data warehousing & business intelligence 5 years from now
TRANSCRIPT
Data Warehousing & Business Intelligence 5 years from now
Presentation to Helsinki TDWI MeetingDecember 15th, 2009Martin Willcox
2 > 04/15/2023
Disclaimer
• The views expressed are those of the author and do not necessarily reflect those of the Teradata Corporation in all cases.
• Predicting the future is notoriously difficult and subject to error!
• The engineering plans outlined in this presentation are subject to change; they are not firm commitments, either to develop the features in question, or to provide them within a specified timeframe.
Source: Text
3 Teradata Confidential
The information explosion and the data warehouse - lies, damn lies & statistics?
“Winter Corp primary research... shows a consistent trend since 1988: the size of the largest data warehouse we validate triples approximately every two years.”
Source: http://www.b-eye-network.com/view/7188
4 Teradata Confidential
Decreasing storage unit costs will continue to drive increasing digitisation
• asdasd• sasdasdas
Source: IBM Research
“Around the year 2000 the price of storage dropped to a point where it became cheaper to store data on computer disks than on paper. In fact, this probably was a great turning point in the history of the development of western civilisation... now the digitisation of text is not only of interest for sharing and analysis, but it is also more economical”
Source: Physical Database Design; Lightstone, Teorey & Nadeau
The Evolution of Detail Data
Invoice summaryBill summary
POS transaction summary
10-20 buckets of detail per customer per month
A few months of history avail
200-500 itemsof detail
What does customerrevenue and expenses
look like over time?
Sensor dataRFID data
GPS location dataWeb behavior
1000’s of event details per month or even day
2+ years of history avail
10,000 – 1M+ items of detail
How do overall customerbehaviors relate to revenue
related activities?
Transaction detailBill line-item detail
POS SKU level detail
100’s of detail transactionsper customer per month
A year of history avail
2,000 – 5,000 itemsof detail
What can I learn from specificcustomer transactions that will
allow me to stimulate revenue?
1x 10x 100x +
DTAPLocation Update Request07/28/02 10:09:10.134 PM
BSC MSC/VLRnew
HLR MSC/VLRold
GSM.98 MAPSend Parameter (old LAI, TMSI)07/28/02 10:09:10.516 PM
GSM.98 MAP Return Result (IMSI)07/28/02 10:09:11.0.57 PM
GSM.98 MAPUpdate Location07/28/02 10:09:11.611 PM
GSM.98 MAPCancel Location07/28/02 10:09:12.187 PM
GSM.98 MAPCancel Location Acknowledge07/28/02 10:09:12.492 PM
BSC MSC/VLRnew
HLR MSC/VLRold
DTAPLocation Update Accept07/28/02 10:09:15.084 PM
GSM.98 MAPInsert Subscriber Data07/28/02 10:09:13.256 PM
GSM.98 MAPInsert Subscriber Data Ack07/28/02 10:09:13.780 PM
GSM.98 MAPUpdate Location Ack07/28/02 10:09:14.322 PM
DTAPTMSI Allocation Complete07/28/02 10:09:15.676 PM
Increasing Sophistication, Complexity & Information
GSM GPRS 3G
91SignalingMessages
11SignalingMessages
200+SignalingMessages
Example new data type: Geospatial(web, text, audio, image…)
Analytical Archive
• Legal and Regulatory Compliance requirements are mainstream
• Organizations need to move from off-line model> 1½ day or longer response to queries > Queries limited to only small subset of entire history> No business value gained from the history data > Impacting other back-up systems> Requires lengthy data retrieval, conversion and archival efforts
• Towards an Analytical Archive on-line environment> Near instantaneous response to any access requests> Able to perform queries on full set of history data at any time> Enables full business analysis of all archive data
Moore’s law is enabling the production of very small, rugged computing systems…
9 > 04/15/2023
Smart dust: self-contained, millimeter-scale sensing and communication platforms for massively distributed sensor networks,
that contain sensors, computational ability, bi-directional wireless communications, and a power supply.
10 > 04/15/2023
Sensor technology in action
Need historical data (pattern identification and forecasting); to integrate this data with other data (correlation); near real-
time capture and analysis of data (to support preventative diagnosis).
11
Teradata Confidential
Computing performance for I/O intensive operations has been limited by storage
2GB-7,200RPM
9GB-10,000RPM
36GB – 7,200RPM
73GB-15,000RPM
36GB-10,000RPM
Storage densities are increasing in line with Moore’s law, but disk access times are increasing much more slowly.
Teradata Confidential 12
data
data
data
data
Database Server Database Server
Mechanical Rotation and Seek Limit HDD Speed
22X Faster on Typical Data Warehouse Workloads
13 > 04/15/2023
Prediction #1
Declining unit cost of storage –
+ increased competition+ increased complexity of products, services and
processes+ new sources, types of data+ increased regulation+ increased digitization and new sensor technology
= sustained growth in size of most data warehouses for the foreseeable future.
14 > 04/15/2023
Green IT is no longer just for the environmentalists…
“On the current path, in 5 years the cost of energy to power the data center will be higher than the cost of the IT equipment that it
powers.”
Gartner Data Center Conference 2007
15 > 04/15/2023
Count on energy prices increasing substantially…
Carbon price /($ per-tonne of CO2)
Proposed by US Congress 12 (rising to 20 by 2020)
European Trading Scheme current 22
Required to make onshore wind generation profitable without subsidy
38
Required to stablize atmospheric levels of CO2 at 450 ppm (average temperature rise of 2 degrees Celsius)
40 (rising to 80 by 2050)
Required to make offshore wind generation profitable without subsidy
136
Required to make solar generation profitable without subsidy
196
Figures taken from “Good policy, and bad”, The Economist, 3rd December 2009.
Teradata Confidential 16
SSD technology to the rescue? Not on its own…
Enterprise SSD Ratio Enterprise 15K HDD
IOPs (4 KB) 105 150X 102
Sequential Read BW (MB/s) >450 3X >150
Random 80/20 BW (MB/s) >450 22X 20
Avg. Random I/O latency Microseconds (10-6) >1000 Milliseconds (10-3 )
Active Power 10W 60% 17W
Typ. Capacity 300 GB 67% 450 GB
17 > 04/15/2023
…but the technology will help if applied selectively…
# of 15k HDD drives 160
Storage capacity 160 * 450GB = 72,000 GB
Storage power 160 * 17W = 2,720W
# of SSD drives 60
Storage capacity 60 * 300GB = 18,000GB
# of 15k HDD drives 40
Storage capacity 40 * 450GB = 18,000GB
# of 7.5k SATA HDD drives 36
Storage capacity 36 * 1,000GB = 36,000GB
Storage power (60 * 10W) + (40 * 17W) + (36 * 12.9) = 1,744W (-36% reduction)
Homogenous system, based on 15k RPM enterprise class HDD
Heterogeneous system, 1 : 1 : 2.5 ratio of SSD : 15k HDD : 7.5k HDD
18 > 04/15/2023
…and empirical evidence is that heterogeneous storage will be a good compromise in many cases
Data Temperature Demographic7 day trace
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Available Space
IO
85% of the IO directed at hottest 15% of data (10% of cylinders)
94% of the IO directed at hottest 30% of data (20% of cylinders)
43% of the IO directed at hottest 1.5% of data (1% of cylinders)
19 > 04/15/2023
Where does the space go?
2.9x more spinning disk than data – and Teradata enjoys lowest disk : data ratios according to last Gartner research published in this area.
RAID 1 is a very space intensive insurance / performance mechanism…
SSD technology may reduce the RDE burden still further?
TD uses value-based compression, actual mileage depends on data demographics. More aggressive
compression schemes typically trade space for performance – lots of current research in this space.
“Fractured mirror” concept may see a revival in the fortunes of “software RAID” mechanisms, with logical, rather than physical, “mirrors”.
20 > 04/15/2023
Multi-core CPU technology affords interesting new power saving possibilities…
…but in a multi-user environment these are likely to yield less dramatic savings.
Teradata Confidential21 >
Greatest opportunity still lies in better management of information assets
“Delivering Business Intelligence to Network Rail – A Strategic Approach”
Christopher Stanley, Presentation to Gartner BI Summit, Den Haag, January 20-22, 2009.
22 > 04/15/2023
Prediction #2
Lots of “technology fixes” will be applied to “green IT” – • Intelligent power management;• New storage technologies;• Enhanced data compression & “S/W RAID”;• Smart data centre design & location.
But shortening the length of the “digital shadow” through increased information consolidation and integration will achieve more than these “silver bullets” can.
Copyright Teradata © 2009 – All rights Reserved
The problem with traditional Business Intelligence
Process Step
Process Step
Process Step
Process Step
Process Step
ProcessStep
User has a key decision to make
here…
…Industry’s default response: stop what you
are doing, logon to a different BI tool /
application, run a report and study it, logoff / logon
again…
Much traditional BI is predicated on the assumption that knowledge workers are sat in a head office in front of a PC, making forward-
looking decisions and with time to develop and test hypotheses by comparing “forecast” versus “actuals” in static reports…
Copyright Teradata © 2009 – All rights Reserved
…not this knowledge worker!
25 > Copyright Teradata © 2009 – All rights Reserved
Pervasive BI: right data to the right actor -at the right time and via the right channel
Copyright Teradata © 2009 – All rights Reserved
Primarily Batchwith Pre-defined
Queries
STAGE 1
REPORTING
WHAThappened?
Event-basedTriggeringTakes Hold
STAGE 5
AnalyticalModeling
Grows
STAGE 3
PREDICTING
WHATwill happen?
ContinuousUpdate and
Time-Sensitive Queries GainImportance
STAGE 4
OPERATIONALIZING
What IShappening?
Increasein Ad HocQueries
STAGE 2
ANALYZING
WHYdid it happen?
Batch Ad HocEvent-based Triggering
AnalyticsContinuous Update
ACTIVE WAREHOUSING
What do I WANT to happen?
Information evolution and “Active Data Warehousing”
“More than 85% of the eBay analytical workload is new & unknown” (Oliver Ratzesberger, eBay, October 2008)
27 > 12/15/09 Copyright Teradata © 2008 – All rights Reserved
Event-based processing: event relationships versus rules
• Conjunctions> All the specified events happened
• Sequencing> The events happened in the specified
order• Disjunction (any n)
> Any n of the m specified events happened
• Temporal association> Events happened within n time units of
each other• Negation
> An event did not happen within a deadline
• Aggregation> Collections of events following sliding
window semantics
28 > 12/15/09 Copyright Teradata © 2008 – All rights Reserved
Some events can only be detected inside the data warehouse…
• Inside the Data Warehouse> When integrating data
detects the event > When analysis of data
detects the event > When KPI thresholds vary
considerably by time period
• Inside the Application> When a business process
detects the event first> When using a BAM or BPM
solution> Simple analysis of non-
integrated data detects the event first
Workflows & Applications
ADW
Workflows & Applications
ADW
29 > 12/15/09 Copyright Teradata © 2008 – All rights Reserved
Complex Event
Processing
…and intelligent responses to externally detected events will anyway require the EDW
Possible Actions
Pricing
Inventory
Distribution
Capital
New supplier
Rebalance staffing
Buy more/less
Modeling & Simulation
Event Streams
History
Today
EDW
Real time
Analytic
Copyright Teradata © 2009 – All rights Reserved
Active Enterprise Intelligence at ABN AMROBetter Web Advertising
There are currently > 50 different proposals. These are a few of them:
Within 2 seconds after a customer has positively identified him/herself the best matching proposition (out of over 50 propositions) is computed and presented based on real time customer information
365 days per year (24x7)
175,000 contacts / day
> 63 million personalizations / year
Copyright Teradata © 2009 – All rights Reserved
Click through rate:
5,5%
Click through rate:1,1%
Click through rate:4,0%
Click through rate for banners (benchmark: 0.2 % or 1 in 500)
Active Enterprise Intelligence at ABN AMROBetter Web Advertising
Copyright Teradata © 2009 – All rights Reserved
Also Used on Inbound Call Dashboard
Recommendations linked to overall targets
Updated daily !
Linked to:
-Total SOW
-Total SOI
-Credit position
Potential indicator – input for
Contact-type
S= Service oriented
V = Sales oriented
E = Efficiency oriented
Housebank indicator
Strategic goal
Ouside in
Customer perspective
Copyright Teradata © 2009 – All rights Reserved
Active Enterprise Intelligence in BankingNext Best Activity on Call Center
Inbound:• Doubled sales (+122%) to high potential customers
• > 24% decline in average handling time for low potential customers - with no negative influence on customer satisfaction
Outbound:• Increased conversion rates by 15%
Call Centre
34 > 04/15/2023
EDW please phone home (or “tech vendor actually eats its own dog food”)
• System status and health > Major incidents
– Disk shutdown, reset, etc.> Change control
– Patches, new hardware…> Transfer types
– TVI/Customer Care Link– Manual
• Sent to secure portal > Extensive security> No GUI interface
secure portal
35 > 04/15/2023
How fast can you fix it? Only as quickly as you know there is a problem!
• Priority 1 events: < 10 minutes• Change control: under 6 hours
ELT
ADW
Teradata@YourService
Customer
CustomerServices
CustomerSystem
Secure portal
Pri
ori
ty s
chedule
r
1-5min
1min
1-5min
Every 6 hours
seconds
36 > 04/15/2023
Note that traditional reporting doesn’t go away… “BI is dead, long live BI!”
Prediction #3
“Pervasive” and “event-driven” BI finally come of age – • Data warehouse supports thousands / tens of thousands
of concurrent users, a very diverse mix of queries and multiple Service Level Goals (SLGs);
• Closed-loop integration of operational, analytical processes;
• Majority of data warehouse calls are from SOA applications, not dedicated, “traditional” BI tools – in many cases, users won’t even know that they are interacting with the data warehouse;
• Information management becomes the critical issue in the face of this increased complexity – governance, lineage and validation in particular will have to improve to support widespread SOA deployment.
Market has spoken – and verdict is that the MPP “appliance” platform is best fit for DW…
1980 1985 1990 1995 2000 2005 2010
IBM DB2 Parallel Edition
Oracle ExadataNetezza1st Teradata implementation goes
live at Wells Fargo
DATAllegro
Not all MPP platforms are created equal! Caveat emptor!
Teradata = best technology + best processes + best people
Aster Data Vertica
Greenplum
Kognitio (WhiteCross) NeoView
> 11/04/2009
…but (public) cloud computing is changing the way that many services are delivered
• Essential characteristics > On-demand self-service > Resource pooling [virtualization]> Rapid elasticity > Measured Service [pay per use]
• Service models > Software-as-a-Service (SaaS)> Platform-as-a-Service (PaaS)> Infrastructure-as-a-Service (IaaS)
• Deployment models > Private cloud > Public cloud> Hybrid cloud
Source: Draft NIST Working Definition of Cloud Computing, 8-21-09, version 15http://csrc.nist.gov/groups/SNS/cloud-computing/index.html
“Nearly 90% of organisations expect to maintain or grow use of SaaS in 2009."
Source: Gartner User Survey, November 2008
40 > Oct. 6, 2009
Traditional data exploration architectures
Data Warehouse
basetables
Sandboxes, marts, etc.
• Data Moved across the Enterprise
• Lack of Security• Stale
• Control Issues> Security> Privacy> Completeness
41 > Oct. 6, 2009
Agile Analytics or “Private Cloud Computing”
• Inside the existing EDW
• An internal private cloud> Dynamic mart provisioning> Self service, multi-tenant> Virtualized, chargeback
• Enablers> Support for self-service
provisioning> Advanced Workload
Management > Information governance, to
prevent proliferation of obsolete & redundant data.
Enterprise Data
Warehouse
basetables
Active Workload Management
Sandboxes, marts, etc.
EDW Server & Storage
42 > Oct. 6, 2009
• Dependent virtual marts> Small applications> Prototyping> User education> Short term projects> Mart consolidation
• Extremely private data> Healthcare> Payroll> HR> Etc.
• Proof of Concept> Demos, function test
• Development> Easy access to real data
• Power user sand box> Research, discovery> Trial and error> Hypothesis testing
• Testing> Quality Assurance> New features> Application upgrades
Use cases for internal analytic clouds
Prediction #4
Public cloud computing will continue to evolve; private cloud computing will come of age; virtualization with everything
• Departmental data marts will migrate to the public cloud, making information management (even more) complex;
• Public cloud computing infrastructures will evolve, but performance, security and privacy issues will prevent widespread adoption for data warehousing – “appliances” continue to dominate;
• Private cloud computing / analytical sandboxing becomes an industry-standard best practice;
• Continued focus on virtualization of private computing resources to drive higher levels of hardware utilization and efficiency.
Teradata Copyright 200944 > 04/15/2023
Hadoop
• Hadoop is> A parallel programming framework – open source
implementation of Map/Reduce concept> A file system (HDFS)> A batch job and task dispatching system> Massively parallel> An Apache open source project
• Hadoop is NOT> A database
– No indexes, transactions, recovery journals, SQL> A data warehouse
– Not subject oriented, nonvolatile, time variant, integrated data> A commercial-off-the-shelf BI or ETL tool
Teradata Copyright 200945 > 04/15/2023
Benefits of Hadoop
• Support for 100s to 1000s of server nodes> Extreme scalability> Commodity hardware = low costs
• Data analyzed where it is stored> Low or no data movement
• Easy integration of developer tools> Java, grep, python, etc.
• IT programmers do parallel processing> Wow!
• Batch programming> Complex multi-step processing
Teradata Copyright 200946 > 04/15/2023
Somewhat Equivalent Terminology
Hadoop Teradata
single namespace single image
Map tasks AMP SQL execution
Reduce tasks AMP SQL aggregation
Partition, shuffle and sort row redistribution
intermediate data spool
HDFS Master Name Node Parsing Engine
HDFS Slave Data Node AMP nodes
HDFS replicated data fallback
Job Tracker dispatcher
Task Tracker AMPs
map functions UDFs, in-database functions, push-down
key primary key
Hash Partitioner hash buckets
rack or cluster clique
Teradata Copyright 200947 > 04/15/2023
No Equivalent Function
Hadoop Unique Teradata Unique
varying node counts (extreme scale) Cost based optimizer
tools: grep, Pig, Python, etc. Primary and secondary indexes
automatic re-execution on failure multi-table joins
cloud based FastLoad, MultiLoad, FastExport
process non-relational data BI query tools & spreadsheets
InputFormats pre-execution query cost estimate
InputSplit role based security admin.
Referential Integrity
Views, schemas, data types
row and table locking
spatial and temporal types
historical data persistence
Teradata Copyright 200948 > 04/15/2023
When to Use Which Infrastructure
Complex processesMulti-step processes1,000+ nodes requiredCan’t move the dataExtensive text parsingHouseholding analysis
Data miningSimple reportsData cleansing
“It depends” Iterative discoveryDrill down, OLAPDashboardsBusiness user queryingIntegrated subject areasMultidimensional viewsTrends over timeVisualization toolsText analysis tools
Teradata Copyright 200949 > 04/15/2023
Prediction #5
Hadoop will complement, rather than replace, the RDBMS• Continued adoption of Hadoop for implementation of
process-parallel, c.f.: data-parallel applications by early-adopters who can justify the required investment in skilled resources – and can retain them;
• Limited adoption for general data management unless Hadoop evolves to include traditional DBMS functionality (locking, logging, transactions, recovery, schema support, support for declarative rather than procedural programming, etc., etc.).
Available now…
Presentation to Helsinki TDWI MeetingDecember 14th, 2009Martin Willcox
Teradata Confidential51 >
Teradata Reduces Data Center Burden
0
1000
2000
3000
4000
5000
1992 1994 1996 1998 2000 2002 2004 2006 2008
Wa
tts
/Eq
uip
me
nt
sq
. ft
. 1U & Blade
2U & Greater
Storage Servers
TD Servers
TD Storage
5200 5255 5300 5380 5400 5450 5500
Industry Standard Equipment Power Density Increase
According to ASHRAE
Teradata Equipment Power Density
• Teradata has lower IT equipment power density than industry average• Lowers the demands on data center cooling• Results in energy and floor space savings
5555
Teradata Confidential52 >
Teradata Metric for Platform Efficiency- PPW
• Teradata is now using a method for measuring the energy efficiency of their data warehouse systems
• PPW = Platform energy efficiency metric: Performance per Watt> Based on TPerf - Teradata’s traditional measure of data warehouse
performance capacity > Calculated by dividing TPerf by the total electrical power (in
kilowatts) measured for a complete platform system– Teradata node cabinets– Teradata Enterprise Storage cabinets– BYNET switch hardware
Teradata Data Warehouse Performance - TPerf Electrical Power Consumed for SystemPPW =
Teradata Confidential53 >
Teradata Platform Efficiency
• Teradata has delivered significant Platform Efficiency improvements over the last 6 years> Plotting the PPW for six generation of products demonstrate the
enhancements
5.42
9.59
3.04
8.60
4.98
0.0
2.0
4.0
6.0
8.0
10.0
12.0
53xx (2002-2003)
54xx (2005-2006)
55xx (2007-2008)
Platform Generation (GCA Dates)
Av
era
ge
TP
ER
F/k
W
Performance per Watt improvement: 215%
Performance per Watt improvement: 93%
Teradata Confidential 54
World’s Fastest Data WarehouseLeveraging Teradata Technology
• First 100% enterprise class Solid State Drive analytic appliance> 4 million IOPS* per rack
– Competition claims 1 million IOPS> Loads 7TB+ an hour per rack
– Competition claims 5TB/hr
• SSD is 150 times faster than HDD for typical data warehouse work> 55,000 I/Os per second per drive> Latency up to 1000X faster
• Data protection for high availability
1
3
1
3
1
3
1
3
1
3
1
3
1
3
1
3
1
3
1
3
1
3
* I/O per second at 8KB I/O size
100% Solid State
Drive Appliance
55 > Oct. 6, 2009
Elastic Mart Builder…
56 > Oct. 6, 2009
Enterprise Data Warehouse
Verify Data
…for simple, self-service provisioning in support of “private cloud” deployments
Table Name
xyz
Col3
PI
Type
123abcSample
Col2Col1
CSV
Upload File
Upload
Password
Myfile.csvFile name
User Name
IntegerDateCharDecimal
Create Elastic Mart
Create
UserName MySpecialID
Password
Perm space 500MB
Elastic Marts
POCSandbox
Virtualmart
Import
1 2
34
5
57 > Oct. 6, 2009
Teradata Express for Amazon Public Cloud
• Free Teradata software> Teradata 13 + SLES10> 1TB disk limit> Non-production use
• Runs in Amazon Web Services> Cloud computing Leader
– 100,000+ servers for rent
> Working with Teradata Engineering
> Amazon is migrating to a Teradata EDW
EBS
datasources
Coming soon…
Presentation to Helsinki TDWI MeetingDecember 14th, 2009Martin Willcox
Teradata Confidential12 > 11/04/09
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
Node
HBA HBA
High Performance
20 MB/Sec
SSD
150 MB/Sec
High Capacity
6 MB/Sec
TVS: automatic migration of hot / cold data across heterogeneous storage devices (2H2010 / 1H2011)
Teradata Takes Appliances to the Next Step
Teradata Data Mover, Replication Services, Dual Load, ETL Partners
Active Enterprise Data Warehouse
Analytical Ecosystem Management
Extreme Performance
Extreme Data
DSS
Single, IntegratedActive Data Warehouse
SSD
Entrp.HDD
FatHDD
Centralized ApplianceArchitecture Flexibility withAppliances
DSS Extreme Performance
Extreme Data
Appliances
Teradata Takes Appliances to the Next Step
Analytical Ecosystem Management
Extreme Performance
Extreme Data
DSS
Single, IntegratedActive Data Warehouse
SSD
Entrp.HDD
FatHDD
Centralized ApplianceArchitecture Flexibility
For All Your Analytical Needs!
• “Purpose Built” Platform Family
• Architecture Flexibility
• Products to fit your needs
• Active Data Warehousing as the goal
Teradata Confidential >
Teradata 13.10 - and beyond(Continued focus on performance, scalability, mixed-workload management)
• Native temporal support (TD 13.10, 2H2010);• Enhanced, automated compression (TD 13.10 & TD 14,
2H2011);• Enhanced BAR, Replication (TD 14) -
> Re-architect these components;> Improved performance, reliability;> Improved integration with 3rd party products.
• Eliminate planned downtime / “always on” (TD 14) - > Improved handling of hardware failures / re-start elimination;> Seamlessly re-submit queries impacted by system failure;> Online expansion and upgrades;> Enhanced FALLBACK for less storage-intensive data availability
and improved query performance.
Teradata Confidential >
Teradata 14 - and beyond
• Simplified deployment and maintenance / “autonomic computing (TD 14) - > Automated Physical Layout (indexes, etc);> Automated background compression;> Automated Statistics Collection;> Automated Checksums;> Self-Healing File System.
• Virtualization configurations.