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 #9 with an average rating of 8.0. Amazon Web Services (AWS) holds a 10.6% mindshare in SA, compared to Apache’s 3.7% mindshare. Additionally, 88% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 90% of Apache users who would recommend it.

Amazon Kinesis

Read 26 Amazon Kinesis reviews

8,771 Views
6,492 Comparison Views

88% willing to recommend

Apache Spark Streaming

Read 10 Apache Spark Streaming reviews

2,712 Views
1,973 Comparison Views

90% willing to recommend

Amazon Kinesis

Apache Spark Streaming

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Amazon Kinesis and Apache Spark Streaming based on real PeerSpot user reviews.

Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon Kinesis vs. Apache Spark Streaming Report (Updated: October 2024).

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

October 2024

Download the complete report

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Categories and Ranking

Amazon Kinesis

Ranking in Streaming Analytics

2nd

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Apache Spark Streaming

Ranking in Streaming Analytics

9th

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Amazon Kinesis is 10.6%, down from 15.4% compared to the previous year. The mindshare of Apache Spark Streaming is 3.7%, down from 4.7% 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.

Apr 3, 2024

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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Oscar Estorach

Chief Data-strategist and Director at Theworkshop.es

Jan 25, 2024

Versatile and flexible when dealing with large-scale data streams

What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.

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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

"Great auto-scaling, auto-sharing, and auto-correction features."

"The solution's technical support is flawless."

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

"Everything is hosted and simple."

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

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

"What I like about Amazon Kinesis is that it's very effective for small businesses. It's a well-managed solution with excellent reporting. Amazon Kinesis is also easy to use, and even a novice developer can work with it, versus Apache Kafka, which requires expertise."

"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."

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."

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

"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."

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

"The solution is very stable and reliable."

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

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

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

More Apache Spark Streaming pros

Cons

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

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

"If there were better documentation on optimal sharding strategies then it would be helpful."

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

"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."

"Kinesis can be expensive, especially when dealing with large volumes of data."

"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."

"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

"The solution itself could be easier to use."

"The initial setup is quite complex."

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

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

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

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

"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."

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

More Apache Spark Streaming cons

Pricing and Cost Advice

"The solution's pricing is fair."

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

"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."

"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."

"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 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."

"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."

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

"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."

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Top Industries

By visitors reading reviews

Computer Software Company

18%

Financial Services Firm

18%

Manufacturing Company

10%

Retailer

Financial Services Firm

23%

Computer Software Company

20%

University

Manufacturing Company

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?

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.

See all answers

What needs improvement with Amazon Kinesis?

There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error. We have to deal with and reduce how many consumers are hitting Amazon Kines...

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?

The product's event handling capabilities, particularly compared to Kaspersky, need improvement. Integrating event-level streaming capabilities could be beneficial. This aligns with the idea of exp...

See all answers

What is your primary use case for Apache Spark Streaming?

I've used it more for ETL. It's useful for creating data pipelines, streaming datasets, generating synthetic data, synchronizing data, creating data lakes, and loading and unloading data is fast an...

See all answers

Comparisons

Azure Stream Analytics vs Amazon Kinesis

Compared 32% of the time

Confluent vs Amazon Kinesis

Compared 23% of the time

Amazon MSK vs Amazon Kinesis

Compared 15% of the time

Apache Flink vs Amazon Kinesis

Compared 12% of the time

Google Cloud Dataflow vs Amazon Kinesis

Compared 5% of the time

More Amazon Kinesis Competitors

Spring Cloud Data Flow vs Apache Spark Streaming

Compared 21% of the time

Apache Pulsar vs Apache Spark Streaming

Compared 16% of the time

Azure Stream Analytics vs Apache Spark Streaming

Compared 16% of the time

Apache Flink vs Apache Spark Streaming

Compared 7% of the time

SAS Event Stream Processing vs Apache Spark Streaming

Compared 6% of the time

More Apache Spark Streaming Competitors

Product Reports

Buyer's Guide

Amazon Kinesis

November 2024

Download Amazon Kinesis product report

Buyer's Guide

Streaming Analytics

October 2024

Download Apache Spark Streaming product report

Also Known As

Amazon AWS Kinesis, AWS Kinesis, Kinesis

Spark Streaming

Learn More

Amazon Web Services (AWS)

Apache

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.

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

Sample Customers

Zillow, Netflix, Sonos

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

Buyer's Guide

Amazon Kinesis vs. Apache Spark Streaming

October 2024

Free Report: Amazon Kinesis vs. Apache Spark Streaming

Find out what your peers are saying about Amazon Kinesis vs. Apache Spark Streaming and other solutions. Updated: October 2024.

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