Information BuildersMay 11, 2012

Information Builders (Canada) Inc.

WebFOCUS HyperstageAnalyze/Report from large Volumes of Data

Reporting

Query & Analysis

Dashboards

Information Delivery

PerformanceManagement

EnterpriseSearch

Visualization& Mapping

Data UpdatingPredictive Analytics

MS Office &e-Publishing

Extended BI

Core BI

Extensions to the WebFOCUS platform

allow you to build more application

types at a lower cost

Business toBusiness

Data Warehouse& ETL

Master DataManagement

Data Profiling & Data Quality

Business ActivityMonitoring

High PerformanceData Store

Mobile Applications

WebFOCUSHigher Adoption & Reuse with Lower TCO

High PerformanceData Store

Reporting

Query & Analysis

Dashboards

Information Delivery

PerformanceManagement

EnterpriseSearch

Visualization& Mapping

Data UpdatingPredictive Analytics

MS Office &e-Publishing

Extended BI

Core BI

Extensions to the WebFOCUS platform

allow you to build more application

types at a lower cost

Business toBusiness

Data Warehouse& ETL

Master DataManagement

Data Profiling & Data Quality

Business ActivityMonitoring

Mobile Applications

WebFOCUS High Performance Data Store

The Business Challenge

Big Data

Copyright 2007, Information Builders. Slide 4

Data Storage

Time

Machine- GeneratedData

Human-GeneratedData

Today’s Top Data-Management ChallengeBig Data and Machine Generated Data

Source: KEEPING UP WITH EVER-EXPANDING ENTERPRISE DATA ( Joseph McKendrick Unisphere Research October 2010)

How Performance Issues are Typically Addressed – by Pace of Data Growth

IT Managers try to mitigate these response times …..

Don't Know / Unsure

Upgrade networking infrastructure

Archive older data on other systems

Upgrade/expand storage systems

Upgrade server hardware/processors

Tune or upgrade existing databases

0% 20% 40% 60% 80% 100%

7%

21%

30%

33%

54%

66%

4%

32%

44%

60%

70%

75%

High Growth

Low Growth

When organizations have long running queries that limit the business, the response is often to spend much more time and money to resolve the problem

Classic Approaches and ChallengesData Warehousing

Traditional Data Warehousing

Labour intensive, heavy indexing, aggregations and partitioning

Hardware intensive: massive storage; big servers

Expensive and complex

More Data, More Data Sources

More Kinds of Output Needed by More Users,

More Quickly

Limited Resources and Budget

0101010101010101010101010101

0101010101010101010101010

0101010101010101010101

1

0101010101010101010101

10

1010 1011001

0 110

01

1

0

01

101

010101

1

1

0101

0

1010101

10 0101

10

01

10

0110

1

0

10101

01 010 01 0101

011

10100101

1

01

0

10

1010 101 10010 1

10

01

1

0

01

101

0

10101

10

0101010101010101010101010

0101010101010101010101010101

1

10110

0

101

1010 10 1101

010

0

0 101 0010

0

Real time data

Multiple databases

External Sources

New Demands: Larger transaction volumes driven by the internetImpact of Cloud ComputingMore -> Faster -> Cheaper

Data Warehousing Matures: Near real time updatesIntegration with master data managementData mining using discrete business transactionsProvision of data for business critical applications

Early Data Warehouse Characteristics:Integration of internal systemsMonthly and weekly loadsHeavy use of aggregates

Classic Approaches and ChallengesData Warehousing – Growing Demands

Classic Approaches and ChallengesDealing with Large Data

INDEXES

CUBES/OLAP

Classic Approaches and Challenges Limitations of Indexes Increased Space requirements

Sum of Index Space requirements can exceed the source DB Index Management

Increases Load times Building the index

Predefines a fixed access path

Classic Approaches and Challenges Limitations of OLAP Cube technology has limited scalability

Number of dimensions is limited Amount of data is limited

Cube technology is difficult to update (add Dimension) Usually requires a complete rebuild Cube builds are typically slow New design results in a new cube

Limitations of RowsThese Solutions Contribute to Operational Limitations1. Impediments to business agility

wait for DBAs to create indexes or other tuning structures, thereby delaying access to data.

Indexes significantly slow data-loading operations and increase the size of the database, sometimes by a factor of 2x.

2. Loss of data and time fidelity: ETL operations typically performed in batch during non-business hours. Delay access to data, often result in mismatches between operational and

analytic databases.3. Limited ad hoc capability:

Response times for ad hoc queries increase as the volume of data grows. Unanticipated queries (where DBAs have not tuned the database in

advance) can result in unacceptable response times.4. Unnecessary expenditures:

Attempts to improve performance using hardware acceleration and database tuning schemes raise the capital costs of equipment and the operational costs of database administration.

Added complexity of managing a large database diverts operational budgets away from more urgent IT projects.

Pivoting Your Perspective:Columnar Technology ….

Copyright 2007, Information Builders. Slide 13

Row-based databases are ubiquitous because so many of our most important business systems are transactional.

Row-oriented databasesare well suited for transactional environments, such as a call center where a customer’s entire record is required when their profileis retrieved and/or when fields are frequently updated.

The Ubiquity of Rows

But - Disk I/O becomes a substantial limiting factor since a row-oriented design forces the database to retrieve all column data for any query.

30 columns

50 millionsRows

The Limitation of Rows

Row Oriented (1, Smith, New York, 50000; 2, Jones, New York, 65000; 3, Fraser, Boston, 40000; 4, Fraser, Boston, 70000)

Works well if all the columns are needed for every query. Efficient for transactional processing if all the data for the row is available

Works well with aggregate results (sum, count, avg. ) Only columns that are relevant need to be touched Consistent performance with any database design Allows for very efficient compression

Column Oriented (1, 2, 3, 4; Smith, Jones, Fraser, Fraser; New York, New York, Boston, Boston, 50000, 65000, 40000, 70000)

Employee Id

1

2

3

Name

Smith

Jones

Fraser

Location

New York

New York

Boston

Sales

50,000

65,000

40,000

4 Fraser Boston 70,000

Pivoting Your PerspectiveColumnar Technology

WebFOCUS Hyperstage

Copyright 2007, Information Builders. Slide 16

IntroducingWebFOCUS Hyperstage Mission

Improve database performance for WebFOCUS applications with less hardware, no database tuning, and easy migration

What is WebFOCUS Hyperstage High performance analytic data store Designed to handle business-driven queries on large volumes of data

without IT intervention. Easy to implement and manage, Hyperstage provides answers to your

business users need at a price you can afford Advantages

Dramatically increase performance of WebFOCUS applications Disk footprint reduced with powerful compression algorithm = faster

response time Embedded ETL for seamless migration of existing analytical databases

No change in query or application required Includes optimized Hyperstage Adapter WebFOCUS metadata can be used to define hierarchies and drill

paths to navigate the star schema17

Hyperstage Engine

Knowledge Grid

Compressor

BulkLoader

• Unmatched Administrative Simplicity • No Indexes• No data partitioning• No Manual tuning

Introducing WebFOCUS HyperstageHow it is architected

Combines a columnar database with intelligence we call the Knowledge Grid

to deliver fast query responses.

Improve database performance for WebFOCUS applications with less

hardware, no database tuning, and easy migration

Introducing WebFOCUS HyperstageWhat it means for Customers

Self-managing: 90% less administrative effort

Low-cost: More than 50% less than alternative solutions

Scalable, high-performance: Up to 50 TB using a single industry standard server

Fast queries: Ad hoc queries are as fast as anticipated queries, so users have total flexibility

Compression: Data compression of 10:1 to 40:1 means a lot less storage is needed, it might mean you can get the entire database in memory!

Create Information(Metadata) about the data,

and, upon Load, automatically …

Uses the metadata whenProcessing a query to

Eliminate / reduce need to access data

Architecture Benefits

o Stores it in the Knowledge Grid (KG)o KG Is loaded into Memoryo Less than 1% of compressed data Size

o The less data that needs to be accessed, the faster the response o Sub-second responses when answered by KG

o No Need to partition data, create/maintain indexes projections, or tune for performanceo Ad hoc queries are as fast as static queries, so users have total flexibility

Introducing WebFOCUS HyperstageHow it works

Smarter Architecture

No maintenance No query planning No partition schemes No DBA

Data Packs – data stored in manageably sized, highly compressed data packs

Knowledge Grid – statistics and metadata “describing” the super-compressed data

Column Orientation

Data compressed using algorithms tailored to data type

WebFOCUS Hyperstage EngineHow it works

Summary

Copyright 2007, Information Builders. Slide 22

Business Intelligence – Meeting Requirements

No indexes No partitions No views No materialized aggregates

Value propositionLow IT overheadAllows for autonomy from ITEase of implementationFast time to marketLess Hardware Lower TCO

No DBA Required!

WebFOCUS HyperstageThe Big Deal

WebFOCUS Hyperstage AdapterWhat it looks like

WebFOCUS Hyperstage AdapterWhat it looks like

Example – Focus to Hyperstage Compression 243639 Rows

Q&A

Copyrigh

t 200

7, Infor

matio

n Builders.

Slide

28