Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
Data storage expert Henry Newman details the unique problems in planning for disaster recovery for large archives — a growing concern, given the exponential growth in today’s data archives.
Disaster recovery (DR) is often discussed in broad terms throughout the storage industry, but in this article I will explore a specific segment of the overall market: DR planning for large archives.
This is the first article in a two-part series on archives. The next article will cover architectural planning for large archives.
First, what are my definitions of an archive, and what is a large archive?An archive is a repository of information that is saved, but most of the information is infrequently accessed.
The definitions of archives have changed recently. Just three or four years ago, archives were always on tape, with only a small disk cache (usually less than 5% of the total capacity). The software to manage data on tape and/or disk is called hierarchical storage management (HSM) and was developed for mainframes more than 35 years ago.
Today we have large disk-based archives that back up data over networks. For example, both my work PC and home PCs are backed up via the internet, and large cloud-based archives are common today. There is of course a question of reliability (see “Cloud Storage Will Be Limited By Drive Reliability, Bandwidth”), but that is a different topic.
My definition of a large archive is fairly simple: anything over 2,000 SATA disk drives. Today, that is about 4 PB, and next year it will likely be 8PB when drive capacities increase.I am using 2,000 drives for the archive size given the expected failure rate of the 2,000 drives. Even in a RAID-6 configuration which would require 2,400 drives it will be challenging given the rebuild time to manage that many drives for a single application.
Read the rest at Enterprise Storage Forum.
RELATED NEWS AND ANALYSIS
-
Huawei’s AI Update: Things Are Moving Faster Than We Think
FEATURE | By Rob Enderle,
December 04, 2020
-
Keeping Machine Learning Algorithms Honest in the ‘Ethics-First’ Era
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 18, 2020
-
Key Trends in Chatbots and RPA
FEATURE | By Guest Author,
November 10, 2020
-
Top 10 AIOps Companies
FEATURE | By Samuel Greengard,
November 05, 2020
-
What is Text Analysis?
ARTIFICIAL INTELLIGENCE | By Guest Author,
November 02, 2020
-
How Intel’s Work With Autonomous Cars Could Redefine General Purpose AI
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 29, 2020
-
Dell Technologies World: Weaving Together Human And Machine Interaction For AI And Robotics
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
October 23, 2020
-
The Super Moderator, or How IBM Project Debater Could Save Social Media
FEATURE | By Rob Enderle,
October 16, 2020
-
Top 10 Chatbot Platforms
FEATURE | By Cynthia Harvey,
October 07, 2020
-
Finding a Career Path in AI
ARTIFICIAL INTELLIGENCE | By Guest Author,
October 05, 2020
-
CIOs Discuss the Promise of AI and Data Science
FEATURE | By Guest Author,
September 25, 2020
-
Microsoft Is Building An AI Product That Could Predict The Future
FEATURE | By Rob Enderle,
September 25, 2020
-
Top 10 Machine Learning Companies 2020
FEATURE | By Cynthia Harvey,
September 22, 2020
-
NVIDIA and ARM: Massively Changing The AI Landscape
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
September 18, 2020
-
Continuous Intelligence: Expert Discussion [Video and Podcast]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 14, 2020
-
Artificial Intelligence: Governance and Ethics [Video]
ARTIFICIAL INTELLIGENCE | By James Maguire,
September 13, 2020
-
IBM Watson At The US Open: Showcasing The Power Of A Mature Enterprise-Class AI
FEATURE | By Rob Enderle,
September 11, 2020
-
Artificial Intelligence: Perception vs. Reality
FEATURE | By James Maguire,
September 09, 2020
-
Anticipating The Coming Wave Of AI Enhanced PCs
FEATURE | By Rob Enderle,
September 05, 2020
-
The Critical Nature Of IBM’s NLP (Natural Language Processing) Effort
ARTIFICIAL INTELLIGENCE | By Rob Enderle,
August 14, 2020