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Description
A framework for distributed data integration that streamlines essential functions of Big Data integration, including data ingestion, replication, organization, and lifecycle management, is designed for both streaming and batch data environments. It operates as a standalone application on a single machine and can also function in an embedded mode. Additionally, it is capable of executing as a MapReduce application across various Hadoop versions and offers compatibility with Azkaban for initiating MapReduce jobs. In standalone cluster mode, it features primary and worker nodes, providing high availability and the flexibility to run on bare metal systems. Furthermore, it can function as an elastic cluster in the public cloud, maintaining high availability in this setup. Currently, Gobblin serves as a versatile framework for creating various data integration applications, such as ingestion and replication. Each application is usually set up as an independent job and managed through a scheduler like Azkaban, allowing for organized execution and management of data workflows. This adaptability makes Gobblin an appealing choice for organizations looking to enhance their data integration processes.
Description
IPFS Cluster enhances data management across a collection of IPFS daemons by managing the allocation, replication, and monitoring of a comprehensive pinset that spans multiple peers. While IPFS empowers users with content-addressed storage capabilities, the concept of a permanent web necessitates a solution for data redundancy and availability that preserves the decentralized essence of the IPFS Network. Serving as a complementary application to IPFS peers, IPFS Cluster maintains a unified cluster pinset and intelligently assigns its components to various IPFS peers. The peers in the Cluster create a distributed network that keeps an organized, replicated, and conflict-free inventory of pins. Users can directly ingest IPFS content to multiple daemons simultaneously, enhancing efficiency. Additionally, each peer in the Cluster offers an IPFS proxy API that executes cluster functions while mimicking the behavior of the IPFS daemon's API seamlessly. Written in Go, the Cluster peers can be launched and managed programmatically, making it easier to integrate into existing workflows. This capability empowers developers to leverage the full potential of decentralized storage solutions effectively.
API Access
Has API
API Access
Has API
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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
Apache Software Foundation
Country
United States
Website
gobblin.apache.org
Vendor Details
Company Name
IPFS Cluster
Website
cluster.ipfs.io
Product Features
Big Data
Collaboration
Data Blends
Data Cleansing
Data Mining
Data Visualization
Data Warehousing
High Volume Processing
No-Code Sandbox
Predictive Analytics
Templates