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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
The Stackable data platform was crafted with a focus on flexibility and openness. It offers a carefully selected range of top-notch open source data applications, including Apache Kafka, OpenSearch, Trino, and Apache Spark. Unlike many competitors that either promote their proprietary solutions or enhance vendor dependence, Stackable embraces a more innovative strategy. All data applications are designed to integrate effortlessly and can be added or removed with remarkable speed. Built on Kubernetes, it is capable of operating in any environment, whether on-premises or in the cloud. To initiate your first Stackable data platform, all you require is stackablectl along with a Kubernetes cluster. In just a few minutes, you will be poised to begin working with your data. You can set up your one-line startup command right here. Much like kubectl, stackablectl is tailored for seamless interaction with the Stackable Data Platform. Utilize this command line tool for deploying and managing stackable data applications on Kubernetes. With stackablectl, you have the ability to create, delete, and update components efficiently, ensuring a smooth operational experience for your data management needs. The versatility and ease of use make it an excellent choice for developers and data engineers alike.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Apache Airflow
Apache Druid
Apache HBase
Apache Hive
Apache Iceberg
Apache Kafka
Apache NiFi
Apache Spark
Apache ZooKeeper
Docker
Integrations
Apache Airflow
Apache Druid
Apache HBase
Apache Hive
Apache Iceberg
Apache Kafka
Apache NiFi
Apache Spark
Apache ZooKeeper
Docker
Pricing Details
$40/month
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Zeliot
Founded
2018
Country
India
Website
www.zeliot.in
Vendor Details
Company Name
Stackable
Founded
2020
Country
Germany
Website
stackable.tech/
Product Features
Product Features
Data Management
Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge
Data Warehouse
Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge
Alternatives
No Alternatives