Amazon Kinesis Reviews

Name: Amazon Kinesis
Brand: Amazon Web Services (AWS)
Rating: 4.0 (28 reviews)

Vendor: Amazon Web Services (AWS)

4.0 out of 5

28 reviews
89% willing to recommend

118 followers

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What is Amazon Kinesis?

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.

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Amazon Kinesis is the #2 ranked solution in Streaming Analytics tools. PeerSpot users give Amazon Kinesis an average rating of 8.0 out of 10. Amazon Kinesis is most commonly compared to Databricks: Amazon Kinesis vs Databricks. Amazon Kinesis is popular among the large enterprise segment, accounting for 67% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a computer software company, accounting for 18% of all views.

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Featured Amazon Kinesis reviews

Rajni Kumar Jha

Senior Software Engineer at JPMorgan Chase & Co.

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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Prabin Silwal

Senior Engineering Consultant at ASSURANCE IQ, INC.

I am not exactly sure about where improvements are needed in the tool. When I was working on the tool, it was very scalable, and the only thing we needed in our company was temporary streaming stuff that could work well. We didn't want to set up our own Kafka, other queues, or processing systems. As it is a cloud tool, it is easy for us to use the tool, and it satisfies all our requirements. Maybe for the other cases, if we need, then it may need some improvements. The tool satisfies our particular needs. Currently, the pipeline setup is very simple. For our particular use cases, it is because we just want to get the data and send it to the different data lakes or some logging system. Previously, we also used Amazon Kinesis to log those to Splunk, and later on, we removed Splunk and transferred that to Datadog. For our use cases, I don't want any new features in the tool. Amazon Kinesis' use case is for collecting, processing, and analyzing. If anything can be added to the tool, then I feel one should be able to use the same kind of tool so that everything is there in the product, like an alert system, and so that one can analyze, make a query, and do sourcing from the solution itself rather than using other logging and monitoring systems. The tool should focus on having an alert system rather than having to use a third-party solution. We can just get the data over Amazon Kinesis, and we can directly use all the benefits of current analytical tools, like in the areas involving BI, Looker, and Tableau. One would not need to buy the aforementioned tools, and we can just get started with Amazon Kinesis.

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Sunil Morya

Consultant at Tata Consultancy

The solution has the capacity to store the data anywhere from one day to a week and provides limitless storage for us. The data is time-stamped, and the data is in sequence, so we don't need to maintain the sequence or order of the data, and multiple consumers can reference the data in the Kinesis Data Streams simultaneously. Using the same data one application can perform Task A, and another application can perform Task B. The Kinesis Data Stream is integrated with Amazon CloudWatch where we can monitor all the requests of who wants to read information, what kind of APIs are being requested, what errors there are, who is producing them, and who are the data consumers. All the information is logged in one spot and this allows us to identify the problematic points easily. The solution allows us to apply the security so the consumers can have access, based on the subscription they have. Amazon Kinesis has a fan-out feature that allows us to increase the throughput when the number of consumers increases, instead of having to pull the data from the consumer side the information is posted by the solution itself.

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Amazon Kinesis mindshare

As of May 2025, the mindshare of Amazon Kinesis in the Streaming Analytics category stands at 8.3%, down from 13.6% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Streaming Analytics

PeerResearch reports based on Amazon Kinesis reviews

Type	Title	Date
Category	Streaming Analytics	May 13, 2025	Download
Product	Reviews, tips, and advice from real users	May 13, 2025	Download
Comparison	Amazon Kinesis vs Databricks	May 13, 2025	Download
Comparison	Amazon Kinesis vs Azure Stream Analytics	May 13, 2025	Download
Comparison	Amazon Kinesis vs Confluent	May 13, 2025	Download

Title	Rating	Mindshare	Recommending
Databricks	4.1	14.6%	96%	89 interviews Add to research
Confluent	4.1	8.2%	95%	23 interviews Add to research

Valuable Features

Amazon Kinesis offers real-time data processing and seamless integration with AWS services, featuring efficient data streams and Firehose for easy data ingestion. Users benefit from its scalability and easy setup, avoiding infrastructure management. With features like multi-partition, replay capability, and Lambda integration, they ensure robust data handling. Kinesis prioritizes order and fault tolerance, providing valuable insights and analytics. Its quick deployment and flexibility make it a preferred choice for many organizations.

"Amazon Kinesis is easy to get started with, provides good documentation, and has a multilang daemon interface that makes it programming-language agnostic."
"The Kinesis VideoStream and DataStream are the most important features."
"There is no problem with the tool's stability."

Room for Improvement

Amazon Kinesis could simplify sharding, improve default limits, and enhance aggregation capabilities. It should offer better documentation and support for sharding strategies and provide easier data stream management. Integration with Lambda is limited and there are constraints with file sizes and retention periods. Pricing and automation could be more competitive. Users suggest adding machine learning features, improving video stream delays, and automating the scaling of streams. Enhanced visibility into data processing would be beneficial.

"Amazon Kinesis could improve its pricing to be more competitive, especially for large volumes."
"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."
"There are some kind of hard limits on Amazon Kinesis, and if you hit that, then you will get the throughput exceed error."

ROI

Amazon Kinesis has evidently enhanced ROI. Users report substantial savings, reducing data ingestion costs by 60-70%. Its integration with AWS simplifies security and monitoring, offering better architecture compared to alternatives like Kafka. Critical tasks benefit from its analytics, offering timely issue responses. It allows seamless data processing into databases like Snowflake without complications. Running workloads with Kinesis proves financially advantageous over EC2 and ensures viable investment returns, critical for operational success.

Pricing

Enterprise users find Amazon Kinesis pricing generally fair, with costs typically under $1,000 monthly. While Kinesis Data Stream and Firehose offer good value, Kinesis Analytics can be expensive. Pricing depends on factors like data volume, use cases, and service activation. Comparatively, it is moderately priced against competitors, yet scaling can raise expenses. Users appreciate the convenience and managed services provided, but costs vary based on usage patterns and geographic access limitations.

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

Popular Use Cases

Amazon Kinesis users primarily utilize it for streaming architecture, capturing various data types like IoT, sales, and Twitter data. They implement it in their data pipelines to manage large volumes and for near real-time processing. Kinesis Firehose aids in moving data to destinations like S3 and Snowflake. Many rely on Kinesis for its scalability, reliability, and ability to handle diverse data sources, using it extensively in industries such as eCommerce, gaming, and media streaming.

Service and Support

Amazon Kinesis' customer service and support experience varies. Some users find AWS support quick and effective, resolving issues swiftly. Others indicate technical support could improve, often redirecting to documentation. Support satisfaction often depends on the user's relationship with AWS. Some receive valuable assistance through technical advisors or service quota requests, while others struggle with unresolved issues and long response times. Generally, experiences range from excellent to needing improvement.

Deployment

Users describe Amazon Kinesis's initial setup as straightforward due to its managed service nature and intuitive interface. Configuration mainly involves naming streams, setting buffering times, and integrating with Amazon S3. Deployments range from minutes to weeks depending on complexity, though simpler implementations are often quick and assisted by comprehensive documentation. Methods like CloudFormation and Terraform are commonly used, and though some find minor challenges, many appreciate the ease compared to alternatives.

Scalability

Amazon Kinesis is described as highly scalable, handling large data volumes efficiently with automatic scaling. Organizations deploy it widely, benefiting from minimal latency and easy scaling, even for complex real-time processing. Some faced scalability challenges requiring additional configuration, but others found it managed massive updates effortlessly. Shards enable flexibility to handle spikes in data flow, although financial and operational considerations are noted. Alternatives like Kafka are sometimes preferred for specific use cases with intense demands.

Stability

Amazon Kinesis is generally stable with occasional slowdowns. Most users report high stability, rating it between nine and ten out of ten, though some experience delays due to configuration mistakes or capacity limits. Users appreciate its fault tolerance, allowing data retrieval from specific points if a failure occurs. While it can be overwhelmed under certain conditions, most users remain satisfied with its performance, noting minimal stability issues when configured correctly.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

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