Platform vs Amazon Kinesis

Compared 5% of the time

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Compared 14% of the time

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Compared 11% of the time

Databricks vs Apache Spark Streaming

Compared 9% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 6% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Amazon Kinesis

August 2026

Download Amazon Kinesis product report

Buyer's Guide

Apache Spark Streaming

August 2026

Download Apache Spark Streaming product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Spark Streaming

Overview

Amazon Kinesis provides real-time data streaming with seamless AWS integration, ideal for analytics, data transformation, and external customer feeds. It offers cost-effective data management with high throughput and low latency, supporting multiple programming languages.

Amazon Kinesis enables organizations to manage real-time data streams efficiently. Its integration with AWS ensures seamless setup and operation, while features like auto-scaling and fault tolerance make it reliable for diverse data sources such as IoT devices and server logs. The platform's ability to handle large-scale event-driven systems and dynamic workloads makes it suitable for complex streaming architectures. Despite some challenges with costs and setup complexity, Kinesis remains a popular choice for its efficient data management and processing capabilities.

What are the key features of Amazon Kinesis?

Real-time Data Streaming: Enables live data processing and analytics.
Seamless AWS Integration: Ensures smooth operation with AWS services.
High Throughput: Consistent 2Mbps rate supports large volumes of data.
Auto-scaling: Automatically adjusts to data load.
Fault Tolerance: Maintains system reliability and data integrity.
Lambda Function Processing: Processes data efficiently using Lambda.

What benefits or ROI should users expect?

Scalable Data Management: Handles data influx effortlessly.
Efficiency: Streamlines data flows without impacting performance.
Cost-effectiveness: Delivers value for budget-conscious users.
Low Latency: Provides real-time insights and reactions.
Multi-language Support: Accommodates various development needs.

In industries such as IoT, finance, and entertainment, Amazon Kinesis facilitates the real-time ingestion and processing of data streams. It connects seamlessly to data lakes and warehouses, enabling businesses to harness data-driven insights without performance loss. This capability is essential for managing dynamic workloads and large-scale event systems. By supporting tools like KDS, Firehose, and Video Streams, Kinesis empowers organizations to respond quickly to changing data environments, enhancing operational effectiveness across different sectors.

Amazon Web Services (AWS)

Apache Spark Streaming efficiently processes real-time data with features like micro-batching and native Python support. It's scalable and integrates with many services, ideal for reducing data latency and enabling real-time analytics across industries.

Apache Spark Streaming is a powerful tool for real-time data processing and analytics, offering support for multiple languages and robust integration capabilities. Its open-source nature, combined with features like checkpointing and watermarking, makes it a reliable choice for managing data streams with low latency. However, it faces challenges with Kubernetes deployments and requires improvements in memory management and latency. The installation process and handling of structured and unstructured data also present complexities. Despite these challenges, it's heavily utilized in building data pipelines and leveraging machine learning algorithms.

What are Apache Spark Streaming's key features?

Native Python Support: Efficient processing with Python language integration.
Micro-Batching: Handles streams in small batches for real-time processing.
Real-Time Analytics: Enables instant data insights.
Scalability: Adapts to varying data loads.
Low Latency: Processes data with minimal delays.

What benefits or ROI should users expect?

Efficiency: Streamlined real-time data processing.
Reliability: Consistent performance across tasks.
Integration: Seamless connection with other services.
Cost Optimization: Reduces processing expenses over time.

In industries like healthcare, telecommunications, and logistics, Apache Spark Streaming is implemented for real-time data processing and machine learning. It aids in predictive maintenance, anomaly detection, and fraud detection by reducing data latency with comprehensive analytics. Organizations frequently use it alongside Kafka and cloud storage solutions to enhance GIS, predictive analytics, and Customer 360 profiling.

Apache

Sample Customers

Zillow, Netflix, Sonos

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

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

August 2026

Free Report: Amazon Kinesis vs. Apache Spark Streaming

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