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Description
Apache Kafka® is a robust, open-source platform designed for distributed streaming. It can scale production environments to accommodate up to a thousand brokers, handling trillions of messages daily and managing petabytes of data with hundreds of thousands of partitions. The system allows for elastic growth and reduction of both storage and processing capabilities. Furthermore, it enables efficient cluster expansion across availability zones or facilitates the interconnection of distinct clusters across various geographic locations. Users can process event streams through features such as joins, aggregations, filters, transformations, and more, all while utilizing event-time and exactly-once processing guarantees. Kafka's built-in Connect interface seamlessly integrates with a wide range of event sources and sinks, including Postgres, JMS, Elasticsearch, AWS S3, among others. Additionally, developers can read, write, and manipulate event streams using a diverse selection of programming languages, enhancing the platform's versatility and accessibility. This extensive support for various integrations and programming environments makes Kafka a powerful tool for modern data architectures.
Description
IBM Event Streams is a comprehensive event streaming service based on Apache Kafka, aimed at assisting businesses in managing and reacting to real-time data flows. It offers features such as machine learning integration, high availability, and secure deployment in the cloud, empowering organizations to develop smart applications that respond to events in real time. The platform is designed to accommodate multi-cloud infrastructures, disaster recovery options, and geo-replication, making it particularly suitable for critical operational tasks. By facilitating the construction and scaling of real-time, event-driven solutions, IBM Event Streams ensures that data is processed with speed and efficiency, ultimately enhancing business agility and responsiveness. As a result, organizations can harness the power of real-time data to drive innovation and improve decision-making processes.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Waterstream
Yes
meshIQ
Yes
100x
Yes
AccelByte
Yes
Acryl Data
Yes
Akira AI
Yes
Apache Storm
Yes
Azure Spring Apps
Yes
Baffle
Yes
Diamanti
Yes
Integrations
Waterstream
Yes
meshIQ
Yes
100x
No
AccelByte
No
Acryl Data
No
Akira AI
No
Apache Storm
No
Azure Spring Apps
No
Baffle
No
Diamanti
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
The Apache Software Foundation
Founded
1999
Country
United States
Website
kafka.apache.org
Vendor Details
Company Name
IBM
Founded
1911
Country
United States
Website
www.ibm.com/products/event-streams
Product Features
iPaaS
AI / Machine Learning
No
Cloud Data Integration
No
Dashboard
No
Data Quality Control
No
Data Security
No
Drag & Drop
No
Embedded iPaaS
No
Integration Management
No
Pre-Built Connectors
No
White Label
No
Workflow Management
No
Message Queue
Asynchronous Communications Protocol
Yes
Data Error Reduction
Yes
Message Encryption
Yes
On-Premise Installation
Yes
Roles / Permissions
Yes
Storage / Retrieval / Deletion
Yes
System Decoupling
Yes
Product Features
Message Queue
Asynchronous Communications Protocol
No
Data Error Reduction
No
Message Encryption
No
On-Premise Installation
No
Roles / Permissions
No
Storage / Retrieval / Deletion
No
System Decoupling
No
