Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Condense, developed by Zeliot, is a comprehensive real-time data streaming solution that integrates fully managed Apache Kafka with all necessary components to create and operate event-driven applications seamlessly. By providing a unified platform, teams can avoid the hassle of integrating multiple tools, as Condense offers managed Kafka clusters, customizable transforms, deployment pipelines, and monitoring capabilities, all securely hosted within their chosen cloud environment (AWS, Azure, or GCP) to ensure data remains protected. Additionally, Condense features Vapr, an autonomous AI supervisor designed to manage specialized agents for tasks related to Kafka, Kubernetes, Grafana, and coding, significantly reducing the operational burden associated with maintaining streaming infrastructure. Proven in demanding production settings, Condense effectively supports various industries such as connected vehicle platforms, automotive OEM telematics, electric vehicle fleets, logistics, travel and hospitality, healthcare, and fintech, processing billions of events daily and showcasing its reliability and efficiency across diverse applications. The platform's ability to streamline event-driven processes while ensuring security makes it an invaluable asset for organizations aiming to harness real-time data effectively.
Description
Apache Flink serves as a powerful framework and distributed processing engine tailored for executing stateful computations on both unbounded and bounded data streams. It has been engineered to operate seamlessly across various cluster environments, delivering computations with impressive in-memory speed and scalability. Data of all types is generated as a continuous stream of events, encompassing credit card transactions, sensor data, machine logs, and user actions on websites or mobile apps. The capabilities of Apache Flink shine particularly when handling both unbounded and bounded data sets. Its precise management of time and state allows Flink’s runtime to support a wide range of applications operating on unbounded streams. For bounded streams, Flink employs specialized algorithms and data structures optimized for fixed-size data sets, ensuring remarkable performance. Furthermore, Flink is adept at integrating with all previously mentioned resource managers, enhancing its versatility in various computing environments. This makes Flink a valuable tool for developers seeking efficient and reliable stream processing solutions.
API Access
Has API
No
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Alibaba Log Service
No
Apache Doris
No
Apache Iceberg
No
Apache Mesos
No
DeltaStream
No
E-MapReduce
No
Foundational
No
Gable
No
GlassFlow
No
Hadoop
No
Integrations
Alibaba Log Service
Yes
Apache Doris
Yes
Apache Iceberg
Yes
Apache Mesos
Yes
DeltaStream
Yes
E-MapReduce
Yes
Foundational
Yes
Gable
Yes
GlassFlow
Yes
Hadoop
Yes
Pricing Details
$40/month
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
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
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Zeliot
Founded
2018
Country
India
Website
www.zeliot.in
Vendor Details
Company Name
Apache Software Foundation
Founded
1999
Country
United States
Website
flink.apache.org
Product Features
Product Features
Streaming Analytics
Data Enrichment
No
Data Wrangling / Data Prep
No
Multiple Data Source Support
No
Process Automation
No
Real-time Analysis / Reporting
No
Visualization Dashboards
No