Data Classification and Storage Efficiency

Welcome to Lewan & Associates IT Solutions Division

Storage Training


Agenda

Lewan/Speaker Background Setting the Stage Data Classification and The Cube 4 Buckets of Storage Data Efficiency


Speaker Information

Scott Pelletier – VP Technology [email protected] @scottpelletier http://blog.lewan.com


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Staff staff augmenta9on to supplement your exis9ng team


KEY VENDORS

•  Microsoft •  Fortinet •  Kaviza

•  VMware •  Citrix •  Appsense •  Cisco •  NetApp •  Dell •  CommVault •  HP

•  Cisco •  Lifesize

•  N-Able •  ConnectWise •  Asigra •  Microsoft


Today We Will Discuss 3 Things

Shred

Offline

Importance

Performance

File

Block

Lifecycle

Nearline

Online

Critical

Essential

Necessary

Desirable

Primary Backup / Recovery

Disaster Recovery

Archive

The Cube 4 Buckets Storage Efficiency


Setting the stage

What is data classification (high-level) How might classifying your data drive

infrastructure choices


Data Classification and The Cube

Shred

Offline

Importance

Performance

File

Block

Lifecycle

Nearline

Online

Critical

Essential

Necessary

Desirable


Data Classification Metrics

Importance – The importance of data to an organization is based on the impact of the data on the business functions it supports.

Performance – The performance requirement for data is important to an organization due to the competitive advantage gained by providing fast access to information the business and its users need.

Lifecycle – The lifecycle of information has a significant impact on how the information should be treated within a storage environment. Some criteria that impact lifecycle are compliance, legal exposure, long term reporting and reference of the data for business purposes.


Impacts on Architecture

Real-time Protection - Approach for protecting data against hardware failure of one (or more) physical components (e.g., disk, power supply, controller, etc.). This type of protection is more real-time in nature and is intended to protect against a physical problem.

Point-in-time Protection - Approaches for protecting data against both catastrophic hardware failures and corruption (due to hardware/software failure or human error).

Geographic - Approach for protecting data against site failure. Live data is copied, by some mechanism, over a network to a remote site. Various strategies exist from scheduled file copy to live, synchronous bit-by-bit replication.

Long Term Protection - Approach allowing for continued availability of data that is less likely to be accessed, but still has some importance to the business. Archived data is normally stored on lower performance media and is not as readily available to an end user.

Security – Approach for securing information from unauthorized access (accidental or intentional), modification, destruction, or disclosure.


Importance


RTO & RPO Defined

•  The Recovery Time Objective (RTO) helps us understand the maximum time tolerated to recover a system to a functional state before experiencing extreme business impact.

•  The Recovery Point Objective (RPO) determines how much data could be lost, before experiencing extreme business impact.

•  These metrics are measured in time and the worst-case data point for a system to be recovered is effectively the time for each measurement added together.

•  Affect real-time, point-in-time and geographic protection strategy


Performance Metrics


Common Metrics

Response time – less is better •  <1ms = excellent, <10ms=good, <25ms=usually acceptable

IOPS – IO’s per second •  size of the IO matters •  Number of disk spindles is critical •  More important for general purpose apps (DB, file, Mail) •  RAID adjusted IOPS

MB/s - Throughput measurement •  Larger IO’s perform better •  Number of spindles less critical •  More important for streaming, D2D applications


Lifecycle

Time after creation

Bus

ines

s R

elev

ance

Immediate needs

accountability

seeking evidence

Re-use Future Reference

Source: Jan Möller, Ministry of Transport, NL


Data Lifecycle

•  Long-term access to information, compliance and risk mitigation during legal discovery.

•  Common for IT to use their backup systems as archival systems •  Automatic archiving and data movement or placement is key •  Providing eDicsovery


Impacts on Architecture

Long Term Protection - Approach allowing for continued availability of data that is less likely to be accessed, but still has some importance to the business. Archived data is normally stored on lower performance media and is not as readily available to an end user.

Security – Approach for securing information from unauthorized access (accidental or intentional), modification, destruction, or disclosure.

Media and Data Format – As retention time scales beyond a couple years, these two items become an issue


The 4 Buckets


Four Storage Containers


Disaster Recovery

Archive

10TB * $25,000 10TB * $25,000 10TB * $25,000

0TB * $25,000

Total Cost: $750,000 of cost over 3 years

•  Every container of storage costs money.

•  Each container is related to the size of the customers “primary” data

•  If we can optimize the primary data, we also optimize the backup and DR data

•  The prior slide showed two extremes of 10:1 and 2:1 of raw storage needed per application capacity

•  These examples are assuming somewhere in the middle, e.g., 5:1, so 10TB of application capacity = 50TB needed raw * $5,000/TB = $250,000 or $25,000/TB for application data


Four Storage Containers


Disaster Recovery

Archive

5TB * $25,000 5TB * $25,000 5TB * $25,000

5TB * $25,000

Prior w/o data reduction: $750,000 Total Cost: $500,000 Total Savings: $250,000

•  Data management analysis will uncover inefficient use of resources

•  e.g., introducing a self protecting archive platform will help save the customer money and reclaim many of their resources for growth


Efficiency of Primary Online Storage


What’s a TB to ya

NetApp example of dealing with overhead of enterprise storage solutions and how they combat some of this.


Efficiency is Key

Efficiency Techniques: •  Intelligent snaps and clones •  De-duplication •  Thin Provisioning •  Granular storage blocks •  Compression •  Intelligent caching


How Much Storage Is Needed

1TB Application Data

5TB Application Snaps/Clones

1TB Headroom

3TB Array Functions


200GB Application Snaps/Clones

200GB Headroom

600GB Array Functions

No Efficiency = 1TB Requires = 10TB Raw Capacity W/ Efficiencies = 1TB Requires = 2TB Raw Capacity Efficiency Techniques: •  Intelligent snaps and clones •  De-duplication •  Thin Provisioning •  Granular storage blocks •  Compression •  Intelligent caching


How Much Does Storage Cost


5TB Application Snaps/Clones

1TB Headroom

3TB Array Functions


200GB Application Snaps/Clones

200GB Headroom

600GB Array Functions

10TB Raw Capacity * $5,000/TB = $50,000 for 3 years

2TB Raw Capacity * $5,000/TB = $10,000 for 3 years

Note: Calculations based on raw storage and no management costs, purely hardware, software and maintenance


Today We Discussed 3 Things

Shred

Offline

Importance

Performance

File

Block

Lifecycle

Nearline

Online

Critical

Essential

Necessary

Desirable


Disaster Recovery

Archive

The Cube 4 Buckets Storage Efficiency


Questions

Data Classification and Storage Efficiency

Technology

Transcript of Data Classification and Storage Efficiency