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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 IBM Analytics Engine?

IBM Analytics Engine presents an innovative structure for Hadoop clusters by distinctively separating the compute and storage functionalities. Instead of depending on a static cluster where nodes perform both roles, this engine allows users to tap into an object storage layer, like IBM Cloud Object Storage, while also enabling the on-demand creation of computing clusters. This separation significantly improves the flexibility, scalability, and maintenance of platforms designed for big data analytics. Built upon a framework that adheres to ODPi standards and featuring advanced data science tools, it effortlessly integrates with the broader Apache Hadoop and Apache Spark ecosystems. Users can customize clusters to meet their specific application requirements, choosing the appropriate software package, its version, and the size of the cluster. They also have the flexibility to use the clusters for the duration necessary and can shut them down right after completing their tasks. Furthermore, users can enhance these clusters with third-party analytics libraries and packages, and utilize IBM Cloud services, including machine learning capabilities, to optimize their workload deployment. This method not only fosters a more agile approach to data processing but also ensures that resources are allocated efficiently, allowing for rapid adjustments in response to changing analytical needs.

Media

Media

Integrations Supported

Acquia CDP
Apache Spark
Docker
Ethereum
Filecoin
Galileo
Hadoop
IBM Cloud Object Storage
IPFS
Netdata
RadiantOne
Switch Automation

Integrations Supported

Acquia CDP
Apache Spark
Docker
Ethereum
Filecoin
Galileo
Hadoop
IBM Cloud Object Storage
IPFS
Netdata
RadiantOne
Switch Automation

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

$0.014 per hour
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

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/cloud/analytics-engine

Categories and Features

Categories and Features

Data Discovery

Contextual Search
Data Classification
Data Matching
False Positives Reduction
Self Service Data Preparation
Sensitive Data Identification
Visual Analytics

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

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Popular Alternatives

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