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What is IPFS Cluster?

IPFS Cluster significantly improves data management across a network of IPFS daemons by overseeing the allocation, replication, and monitoring of a robust pinset that spans various peers. As IPFS offers users the ability to utilize content-addressed storage, the need for a permanent web brings forth the requirement for solutions that ensure data redundancy and availability while maintaining the decentralized nature of the IPFS Network. Acting as a complementary tool for IPFS peers, IPFS Cluster sustains a cohesive cluster pinset and smartly distributes its elements among different IPFS peers. The peers forming the Cluster establish a distributed network that upholds an organized, replicated, and conflict-free record of pins, ensuring reliability. Users can efficiently ingest IPFS content to multiple daemons at once, thereby boosting operational effectiveness. Furthermore, each peer within the Cluster provides an IPFS proxy API that carries out cluster functions while seamlessly imitating the behavior of the API from the IPFS daemon. Built using Go, the Cluster peers can be initiated and overseen programmatically, facilitating easier integration into current workflows. This feature empowers developers to tap into the complete capabilities of decentralized storage solutions, ultimately enhancing their projects' functionality and resilience. Additionally, the innovative design of IPFS Cluster fosters collaboration among users and developers alike, creating a more robust ecosystem for decentralized applications.

What is Apache Gobblin?

A decentralized system for data integration has been created to enhance the management of Big Data elements, encompassing data ingestion, replication, organization, and lifecycle management in both real-time and batch settings. This system functions as an independent application on a single machine, also offering an embedded mode that allows for greater flexibility in deployment. Additionally, it can be utilized as a MapReduce application compatible with various Hadoop versions and provides integration with Azkaban for managing the execution of MapReduce jobs. The framework is capable of running as a standalone cluster with specified primary and worker nodes, which ensures high availability and is compatible with bare metal servers. Moreover, it can be deployed as an elastic cluster in public cloud environments, while still retaining its high availability features. Currently, Gobblin stands out as a versatile framework that facilitates the creation of a wide range of data integration applications, including ingestion and replication, where each application is typically configured as a distinct job, managed via a scheduler such as Azkaban. This versatility not only enhances the efficiency of data workflows but also allows organizations to tailor their data integration strategies to meet specific business needs, making Gobblin an invaluable asset in optimizing data integration processes.

Media

Media

Integrations Supported

Docker
Ethereum
Filecoin
Hadoop
IPFS
Netdata

Integrations Supported

Docker
Ethereum
Filecoin
Hadoop
IPFS
Netdata

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

IPFS Cluster

Company Website

cluster.ipfs.io

Company Facts

Organization Name

Apache Software Foundation

Company Location

United States

Company Website

gobblin.apache.org

Categories and Features

Categories and Features

Big Data

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

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