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 #5 with an average rating of 8.4, while Apache is ranked #10 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

3,206 Views
3,206 Comparison Views

90% willing to recommend

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

2,453 Views
2,453 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: June 2026).

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

June 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

5th

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

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 July 2026, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 4.0%, down from 7.9% compared to the previous year. The mindshare of Apache Spark Streaming is 4.7%, up from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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

"The solution works well in rather sizable environments."

"The management and analytics are valuable features."

"The scalability is pretty good."

"We have seen a return on our investment with Amazon Kinesis, as we are able to process data without any issue and it is our solution for ingesting data in other databases, such as Snowflake."

"The solution's technical support is flawless."

"Kinesis is a fully managed program streaming application. You can manage any infrastructure. It is also scalable. Kinesis can handle any amount of data streaming and process data from hundreds, thousands of processes in every source with very low latency."

"The Kinesis VideoStream and DataStream are the most important features."

"Amazon Kinesis also provides us with plenty of flexibility."

More Amazon Kinesis pros

"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 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."

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

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

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"The solution is very stable and reliable."

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

"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."

"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."

"In general, the pain point for us was that once the data gets into Kinesis there is no way for us to understand what's happening because Kinesis divides everything into shards. So if we wanted to understand what's happening with a particular shard, whether it is published or not, we could not. Even with the logs, if we want to have some kind of logging it is in the shard."

"The solution has a two-minute maximum time delay for live streaming, which could be reduced."

"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."

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

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

"Lacks first in, first out queuing."

More Amazon Kinesis cons

"Integrating event-level streaming capabilities could be beneficial."

"The debugging aspect could use some improvement."

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

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

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

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

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

"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

"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."

"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 fee is based on the number of hours the service is running."

"Under $1,000 per month."

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

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

More Amazon Kinesis pricing and cost advice

"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."

"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

15%

Computer Software Company

11%

Manufacturing Company

Construction Company

Financial Services Firm

18%

Comms Service Provider

Outsourcing Company

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...

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Compared 10% of the time

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Compared 7% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 7% of the time

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Compared 6% of the time

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Product Reports

Buyer's Guide

Amazon Kinesis

July 2026

Download Amazon Kinesis product report

Buyer's Guide

Apache Spark Streaming

July 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

June 2026

Free Report: Amazon Kinesis vs. Apache Spark Streaming

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