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features
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support

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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 No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Docker No 
Ethereum No 
Filecoin No 
Hadoop Yes 
IPFS No 
Netdata No 

Integrations

Docker Yes 
Ethereum Yes 
Filecoin Yes 
Hadoop No 
IPFS Yes 
Netdata Yes 

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 No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
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 Yes 

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

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 No 
Data Blends No 
Data Cleansing No 
Data Mining No 
Data Visualization No 
Data Warehousing No 
High Volume Processing No 
No-Code Sandbox No 
Predictive Analytics No 
Templates No 

Product Features

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