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.
$0.01
per GB data ingested / consumed
IBM Event Automation
Score 8.1 out of 10
N/A
IBM Event Automation enables businesses to accelerate their event-driven efforts. The event streams, event endpoint management and event processing capabilities help lay the foundation of an event-driven architecture for unlocking the value of events.
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Pricing
Amazon Kinesis
IBM Event Automation
Editions & Modules
Amazon Kinesis Video Streams
$0.00850
per GB data ingested / consumed
Amazon Kinesis Data Streams
$0.04
per hour per stream
Amazon Kinesis Data Analytics
$0.11
per hour
Amazon Kinesis Data Firehose
tiered pricing starting at $0.029
per month first 500 TB ingested
No answers on this topic
Offerings
Pricing Offerings
Amazon Kinesis
IBM Event Automation
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Amazon Kinesis
IBM Event Automation
Features
Amazon Kinesis
IBM Event Automation
Streaming Analytics
Comparison of Streaming Analytics features of Product A and Product B
Amazon Kinesis is a great replacement for Kafka and it works better whenever the components of the solution are AWS based. Best if extended fan-out is not required, but still price-performance ratio is very good for simplifying maintenance.
I would go with a different option if the systems to be connected are legacy, for instance in the case of traditional messaging clients.
IBM Event Streams is well suited for companies developing event driven Microservices. One of the biggest challenger with microservices is that your data gets distributed into little silos - event streaming (or better known as event sourcing) allows you to get a central source of truth in your event store. We are taking this approach with IBM Event Streams and it is well suited for building an event streaming / sourcing architecture.
The product was very user friendly and extremely easy to get started with. The documentation is excellent and the free tier makes it very easy to get started with without having to make deep or long term financial commitments.
The documentation was confusing and lacked examples. The streams suddenly stopped working with no explanation and there was no information in the logs. All these were more difficult when dealing with enhanced fan-out. In fact, we were about to abort the usage of Kinesis due to a misunderstanding with enhanced fan-out.
I met with the support team and they have deep technical and development understanding of the needs and the problems which IBM Event Streams addresses. If you are looking for a product backed by a highly technical support team then IBM Event Streams is probably the best choice. I was specifically impressed by the level of technical understanding my support team demonstrated.
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.
In Event Streams, applications send data by creating a message and sending it to a topic. To receive messages, applications subscribe to a topic. High availability and reliability. Event Streams offers a highly available and reliable Apache Kafka service running on IBM Cloud. Event Streams. Event Streams stores three replicas of your data to ensure the highest level of resilience across three availability zones.
In using downstreams, the minimal features and the rate of releases were slow, makes us feel that there's no upgrades and other than that there's poor marketing of the product.
The adoption around the service is low, requires focused marketing.
Lack of visibility into topic depth , Monitoring capabilities