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Amazon Kinesis

Amazon Kinesis

Overview

What is Amazon Kinesis?

Amazon Kinesis is a streaming analytics suite for data intake from video or other disparate sources and applying analytics for machine learning (ML) and business intelligence.

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Recent Reviews
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Pricing

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Amazon Kinesis Video Streams

$0.00850

Cloud
per GB data ingested / consumed

Amazon Kinesis Data Streams

$0.04

Cloud
per hour per stream

Amazon Kinesis Data Analytics

$0.11

Cloud
per hour

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Details

What is Amazon Kinesis?

Amazon Kinesis Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
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Comparisons

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Reviews and Ratings

(24)

Attribute Ratings

Reviews

(1-2 of 2)
Companies can't remove reviews or game the system. Here's why
Score 10 out of 10
Vetted Review
Verified User
Incentivized
Amazon Kinesis is being used to stream telemetry data from millions of connected devices on the field - helping to drive command and control use cases and also sensor data for performance monitoring. Our volume is billions of messages per day, real-time, with aggressive requirements on response and processing time. All messages are stored in a big data structure and also two-way communication with a mobile app.
  • Processing huge loads of data
  • Integrating well with IoT Platform on Amazon
  • Integration with overall AWS Ecosystem
  • Scalability
  • Improve integration with AWS Lambda
  • Some duplicate records coming from the stream
Perfect for real-time data processing and streaming. Also, there's no need for any specific setup - you just start using it immediately and it easily integrates with the rest of AWS capabilities (like Redshift), although integration with Lambda could be better. You can make your overall analytics landscape way simpler with Kineses even if you have non-Amazon solutions like Tableau. It all integrates really well!
Streaming Analytics (6)
100%
10.0
Real-Time Data Analysis
100%
10.0
Data Ingestion from Multiple Data Sources
100%
10.0
Low Latency
100%
10.0
Integrated Development Tools
100%
10.0
Data wrangling and preparation
100%
10.0
Linear Scale-Out
100%
10.0
  • Significantly reduced the cost of our IoT platform, compared to other Cloud providers.
  • Better user experience.
  • Enabled real "real-time analytics."
The main benefit was around set up - incredibly easy to just start using Kinesis. Kinesis is a real-time data processing platform, while Kafka is more of a message queue system. If you only need a message queue from a limited source, Kafka may do the job. More complex use cases, with low latency, higher volume of data, real time decisions and integration with multiple sources and destination at a decent price, Kinesis is better.
When needed, Amazon engineers are accessible and provide a fast response. Documentation is good and has been improving a lot over the past couple of years.
Score 1 out of 10
Vetted Review
Verified User
Incentivized
We currently use Kinesis as a trigger for Amazon Lambda functions, however we're working at switching back to SQS since Lambda now works with SQS directly. We simply used Kinesis as a temporary method of plumbing requests from one Lambda function to another.

We also use it as a byproduct of using DynamoDB Streams, but only in so far as that's used under-the-hood to link changes from DynamoDB to a Lambda trigger.
  • Link DynamoDB change events directly to Lambda
  • Fast streaming of events
  • Easy to "tail" changes/latest events
  • Not a queue system, so little visibility into "backlog" if there is any
  • Confusing terminology to make sure events aren't missed
  • Sometimes didn't seem to trigger Lambda functions, or dropped events when a lot came in
It's great for big data applications, where it's not as important to make sure each event is processed, but you're more looking for overall analytics and speed is more important than absolute detail. It's added quite a few features like analytics and other tools specifically designed around big data, but it's not the same thing as a queue system.

Originally we were forced into using Kinesis since Lambda didn't support SQS directly, but now that there is native SQS support for Lambda, we'll be switching almost all of our implementation over to use that instead. Kinesis has its strengths, but monitoring and error retry logic is not one of those.
  • Caused us to need to re-engineer some basic re-try logic
  • Caused us to drop some content without knowing it
  • Made monitoring much more difficult
  • We eventually switched back to SQS because Kinesis is not the same as a Queue system
  • Amazon Simple Queue Service (SQS)
Actually we didn't select Kinesis, we were forced into using it because SQS wasn't yet supported by Lambda. Unlike Kinesis, SQS supports both FIFO and standard queues which let us control order of events processed, as well as handle retry logic, failover logic, and set up CloudWatch Alarms when we have too many events in our backlog. It also lets us know when something didn't process properly and we can alert DevOps to that issue and/or retry the processes automatically.

With SQS, we have much more visibility then we ever did with Kinesis. This is very important to our use cases, as we aren't processing millions of events per second, but for us every event is very important.
Amazon Simple Queue Service (SQS), Amazon DynamoDB, AWS Lambda, Amazon API Gateway
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