SPONSORED WORKSHOP by Cleversafe from Structure:Data 2012

Store and Analyze Big Data
Without Limits
March 23, 2012
Friday, July 27, 2012

Big Data Challenges

From 800 exabytes in 2008 to 35,000 exabytes in 2020

90% of data is unstructured format, and
89% of growth in storage is unstructured format

75% of data is generated by individuals, and
enterprises have liability for 80% of data generated

Concern for data security and reliability in the Cloud
Public Cloud deployments and content depots are projected
to grow to $7.4B by 2014 to accommodate capacity

“Big data technologies describe a new generation of technologies
and architectures, designed to economically extract value from
very large volumes of a wide variety of data, by enabling high-
velocity capture, discovery, and/or analysis.”
– IDC Extracting Value from Chaos, May 2011

Copyright © 2012 Cleversafe, Inc. All rights reserved.
Copyright © 2012 Cleversafe, Inc. All rights reserved. 2


Data Storage is Transforming

5000
Capacity-Optimized storage
growing 63% annually*
3750
Data

2500

1250

0
2002 2012
Year




5000
Capacity-Optimized storage
3750
Traditional Data
Numbers, text,
Data

databases
2500

1250

0
2002 2012
Year




5000 New Data
Capacity-Optimized storage Images, scans, audio files
videos, hi-res videos
3750
Traditional Data
Numbers, text,
Data

databases
2500

1250

0
2002 2012
Year




5000 New Data
Capacity-Optimized storage Images, scans, audio files
videos, hi-res videos
3750
Traditional Data
Numbers, text,
Data

databases
2500

1250

0
2002 2012
Year
•Growing 100X every 10 years
•Required new methods



Practical Applications for a 10 Exabyte Data
Storage System

• Understand certain IP traffic patterns for tracking
fraudulent activity
• Determine online purchasing patterns for a retailer or
merchandiser to help launch a new product or service
• Identify hot new trends in entertainment, sports, gaming,
etc.
• In this election year, understand the appeal of a political
message and more directly target potential voters



RAID Can’t Effectively Scale

• RAID is not ideal for storing large amounts (PB) of digital
content.

• RAID does not allow configurable reliability to be established.

• Increasing amounts of stored data is raising the risk of data loss
and corruption.

• Spindle size is increasing faster than IO performance causing
longer rebuild times and exposure to data loss.

• Spindle size is equal to Unrecoverable Read Error (URE) rates
causing silent data corruption.



How Dispersed Storage Technology Works
DATA Cleversafe IDA

Data is expanded, virtualized, transformed,
1 sliced and dispersed using Information
Dispersal Algorithms.

Slices are distributed to separate
2 disks, storage nodes and
geographic locations.

SITE 1 SITE 2 SITE 3 SITE 4

Even with individual servers or entire
3 sites down, real time bit perfect data is
retrieved from a subset of slices.
Cleversafe IDA DATA



What Does a Limitless Scale Storage System
Look Like?

• Single instance of data with guaranteed reliability and availability –
not RAID and copy based
• Built-in geographic distribution for high availability and site failure
tolerance
• Data concurrency with multiple simultaneous readers and writers
• Continuous data availability through upgrade cycles and storage
node replacement
• Flat namespace with highly efficient metadata management and no
database or master name node
• Architecture delivers independent scaling of storage capacity and
performance
• Take advantage of largest capacity most power-efficient disk drives
available in the industry



10 Exabyte Data Storage System Configuration

• Data integrity and availability provided without the
overhead of replication
• Deployed across multiple sites for site failure
tolerance and high availability Portable Datacenter
• High bandwidth network between sites (PD)
• Utilize a portable datacenter (PD) container model
for rapid setup and mobility
• Each PD houses multiple racks for storage and a
single rack for network connectivity
• Flat architecture with no centralized database or
management node
• Hundreds of simultaneous readers/writers with
instantaneous access to billions of objects



System Configuration

• 16 sites across the US • 35 PDs per site (560 total)
• High bandwidth WAN • 21 Racks / PD (11,760 total)
• IDA W32, T22, 1.45 expansion • 189 Storage Nodes / PD
• Massively parallel distributed (105,840 total)
readers/writers • 45 3TB drives per storage node
• Filter capability with ingest (4.7M total)
• Access embedded in application • ~15 EB raw, ~10EB usable



System Architecture
Very Big Data Sources

Near Real-time Parallel Data Analyzers (and filters)

Multiple Simultaneous Writers
Data &
Indexes
Very Large Object Storage Cloud
• Deployed across multiple sites Metadata
• Using container-based (POD) model
• Flat architecture, no central database
Analysis &
Results
Multiple Simultaneous Readers and Writers

Secondary (Parallel) Data Analyzers

10

Use Case: Store and Analyze 6 months of
Internet traffic
Total Global Monthly Internet Traffic Growing 32% Annually

PB
80 Exabytes
per month in
Dec. 2015

IP Traffic North America Monthly Worldwide Monthly

2012 12 EB 37 EB
2015 23 EB 80 EB Source: Cisco VNI, 2010



Use Case: Store and Analyze 6 months of
Internet traffic
North America North America
Monthly Rolling 6 Months*
2012 12 EB 96 EB
Source: Cisco VNI, 2010

Very Large Scale Very Large Scale
Processing Requirements Storage Requirements
• Ingest/Filter : 4.6 TB per sec • Store 10EB grow to 1,000 EB
• Analyze/Index : ~0.5 TB per sec • ~900 GB/sec of data ingest
(assuming a 10:1 filter of IP traffic) • Growing 32% per year

Potential Solutions: Traditional data storage systems
• Massively parallel, distributed not capable of this scale
pioneered by Google, Yahoo, etc.
Cleversafe Focus

** Rolling 6 months requires capacity to store 8 months worth of data in order to safely capture the next month before deleting the oldest month’s worth of data



Key Takeaways

• RAID can’t effectively scale to multi-petabytes and beyond
• A limitless scale data storage system requires:
– Single instance of data with guaranteed reliability and
availability– not RAID and copy based
– Built-in geographic distribution for high availability and site
failure tolerance
– Data concurrency with multiple simultaneous readers and
writers
– Flat namespace with highly efficient metadata management and
no database or master name node



Text

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