The Intelligent Thing -- Using In-Memory for Big Data and Beyond

The Briefing Room
The Intelligent Thing—Using In-Memory for Big Data and Beyond

Twitter Tag: #briefr The Briefing Room
Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com

!   Reveal the essential characteristics of enterprise software,
good and bad
!   Provide a forum for detailed analysis of today s innovative
technologies
!   Give vendors a chance to explain their product to savvy
analysts
!   Allow audience members to pose serious questions... and get
answers!
Mission

JUNE: Database
July: CLOUD
August: HIGH PERFORMANCE ANALYTICS
September: ANALYTICS

Database

Analyst: John O’Brien
John O’Brien is
Founder and CEO of
Radiant Advisors

!   Teradata is known for its data analytics solutions with a
focus on integrated data warehousing, big data analytics
and business applications
!   It offers a broad suite of technology platforms and
solutions; data management applications; and data mining
capabilities
!   Teradata Intelligent Memory is a new capability that
provides automated management of data based on
temperature
Teradata

Alan Greenspan
Alan Greenspan is Product Marketing Manager for
Teradata Corporation. He is responsible for product
marketing for the Teradata database, key database
technologies, security and performance. Alan has
more than 20 years with Teradata Corporation.

Alan Greenspan
TERADATA INTELLIGENT
MEMORY

10 Teradata Confidential
Trends
•  Memory is 3,000 times faster than disk
•  Memory per node is increasing
>  96GB -> 256GB ->512GB -> 768GB -> 1TB
•  Cost of memory is decreasing
Issues
•  Memory still 80x more expensive than disk
•  Not all data fits into memory
•  Not all data worth 80x premium
Teradata Solution
•  Create a new extended memory space
for most frequently accessed data
Exploiting Technology Trends

Teradata Intelligent Memory
Innovative In-Memory Technology
•  New extended memory space
•  Improves query performance
•  A smarter approach than in-memory databases
•  Leverage large memory capacities in new platforms

•  Sophisticated
algorithms to
track usage,
measure
temperature,
and rank data
•  Compliments
FSG cache
•  Dynamically
adjusts to
new query
patterns
New Extended Memory Space
Intelligent
Memory
most
recently
used data
most
frequently
used data
Hottest data placed and
maintained in memory,
aged out as it cools
cool outvery hot in
FSG
Cache
Temporarily store data
required for current
queries, purges least
recently used

•  1% of data satisfies
43% of query activity
•  Hottest data in
memory/not all the
data
•  Integrated into
Teradata system
•  No need for separate
appliance
Improves Query Performance
Performance of in-memory
databases without their cost

•  Automatic
•  Transparent
•  No DBA effort
•  No SQL changes
•  Maintain user
access to ALL
data for
analysis
A Smarter Approach than In-Memory Databases
Extend multi-temperature data
management to memory

• Memory Capacities Growing Exponentially
• Traditional Cache Reaches Diminishing
Returns
• Data is stored compressed and in columns
and rows
• Created extended memory space beyond
cache
• Use it in a new innovative way
Leverage Large Memory Capacities in New
Platforms

Teradata Workload-Specific Platforms
670
Future
2700
6700
Data Mart
Appliance
Extreme Data
Appliance
Data Warehouse
Appliance
Active Enterprise
Data Warehouse

•  All Members of the Workload-Specific Platform Family
•  Minimum Memory Requirements
>  Recent models only
>  May require memory upgrade
•  Requires Teradata Database 14.10
•  Teradata Virtual Storage is not required
Configuration Requirements
Model Memory/Node
FSG Cache +
I.M./AMP
Active Enterprise Data
Warehouse
6700 512GB 8GB
Data Warehouse
Appliance
2700 256GB 5GB
Data Mart Appliance 670H 256GB TBD
Extreme Data
Appliance
Future TBD TBD

• Teradata SQL-H
allows Hadoop data to
take advantage of
Teradata Intelligent
Memory
• Hadoop data that is
persisted in Teradata
and becomes very hot
will dynamically move
into Teradata
Intelligent Memory
Teradata Intelligent Memory and UDA

Teradata
Intelligent
Memory
In-Memory
Databases
All data in memory Wrong goal Small data sets
Big data per node Yes No
Columnar Yes + rows Yes
Memory-speed performance Yes Yes
Compression Yes Yes
Recovery snapshot Yes Yes
SSD/HDD logging Yes Yes
Indexes, aggregates Yes No
Large node memories Yes Yes
Intelligent Memory vs In-Memory Databases

Reload on Reboot
Candidates
VH
cylinders temp0
Cyl 56 100
Cyl 21 100
Cyl 22 99
Cyl 88 99
Cyl 42 98
Cyl 66 95
Intelligent
memory

•  In memory expectations
>  All “my queries” are faster
•  Business value
>  Majority of queries are faster
>  Increased response time
•  Intelligent Memory won’t help
>  CPU constrained queries
>  Deep history queries
–  Very Hot + cold data joins
>  1-3 second queries
>  Data loading
Not Every Workload is IO Bound
Node

Teradata Intelligent Memory Sample Quotes
May 2013 Coverage Report
Teradata takes on SAP's HANA with in-memory
technologies push
"Teradata Intelligent Memory technology is built into
the data warehouse and customers don't have to
buy a separate appliance
Teradata gets into the in-memory biz to take on SAP’s
HANA
Data analytics veteran Teradata will not let the new era of
data-analysis architectures pass it by without a fight. It has
already built products to address massive data volumes and
Hadoop
Teradata boosts DRAM on appliances for in-memory
queries
You don't need no stinkin' HANA or Exalytics
Teradata enters the in-memory fray,
intelligently
Teradata Intelligent Memory combines RAM
and disk for high-performance Big Data without
the extreme requirement of exclusive in-
memory operation
Teradata Extends In-Memory Computing Reach
Teradata Intelligent Memory, an approach to in-
memory computing that allows the workloads running
on a Teradata database appliance to make use of
extended memory.
Teradata Leverages In-Memory Technology
For Big Data
Teradata (TDC) introduced Intelligent Memory, a
new database technology that creates extended
memory space

Perceptions & Questions
Analyst:
John O’Brien

© Copyright 2013 Radiant Advisors. All Rights Reserved
REDEFINING
HOT AND COLD
DATA
25
Inside Analysis – Teradata Intelligent Memory System
June 11, 2013
John O’Brien | Principal and Founder, Radiant Advisors
@obrienjw @radiantadvisors
john.obrien@radiantadvisors.com

ILM PRINCIPLE
Redefining Hot and Cold Data
Information Management Lifecycle:
“Storage is optimized when the value of
information is persisted on the corresponding
storage cost.”
By using the age of the information, users can
define its value as hot, warm, or cold
temperatures then leverage corresponding tiers
of data storage…
26

PREVIOUS DATA AGING STORAGE TIERS
Information Lifecycle Challenges:
•  Requires business usage
definition to script migration
•  Different business data may
have different aging policies
•  Not all policies are time based
(status based)
•  Marking read-only, backups
•  Isolate data, partition-based
- Very operational oriented -
Try to analyze 3 years of business
activity by demographic, products,
or locations
(hits weakest link storage tier)
27
Database Server (SMP)
Fast Disks
Fast Connectivity
Smaller Capacity
Fast Disks
SSD
Medium Disks
15,000 rpm
Medium Disks
7,200 rpm
Slow Disks
5,400 rpm
Medium Disks
Fast Connectivity
Medium Capacity
Medium Disks
Slow Connectivity
Medium Capacity
Bulk Disks
Slow Connectivity
High Capacity
Tape
Storage Sub-Systems
Tape
Slow Connectivity
Highest Capacity
Defining and Managing Individual Data Record Policies for each tier
1-30 days 30-90 days 3-12 mos. 12-24 mos. 2+ years
This Month This Quarter This Year Yr-over-Yr compliance
$ $ $ $ $ $ $ $ $ $ $ $ $ $ $

MPP SOLVES ANALYTIC WORKLOADS
MPP Multi-tier challenges:
•  Still requires business usage
definition to script data
migration
•  Partition key setting and data
skewness
Parallelism overcomes weakest
link partition isolation
Does the age of a record
correspond to its value in
analytics?
28
Database Server (MPP)
Fast Nodes
Small Capacity
More CPU/Memory
Fast Disks
Solid State Disks
Medium Disks
1s terabytes per node
Slow Disks
10s Terabytes per node
Medium Nodes
Medium Capacity
Avg CPU/Memory
Bulk Nodes
High Capacity
Low CPU/Memory
Node Array
Defining and Managing Individual Data Record Policies for each MPP tier
1-30 days 1 – 12 mos. 1 - n years online
This Month This Year History
$ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $

OPTIMIZING THE MPP PLATFORM
Intelligent Memory:
•  Determines value of data by
its usage in the business via
activity metrics and
algorithms
•  Automatically and
transparently moves data to
the appropriate tier
•  Bi-directional data movement
is heating up or cooling off
•  Loaded data can start hot
and cool off
MPP to overcome partitioning
Usage to govern storage tiers
29
Database Server (MPP)
Fast Nodes
Small Capacity
More CPU/Memory
Fast Disks
Solid State Disks
Medium Disks
1s terabytes per node
Slow Disks
10s Terabytes per node
Medium Nodes
Medium Capacity
Avg CPU/Memory
Bulk Nodes
High Capacity
Low CPU/Memory
Node Array
Intelligent Memory management based on business usage
Hot Warm Cold
Most Often Occasional Use Rarely Used and Available

THE NEW PARADIGM FOR ANALYTICS
By using the age of the information, users can define
its value as hot, warm or cold temperatures matching
corresponding tiers of storage…
Which meta data represents analytic value?
Monitoring a BI system’s analytic usage, the system
can define its analytic value as hot, warm, or cold
temperatures and then transparently persist data
intelligently
30

THANK YOU!
For more information
www.RadiantAdvisors.com
Twitter: @RadiantAdvisors #ModernBI #RediscoveringBI
RSS: feed://radiantadvisors.com/feed/
Email us at: info@RadiantAdvisors.com
Linked IN: www.linkedin.com/company/radiant-advisors
Subscribe: Rediscovering BI monthly newsletter
www.radiantadvisors.com.rediscoveringbi
31

QUESTIONS
•  If Teradata Intelligent-Memory can optimize a BI system’s
storage persistence, how do you know what percentage of
each storage tier to configure beforehand? Is it simply an
economic decision at that point (the most memory and fast disk
that I can afford)?
•  For the secret-sauce algorithms being used in the IOPs
monitoring by TIM, generally how fast do data sets “warm up”
or “cool off” with usage?
•  If I can anticipate high usage for a given data set on an
upcoming Monday morning event, is there a way to bypass
warming up and designate the hot?
•  What are the boundaries for TIM optimization within Teradata
Aster and are there future plans for expansion and
enhancements?
32

July: CLOUD
August: HIGH PERFORMANCE ANALYTICS
September: ANALYTICS
Upcoming Topics
www.insideanalysis.com

Thank You
for Your
Attention

The Intelligent Thing -- Using In-Memory for Big Data and Beyond

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The Intelligent Thing -- Using In-Memory for Big Data and Beyond