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

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

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

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

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

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 Uncompressed Data Data Warehouse Appliances OLTP Data DW Data Archive 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 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 Difficult to Manage Partition Divide and Conquer Easier to Manage Improve Performance Composite Partition Higher Performance Match to business needs

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 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

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 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

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

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

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

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 Scalability Security Backup & Recovery Simplicity OLAP Statistics Data Mining

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 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

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

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”