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Extreme Performance Data WarehousingÇetin ÖzbütünVice President, Data Warehousing Technologies

Less than 500 GB

500 GB - 1 TB

1 - 3 TB

3 - 10 TB

More than 10 TB

21%

20%

21%

19%

17%

5%

12%

18%

25%

34%

In 3 Years Today

Source: TDWI Next Generation Data Warehouse Platforms Report, 2009

Challenge: Much More Data to AnalyzeData Warehouse Size and Growth

Challenge: No Single Source of TruthExpensive Data Warehouse Architecture

ETL

OLAP Data Mining

OLAP Data Mining

ETL

Data Marts

Data Marts

DW Strategy

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

DW Strategy

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

Consolidate Onto a Single PlatformFaster Performance, Single Source of Truth

Oracle Database 11gOracle Exadata Database Machine

DataMarts

Data Mining

Online Analytics ETL

Oracle Exadata Database MachineFor OLTP, Data Warehousing & Consolidated Workloads

• Improve query performance by 10x– Better insight into customer requirements– Expand revenue opportunities

• Consolidate OLTP and analytic workloads– Lower admin and maintenance costs– Reduce points of failure

• Integrate analytics and data mining– Complex and predictive analytics

• Lower risk– Streamline deployment– One support contact

Select sum(sales)where salesdate=‘22-Jan-2010’…

Sum

Return Sales for Jan 22 2010

Exadata Smart ScanImprove Query Performance by 10x or More

What Were Yesterday’s

Sales?

• Off-load data intensive processing to Exadata Storage Server

• Exadata Storage Server only returns relevant rows and columns

• Wide Infiniband connections eliminate network bottlenecks

Exadata Hybrid Columnar CompressionReduce Disk Space Requirements

0

10

20

30

40

50

60

70

80

90

100

Da

ta –

Te

rab

yte

s

3x

10x 15x

1.4x

2.5 x

UncompressedData

Data Warehouse Appliances

OLTP Data DW Data

Archive Data

Oracle

Built-in Analytics Secure, Scalable Platform for Advanced Analytics

• Complex and predictive analytics embedded into Oracle Database 11g

• Reduce cost of additional hardware, management resources

• Improve performance by eliminating data movement and duplication

Oracle Data MiningUncover and predict

Oracle OLAPAnalyze and summarize

Oracle Database 11gThe Best Database for Data Warehousing

• World record performance for fast access to information

• Manage growing volumes of information cost-effectively

• Reduce costs through server and data consolidation

Real Application Clusters

Advanced Compression

Partitioning

OLAP

Data Mining

The Concept of PartitioningMaintain Consistent Performance as Database Grows

SALES SALES

Jan Feb

SALES

Jan Feb

Europe

USA

Large Table

• Difficult to Manage

Partition

• Divide and Conquer

• Easier to Manage

• Improve Performance

Composite Partition

• Higher Performance

• Match to business needs

Partition for PerformancePartition Pruning

What was the total sales amount for May 20 and May 21 2010?

Select sum(sales_amount)

From SALES

Where sales_date between

to_date(‘05/20/2010’,’MM/DD/YYYY’)

And

to_date(‘05/22/2010’,’MM/DD/YYYY’);

5/20

5/21

5/22

5/19

Sales Table

• Performs operations only on relevant partitions

• Dramatically reduces amount of data retrieved from disk

• Improves query performance and optimizes resource utilization

Partition to Manage Data Growth Compress Data and Lower Storage Costs

• Distribute partitions across multiple compression tiers

• Free up storage space and execute queries faster

• No changes to existing applications

Active Data

3x OLTP Compression

Read Only Data

10-15x DW Compression

Archive Data

15-50x Archive Compression

In-Memory Parallel ExecutionEfficient use of memory on clustered servers

• Compress more data into available memory on cluster• Intelligent algorithm

– Places table fragments in memory on different nodes• Reduces disk IO and speeds query execution

© 2010 Oracle Corporation

In-Memory Parallel Query in Database Tier

Automated Degree of Parallelism

• Optimizer derives the best Degree of Parallelism

• Based on resource requirements of all concurrent operations

• Less DBA management, better resource utilization

Automatically determine

DOP

Enough parallel servers available

Execute immediately

Queue statements if not enough parallel servers available

When required number of servers are available, execute first statement

8

64 32 16

• Pre-summarized information stored within Oracle Database 11g

• Separate database object, transparent to queries

• Supports sophisticated transparent query rewrite

• Fast incremental refresh of changed data

Summary ManagementImprove Response Time with Materialized Views

Date

Products Channel

SQL QuerySales by

Date

Sales by Product

Sales by Region

Sales by Channel

Region

Materialized ViewsRelational Star

Schema

Query Rewrite

• Exposes Oracle OLAP cubes as relational materialized views

• Provides SQL access to data stored in an OLAP cubes

• Any BI tool or SQL application can leverage OLAP cubes

Region Date

Products Channel

Cube Organized Materialized Views

SQL Query

Automatic Refresh

Query Rewrite

Summaries

DW Strategy

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

In-database AnalyticsBring Algorithms to the Data, Not Data to the Algorithms

• Analytic computations done in the database– Dimensional analysis– Statistical analysis– Data Mining

• Scalability• Security• Backup & Recovery• Simplicity

OLAP

Data Mining

Statistics

• Multidimensional analytic engine that analyzes summary data

• Offers improved query performance and fast, incremental updates

• Embedded in Oracle Database instance and storage

Oracle OLAPBuilt-in Access to Analytic Calculations

• How do sales in the Western region this quarter compare with sales a year ago?

• What will sales next quarter be?

• What factors can we alter to improve the sales forecast?

• Collection of data mining algorithms that solve business problems

• Simplifies development of predictive BI applications

• Embedded in Oracle Database instance and storage

Oracle Data MiningFind Hidden Patterns, Make Predictions

Retail Financial Services

• Customer Segmentation• Response Modeling

• Credit Scoring• Possibility of default

Communications Utilities

• Customer churn• Network intrusion

• Product bundling• Predict power line failure

Healthcare Public Sector

• Patient outcome prediction• Fraud detection

• Tax fraud• Crime analysis

• Enrich BI with map visualization of Oracle Spatial data

• Enable location analysis in reporting, alerts and notifications

• Use maps to guide data navigation, filtering and drill-down

• Increase ROI from geospatial and non-spatial data

Oracle Spatial and OBIEE

Data Models

Exadata

Business Intelligence

Oracle Exadata Intelligent WarehouseFor Industries

• Combine deep industry knowledge with data warehousing expertise

• Help jump-start design and implementation of data warehouses

• Available for Retail and Communications industries

• Combine deep industry knowledge with data warehousing expertise

• Help jump-start design and implementation of data warehouses

• Optimized for Oracle Database 11g and Oracle Exadata

Reference Data Model

Aggregate Data Model

Relational (STAR) for BIOLAP for Analytical

Derived Data Model

Data Mining/Complex Reports/Query

Base Data Model (3NF)Atomic Level of Transaction Data

Oracle Industry Data Models

Extreme Performance Data Warehousing Integrated Technology Stack

• Single source of truth

• Extreme performance

• Lower cost of ownership

• Deeper Insight

Smart StorageSmart Storage

DatabaseDatabase

Data ModelsData Models

ELT ToolsELT Tools

BI ToolsBI Tools

BI ApplicationsBI Applications

Data Warehouse Reference Architecture

Base data warehouse schemaAtomic-level data, 3nf designSupports general end-user queriesData feeds to all dependent systems

Application-specific performance structuresSummary data / materialized viewsDimensional view of data Supports specific end-users, tools, and applications

Oracle #1 for Data Warehousing

Source: IDC, July 2009 – “Worldwide Data Warehouse Management Tools 2008 Vendor Shares”