Common Use Cases

Common use cases for Tardigrade Object Storage

Common architectural patterns are as follows:

Platform/Service

Description

Decentralized Advantage

Archival Storage

Long term storage of large files required for business continuity or based on regulatory compliance

Low cost and always available high-throughput bandwidth means storage is economical and recovery is rapid

Database Backup

Regular snapshot backups of databases for backup or testing are an entrenched part of infrastructure management

Streaming backup eliminates the need to write large database snapshots to local disk before backup or for recovery

Private Data

Data that is highly sensitive and an attractive target for ransomware attacks or other attempts to compromise or censor the data

Client side encryption and industry-leading access management controls and highly distributed network of storage nodes reduce attack surface and risk

Multimedia Storage

Storage of large numbers of large multimedia files, especially data produced at the edge from sources like security cameras that must be stored for long periods of time with low access

Rapid transit leveraging parallelism makes distributed storage effective for integrating with video compression systems to reduce volume of data stored

Multimedia Streaming

Fluid delivery of multimedia files with the ability to seek to specific file ranges and support for large number of concurrent downloads

Native file streaming support and distributed bandwidth load across highly distributed nodes reduce bottlenecks

Large File Transfer

Transiting large amounts of data point to point over the internet

High-throughput bandwidth takes advantage of parallelism for rapid transit; Client-side encryption ensures privacy during transit

Hybrid Cloud

Flexible ability to provide elastic capacity to on-premise data storage

Enables enterprises to monetize excess storage capacity when not needed and provides secure, private cloud storage on demand

Machine Learning

Storage transit for processing of large data sets from disparate data sources and types

Decentralized architecture provides better response times for data processing, which can translate into the ability to process more data within time limits, as well as efficiency in transport and peering costs

VR/AR

Virtual reality and augmented reality are both latency sensitive and bandwidth demanding with large file sets.

Distributed storage provides better response times toward end users, as well as efficiency in transport and decreased peering costs

IoT Data

Connected devices generate massive amounts of data

Small IoT files can be packed into large blocks for efficient storage while individual message files can be accessed via streaming to specific data ranges