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 7.9, while Apache is ranked #10 with an average rating of 8.2. Amazon Web Services (AWS) holds a 9.1% mindshare in SA, compared to Apache’s 2.9% mindshare. Additionally, 89% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 91% of Apache users who would recommend it.

Amazon Kinesis

Read 27 Amazon Kinesis reviews

6,323 Views
4,745 Comparison Views

89% willing to recommend

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

1,848 Views
1,347 Comparison Views

91% 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: January 2025).

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

January 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

8.0

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of March 2025, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 9.1%, down from 14.2% compared to the previous year. The mindshare of Apache Spark Streaming is 2.9%, down from 3.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

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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AbhishekGupta

Engineering Leader at Walmart

Easy integration, beneficial auto-scaling, and good open-sourced support community

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. Apache Spark Streaming does not have auto-tuning. A customer needs to invest a lot, in terms of management and maintenance.

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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

"The management and analytics are valuable features."

"I have worked in companies that build tools in-house. They face scaling challenges."

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

"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."

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

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

"Amazon Kinesis has improved our ROI."

"The solution's technical support is flawless."

More Amazon Kinesis pros

"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."

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

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

"The solution is better than average and some of the valuable features include efficiency and stability."

"As an open-source solution, using it is basically free."

More Apache Spark Streaming pros

Cons

"One area for improvement in the solution is the file size limitation of 10 Mb. My company works with files with a larger file size. The batch size and throughput also need improvement in Amazon Kinesis."

"The price is not much cheaper. So, there is room for improvement in the pricing."

"Amazon Kinesis should improve its limits."

"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."

"Amazon Kinesis involved a more complex setup and configuration than Azure Event Hub."

"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."

"There are certain shortcomings in the machine learning capacity offered by the product, making it an area where improvements are required."

"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."

More Amazon Kinesis cons

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

"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."

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

"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."

"In terms of improvement, the UI could be better."

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

"The solution itself could be easier to use."

More Apache Spark Streaming cons

Pricing and Cost Advice

"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."

"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."

"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."

"Under $1,000 per month."

"The tool's entry price is cheap. However, pricing increases with data volume."

"The tool's pricing is cheap."

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

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."

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

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

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

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Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

18%

Manufacturing Company

Retailer

Financial Services Firm

26%

Computer Software Company

20%

Manufacturing Company

University

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.

See all answers

What is your experience regarding pricing and costs for Amazon Kinesis?

Amazon Kinesis is moderately priced. In comparison with other competitors, it is fairly priced, however, if they reduced the price a little, it could add more value to customers.

See all answers

What needs improvement with Amazon Kinesis?

I do not see any scope for improvement as it does what it is supposed to do. No changes are required. Since it's predominantly a back-end service, any end-user isn't going to interact with it direc...

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?

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...

See all answers

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.

See all answers

Comparisons

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Compared 29% of the time

Azure Stream Analytics vs Amazon Kinesis

Compared 23% of the time

Apache Flink vs Amazon Kinesis

Compared 16% of the time

Amazon MSK vs Amazon Kinesis

Compared 11% of the time

Redpanda vs Amazon Kinesis

Compared 2% of the time

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Compared 21% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 20% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 16% of the time

Starburst Enterprise vs Apache Spark Streaming

Compared 10% of the time

Apache Flink vs Apache Spark Streaming

Compared 9% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Amazon Kinesis

March 2025

Download Amazon Kinesis product report

Buyer's Guide

Streaming Analytics

February 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

January 2025

Free Report: Amazon Kinesis vs. Apache Spark Streaming

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