1. Extreme Performance Data Warehousing
Çetin Özbütün
Vice President, Data Warehousing Technologies

2. Challenge: Much More Data to Analyze
Data Warehouse Size and Growth
Source: TDWI Next Generation Data Warehouse Platforms Report, 2009

3. Challenge: No Single Source of Truth Expensive Data Warehouse Architecture
Data Marts
OLAP
ETL
Data Mining
Data Marts
ETL
OLAP
Data Mining

4. DW Strategy Single source of truth Extreme performance
Lower cost of ownership
Deeper Insight

5. DW Strategy Single source of truth Extreme performance
Lower cost of ownership
Deeper Insight

6. Oracle Database 11g Oracle Exadata Database Machine
Consolidate Onto a Single Platform Faster Performance, Single Source of Truth
Data
Marts
Data Mining
Online
Analytics
ETL
Oracle Database 11g Oracle Exadata Database Machine

7. Oracle Exadata Database Machine For 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 7

8. Exadata Smart Scan Improve Query Performance by 10x or More
What Were Yesterday’s Sales?
Select sum(sales) where salesdate= ‘22-Jan-2010’…
Return Sales for Jan
Sum
Off-load data intensive processing to Exadata Storage Server
Exadata Storage Server only returns relevant rows and columns
Wide Infiniband connections eliminate network bottlenecks

9. Exadata Hybrid Columnar Compression Reduce Disk Space Requirements
Uncompressed Data
Data Warehouse Appliances
OLTP Data
DW Data
Archive Data
Oracle

10. Built-in Analytics Secure, Scalable Platform for Advanced Analytics
Oracle OLAP
Analyze and summarize
Oracle Data Mining
Uncover and predict
Complex and predictive analytics embedded into Oracle Database 11g
Reduce cost of additional hardware, management resources
Improve performance by eliminating data movement and duplication

11. Oracle Database 11g The Best Database for Data Warehousing
Real Application Clusters
Advanced Compression
Partitioning
OLAP
Data Mining
World record performance for fast access to information
Manage growing volumes of information cost-effectively
Reduce costs through server and data consolidation

12. The Concept of Partitioning Maintain Consistent Performance as Database Grows
SALES
SALES
SALES
Europe
USA
Jan
Feb
Jan
Feb
Large Table
Difficult to Manage
Partition
Divide and Conquer
Easier to Manage
Improve Performance
Composite Partition
Higher Performance
Match to business needs

13. Partition for Performance Partition Pruning
Sales Table
5/19
What was the total sales amount for May 20 and May ?
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
Performs operations only on relevant partitions
Dramatically reduces amount of data retrieved from disk
Improves query performance and optimizes resource utilization

14. Partition to Manage Data Growth Compress Data and Lower Storage Costs
Archive Data
Read Only Data
Active Data
15-50x Archive Compression
10-15x DW Compression
3x OLTP Compression
Distribute partitions across multiple compression tiers
Free up storage space and execute queries faster
No changes to existing applications

15. In-Memory Parallel Query in Database Tier
In-Memory Parallel Execution Efficient use of memory on clustered servers
In-Memory Parallel Query in Database Tier
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

16. Automated Degree of Parallelism
Queue statements if not enough parallel servers available 64 32 16
When required number of servers are available, execute first statement
Automatically determine DOP 8
Execute immediately
Enough parallel servers available
Optimizer derives the best Degree of Parallelism
Based on resource requirements of all concurrent operations
Less DBA management, better resource utilization 16

17. Relational Star Schema
Summary Management Improve Response Time with Materialized Views
SQL Query
Region
Date
Sales by Date
Sales by Product
Sales by Region
Sales by Channel
Query Rewrite
Relational Star Schema
Products
Channel
Materialized Views
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 17

18. Cube Organized Materialized Views
SQL Query
Summaries
Region
Date
Query Rewrite
Automatic Refresh
Products
Channel
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

19. DW Strategy Single source of truth Extreme performance
Lower cost of ownership
Deeper Insight

20. In-database Analytics Bring 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
Statistics
Data Mining

21. Oracle OLAP Built-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?
Multidimensional analytic engine that analyzes summary data
Offers improved query performance and fast, incremental updates
Embedded in Oracle Database instance and storage

22. Oracle Data Mining Find 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
Collection of data mining algorithms that solve business problems
Simplifies development of predictive BI applications
Embedded in Oracle Database instance and storage

23. Oracle Spatial and OBIEE
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

24. Oracle Exadata Intelligent Warehouse For Industries
Data Models
Business Intelligence
Exadata
Combine deep industry knowledge with data warehousing expertise
Help jump-start design and implementation of data warehouses
Available for Retail and Communications industries

25. Oracle Industry Data Models
Reference Data Model
Aggregate Data Model
Relational (STAR) for BI OLAP for Analytical
Derived Data Model
Data Mining/Complex Reports/Query
Base Data Model (3NF)
Atomic Level of Transaction Data
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

26. Extreme Performance Data Warehousing Integrated Technology Stack
Smart Storage
Database
Data Models
ELT Tools
BI Tools
BI Applications
Single source of truth
Extreme performance
Lower cost of ownership
Deeper Insight

27. Data Warehouse Reference Architecture
Base data warehouse schema
Atomic-level data, 3nf design
Supports general end-user queries
Data feeds to all dependent systems
Application-specific performance structures
Summary data / materialized views
Dimensional view of data
Supports specific end-users, tools, and applications

28. Oracle #1 for Data Warehousing
Source: IDC, July 2009 – “Worldwide Data Warehouse Management Tools 2008 Vendor Shares”