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Extreme Performance Data Warehousing
Çetin Özbütün
Vice President, Data Warehousing Technologies
Challenge: Much More Data to Analyze
Data Warehouse Size and Growth
34%
More than 10 TB
17%
25%
3 - 10 TB
19%
18%
21%
1 - 3 TB
12%
500 GB - 1 TB
Less than 500 GB
20%
5%
21%
In 3 Years
Source: TDWI Next Generation Data Warehouse Platforms Report, 2009
Today
Challenge: No Single Source of Truth
Expensive Data Warehouse Architecture
Data
Marts
OLAP
Data Mining
ETL
Data
Marts
ETL
OLAP
Data Mining
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 Platform
Faster Performance, Single Source of Truth
Data
Marts
Online
Analytics
ETL
Data Mining
Oracle Database 11g
Oracle Exadata Database Machine
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
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 22 2010
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
Exadata Hybrid Columnar Compression
Reduce Disk Space Requirements
100
90
Data – Terabytes
80
1.4x
70
60
50
40
2.5 x
3x
30
20
10
10x
15x
DW
Data
Archive
Data
0
Uncompressed Data Warehouse
Data
Appliances
OLTP
Data
Oracle
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
Oracle Database 11g
The Best Database for Data Warehousing
Real Application Clusters
Advanced Compression
Partitioning
OLAP
Data Mining
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


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• World record performance for fast access to information
• Manage growing volumes of information cost-effectively
• Reduce costs through server and data consolidation
The Concept of Partitioning
Maintain Consistent Performance as Database Grows
SALES
SALES
SALES
Europe
USA
Jan
Feb
Jan
Feb
Large Table
Partition
Composite Partition
• Difficult to Manage
• Divide and Conquer
• Higher Performance
• Easier to Manage
• Match to business needs
• Improve Performance
Partition for Performance
Partition Pruning
Sales Table
5/19
What was the total
sales amount for May
20 and May 21 2010?
Select sum(sales_amount)
From SALES
5/20
Where sales_date between
to_date(‘05/20/2010’,’MM/DD/YYYY’)
And
to_date(‘05/22/2010’,’MM/DD/YYYY’);
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
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
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
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
Enough parallel servers available
Execute
immediately
• Optimizer derives the best Degree of Parallelism
• Based on resource requirements of all concurrent operations
• Less DBA management, better resource utilization
Summary Management
Improve Response Time with Materialized Views
Region
SQL Query
Date
Query
Rewrite
Products
Relational Star
Schema
Sales by
Region
Sales by
Date
Sales by
Product
Sales by
Channel
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
Cube Organized Materialized Views
Region
SQL Query
Summaries
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
DW Strategy
• Single source of truth
• Extreme performance
• Lower cost of ownership
• Deeper Insight
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
OLAP
Statistics
Data Mining
•
•
•
•
Scalability
Security
Backup & Recovery
Simplicity
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
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
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
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
Oracle Industry Data Models
Reference Data Model
Aggregate Data Model
Derived Data Model
Relational (STAR) for BI
OLAP for Analytical
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
Extreme Performance Data Warehousing
Integrated Technology Stack
BI Applications
• Single source of truth
BI Tools
ELT Tools
Data Models
Database
Smart Storage
• Extreme performance
• Lower cost of ownership
• Deeper Insight
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
Oracle #1 for Data Warehousing
Source: IDC, July 2009 – “Worldwide Data Warehouse Management Tools 2008 Vendor Shares”