Part II Optimizing Data Warehouses
This section deals with the physical design of a data warehouse.
It contains the following chapters:
Table of Contents
Expand AllCollapse All- Title and Copyright Information
- Preface
- Changes in This Release for Oracle Database Data Warehousing Guide
- Part I Data Warehouse - Fundamentals
- 1 Introduction to Data Warehousing Concepts
- 2 Data Warehousing Logical Design
-
3
Data Warehousing Physical Design
- 3.1 Moving from Logical to Physical Design
-
3.2
About Physical Design
- 3.2.1 Physical Design Structures
- 3.2.2 About Views in Data Warehouses
- 3.2.3 About Integrity Constraints in Data Warehouses
- 3.2.4 About Indexes and Partitioned Indexes in Data Warehouses
- 3.2.5 About Materialized Views in Data Warehouses
- 3.2.6 About Dimensions in Data Warehouses
-
4
Data Warehousing Optimizations and Techniques
-
4.1
Using Indexes in Data Warehouses
- 4.1.1 About Using Bitmap Indexes in Data Warehouses
- 4.1.2 Benefits of Indexes for Data Warehousing Applications
- 4.1.3 About Cardinality and Bitmap Indexes
- 4.1.4 How to Determine Candidates for Using a Bitmap Index
- 4.1.5 Using Bitmap Join Indexes in Data Warehouses
- 4.1.6 Using B-Tree Indexes in Data Warehouses
- 4.1.7 Using Index Compression
- 4.1.8 Choosing Between Local Indexes and Global Indexes
-
4.2
Using Integrity Constraints in a Data Warehouse
- 4.2.1 Overview of Constraint States
-
4.2.2
Typical Data Warehouse Integrity Constraints
- 4.2.2.1 UNIQUE Constraints in a Data Warehouse
- 4.2.2.2 FOREIGN KEY Constraints in a Data Warehouse
- 4.2.2.3 RELY Constraints in a Data Warehouse
- 4.2.2.4 NOT NULL Constraints in a Data Warehouse
- 4.2.2.5 Integrity Constraints and Parallelism in a Data Warehouse
- 4.2.2.6 Integrity Constraints and Partitioning in a Data Warehouse
- 4.2.2.7 View Constraints in a Data Warehouse
- 4.3 About Parallel Execution in Data Warehouses
- 4.4 About Optimizing Storage Requirements in Data Warehouses
-
4.5
Optimizing Star Queries and 3NF Schemas
- 4.5.1 Optimizing Star Queries
-
4.5.2
Using Star Transformation
- 4.5.2.1 Star Transformation with a Bitmap Index
- 4.5.2.2 Execution Plan for a Star Transformation with a Bitmap Index
- 4.5.2.3 Star Transformation with a Bitmap Join Index
- 4.5.2.4 Execution Plan for a Star Transformation with a Bitmap Join Index
- 4.5.2.5 How Oracle Chooses to Use Star Transformation
- 4.5.2.6 Star Transformation Restrictions
- 4.5.3 Optimizing Third Normal Form Schemas
- 4.5.4 Optimizing Star Queries Using VECTOR GROUP BY Aggregation
- 4.6 About Approximate Query Processing
- 4.7 About Approximate Top-N Query Processing
-
4.1
Using Indexes in Data Warehouses
- Part II Optimizing Data Warehouses
-
5
Basic Materialized Views
-
5.1
Overview of Data Warehousing with Materialized Views
- 5.1.1 About Materialized Views for Data Warehouses
- 5.1.2 About Materialized Views for Distributed Computing
- 5.1.3 About Materialized Views for Mobile Computing
- 5.1.4 The Need for Materialized Views
- 5.1.5 Components of Summary Management
- 5.1.6 Data Warehousing Terminology
- 5.1.7 About Materialized View Schema Design
- 5.1.8 About Loading Data into Data Warehouses
- 5.1.9 Overview of Materialized View Management Tasks
- 5.2 Types of Materialized Views
-
5.3
Creating Materialized Views
- 5.3.1 Creating Materialized Views with Column Alias Lists
- 5.3.2 Creating Materialized Views Based on Hybird Partitioned Tables
- 5.3.3 About Materialized Views Names
- 5.3.4 About Storage And Table Compression for Materialized Views
- 5.3.5 About Build Methods for Materialized Views
- 5.3.6 About Enabling Query Rewrite for Materialized Views
- 5.3.7 About Query Rewrite Restrictions
-
5.3.8
About Refresh Options for Materialized Views
- 5.3.8.1 About Refresh Modes for Materialized Views
- 5.3.8.2 About Types of Materialized View Refresh
- 5.3.8.3 About Using Trusted Constraints and Materialized View Refresh
- 5.3.8.4 General Restrictions on Fast Refresh
- 5.3.8.5 Restrictions on Fast Refresh on Materialized Views with Joins Only
- 5.3.8.6 Restrictions on Fast Refresh on Materialized Views with Aggregates
- 5.3.8.7 Restrictions on Fast Refresh on Materialized Views with UNION ALL
- 5.3.8.8 About Achieving Refresh Goals
- 5.3.8.9 Refreshing Nested Materialized Views
- 5.3.9 ORDER BY Clause in Materialized Views
- 5.3.10 Using Oracle Enterprise Manager to Create Materialized Views
- 5.3.11 Using Materialized Views with NLS Parameters
- 5.3.12 Adding Comments to Materialized Views
- 5.4 Creating Materialized View Logs
- 5.5 Creating Materialized Views Based on Approximate Queries
- 5.6 Creating a Materialized View Containing Bitmap-based COUNT(DISTINCT) Functions
- 5.7 Registering Existing Materialized Views
- 5.8 Choosing Indexes for Materialized Views
- 5.9 Dropping Materialized Views
- 5.10 Analyzing Materialized View Capabilities
-
5.1
Overview of Data Warehousing with Materialized Views
-
6
Advanced Materialized Views
- 6.1 About Partitioning and Materialized Views
- 6.2 About Materialized Views in Analytic Processing Environments
- 6.3 About Materialized Views and Models
- 6.4 About Security Issues with Materialized Views
- 6.5 Invalidating Materialized Views
- 6.6 Altering Materialized Views
-
6.7
Using Real-time Materialized Views
- 6.7.1 Overview of Real-time Materialized Views
- 6.7.2 Creating Real-time Materialized Views
- 6.7.3 Converting an Existing Materialized View into a Real-time Materialized View
- 6.7.4 Enabling Query Rewrite to Use Real-time Materialized Views
- 6.7.5 Using Real-time Materialized Views During Query Rewrite
- 6.7.6 Using Real-time Materialized Views for Direct Query Access
- 6.7.7 Listing Real-time Materialized Views
- 6.7.8 Improving Real-time Materialized Views Performance
-
7
Refreshing Materialized Views
-
7.1
About Refreshing Materialized Views
- 7.1.1 About Complete Refresh for Materialized Views
- 7.1.2 About Fast Refresh for Materialized Views
- 7.1.3 About Partition Change Tracking (PCT) Refresh for Materialized Views
- 7.1.4 About the Out-of-Place Refresh Option
- 7.1.5 About ON COMMIT Refresh for Materialized Views
- 7.1.6 About ON STATEMENT Refresh for Materialized Views
- 7.1.7 About Manual Refresh Using the DBMS_MVIEW Package
- 7.1.8 Refreshing Specific Materialized Views with REFRESH
- 7.1.9 Refreshing All Materialized Views with REFRESH_ALL_MVIEWS
- 7.1.10 Refreshing Dependent Materialized Views with REFRESH_DEPENDENT
- 7.1.11 About Using Job Queues for Refresh
- 7.1.12 When Fast Refresh is Possible
- 7.1.13 Refreshing Materialized Views Based on Approximate Queries
- 7.1.14 About Refreshing Dependent Materialized Views During Online Table Redefinition
- 7.1.15 Recommended Initialization Parameters for Parallelism
- 7.1.16 Monitoring a Refresh
- 7.1.17 Checking the Status of a Materialized View
- 7.1.18 Scheduling Refresh of Materialized Views
-
7.2
Tips for Refreshing Materialized Views
- 7.2.1 Tips for Refreshing Materialized Views with Aggregates
- 7.2.2 Tips for Refreshing Materialized Views Without Aggregates
- 7.2.3 Tips for Refreshing Nested Materialized Views
- 7.2.4 Tips for Fast Refresh with UNION ALL
- 7.2.5 Tips for Fast Refresh with Commit SCN-Based Materialized View Logs
- 7.2.6 Tips After Refreshing Materialized Views
- 7.3 Using Materialized Views with Partitioned Tables
- 7.4 Refreshing Materialized Views Based on Hybrid Partitioned Tables
- 7.5 Using Partitioning to Improve Data Warehouse Refresh
- 7.6 Optimizing DML Operations During Refresh
-
7.1
About Refreshing Materialized Views
-
8
Synchronous Refresh
-
8.1
About Synchronous Refresh for Materialized Views
- 8.1.1 What Is Synchronous Refresh?
- 8.1.2 Why Use Synchronous Refresh?
- 8.1.3 Registering Tables and Materialized Views for Synchronous Refresh
- 8.1.4 Specifying Change Data for Refresh
- 8.1.5 Synchronous Refresh Preparation and Execution
-
8.1.6
Materialized View Eligibility Rules and Restrictions for Synchronous Refresh
- 8.1.6.1 Synchronous Refresh Restrictions: Partitioning
- 8.1.6.2 Synchronous Refresh Restrictions: Refresh Options
- 8.1.6.3 Synchronous Refresh Restrictions: Constraints
- 8.1.6.4 Synchronous Refresh Restrictions: Tables
- 8.1.6.5 Synchronous Refresh Restrictions: Materialized Views
- 8.1.6.6 Synchronous Refresh Restrictions: Materialized Views with Aggregates
- 8.2 Using Synchronous Refresh for Materialized Views
- 8.3 Using Synchronous Refresh Groups
- 8.4 Specifying and Preparing Change Data for Synchronous Refresh
-
8.5
Troubleshooting Synchronous Refresh Operations
- 8.5.1 Overview of the Status of Refresh Operations
- 8.5.2 How PREPARE_REFRESH Sets the STATUS Fields
- 8.5.3 Examples of Preparing for Synchronous Refresh Using PREPARE_REFRESH
- 8.5.4 How EXECUTE_REFRESH Sets the Status Fields During Synchronous Refresh
- 8.5.5 Examples of Executing Synchronous Refresh Using EXECUTE_REFRESH
- 8.5.6 Example of EXECUTE_REFRESH with Constraint Violations
- 8.6 Performing Synchronous Refresh Eligibility Analysis
- 8.7 Overview of Synchronous Refresh Security Considerations
-
8.1
About Synchronous Refresh for Materialized Views
-
9
Monitoring Materialized View Refresh Operations
- 9.1 About Materialized View Refresh Statistics
- 9.2 Overview of Managing Materialized View Refresh Statistics
- 9.3 About Data Dictionary Views that Store Materialized View Refresh Statistics
- 9.4 Collecting Materialized View Refresh Statistics
- 9.5 Retaining Materialized View Refresh Statistics
- 9.6 Viewing Materialized View Refresh Statistics Settings
- 9.7 Purging Materialized View Refresh Statistics
-
9.8
Viewing Materialized View Refresh Statistics
- 9.8.1 Viewing Basic Refresh Statistics for a Materialized View
- 9.8.2 Viewing Detailed Statistics for Each Materialized View Refresh Operation
- 9.8.3 Viewing Change Data Statistics During Materialized View Refresh Operations
- 9.8.4 Viewing the SQL Statements Associated with A Materialized View Refresh Operation
- 9.9 Analyzing Materialized View Refresh Performance Using Refresh Statistics
- 10 Dimensions
-
11
Basic Query Rewrite for Materialized Views
- 11.1 Overview of Query Rewrite
-
11.2
Ensuring that Query Rewrite Takes Effect
- 11.2.1 Enabling Query Rewrite for Materialized Views
- 11.2.2 About Initialization Parameters for Query Rewrite
- 11.2.3 Controlling Query Rewrite
- 11.2.4 About the Accuracy of Query Rewrite
- 11.2.5 About Privileges for Enabling Query Rewrite
- 11.2.6 Sample Schema and Materialized Views
- 11.2.7 How to Verify if Query Rewrite Occurred
- 11.3 Example of Query Rewrite
-
12
Advanced Query Rewrite for Materialized Views
-
12.1
How Oracle Rewrites Queries
- 12.1.1 About Cost-Based Optimization and Query Rewrite
- 12.1.2 General Query Rewrite Methods
- 12.1.3 About Checks Made by Query Rewrite
- 12.1.4 About Query Rewrite Using Dimensions
-
12.2
Types of Query Rewrite
- 12.2.1 Query Rewrite Method 1: Text Match Rewrite
- 12.2.2 Query Rewrite Method 2: Join Back
- 12.2.3 Query Rewrite Method 3: Aggregate Computability
- 12.2.4 Query Rewrite Method 4: Aggregate Rollup
- 12.2.5 Query Rewrite Method 5: Rollup Using a Dimension
-
12.2.6
Query Rewrite Method 6: When Materialized Views Have Only a Subset of Data
- 12.2.6.1 Query Rewrite Definitions When Materialized Views Have Only a Subset of Data
- 12.2.6.2 Selection Categories When Materialized Views Have Only a Subset of Data
- 12.2.6.3 Examples of Query Rewrite Selection
- 12.2.6.4 About Handling of the HAVING Clause in Query Rewrite
- 12.2.6.5 About Query Rewrite When the Materialized View has an IN-List
- 12.2.7 Partition Change Tracking (PCT) Rewrite
- 12.2.8 About Query Rewrite Using Multiple Materialized Views
-
12.3
Other Query Rewrite Considerations
- 12.3.1 About Query Rewrite Using Nested Materialized Views
- 12.3.2 About Query Rewrite in the Presence of Inline Views
- 12.3.3 About Query Rewrite Using Remote Tables
- 12.3.4 About Query Rewrite in the Presence of Duplicate Tables
- 12.3.5 About Query Rewrite Using Date Folding
- 12.3.6 About Query Rewrite Using View Constraints
- 12.3.7 About Query Rewrite in the Presence of Hybrid Partitioned Tables
- 12.3.8 Query Rewrite Using Set Operator Materialized Views
- 12.3.9 About Query Rewrite in the Presence of Grouping Sets
- 12.3.10 Query Rewrite in the Presence of Window Functions
- 12.3.11 Query Rewrite and Expression Matching
- 12.3.12 Cursor Sharing and Bind Variables During Query Rewrite
- 12.3.13 Handling Expressions in Query Rewrite
- 12.4 Advanced Query Rewrite Using Equivalences
- 12.5 Creating Result Cache Materialized Views with Equivalences
- 12.6 Query Rewrite and Materialized Views Based on Approximate Queries
- 12.7 Query Rewrite and Materialized Views Based on Bitmap-based COUNT(DISTINCT) Functions
-
12.8
Verifying that Query Rewrite has Occurred
- 12.8.1 Using EXPLAIN PLAN with Query Rewrite
-
12.8.2
Using the EXPLAIN_REWRITE Procedure with Query Rewrite
- 12.8.2.1 DBMS_MVIEW.EXPLAIN_REWRITE Syntax
- 12.8.2.2 Using REWRITE_TABLE to View EXPLAIN_REWRITE Output
- 12.8.2.3 Using a Varray to View EXPLAIN_REWRITE Output
- 12.8.2.4 EXPLAIN_REWRITE Benefit Statistics
- 12.8.2.5 Support for Query Text Larger than 32KB in EXPLAIN_REWRITE
- 12.8.2.6 About EXPLAIN_REWRITE and Multiple Materialized Views
- 12.8.2.7 About EXPLAIN_REWRITE Output
-
12.9
Design Considerations for Improving Query Rewrite Capabilities
- 12.9.1 Query Rewrite Considerations: Constraints
- 12.9.2 Query Rewrite Considerations: Dimensions
- 12.9.3 Query Rewrite Considerations: Outer Joins
- 12.9.4 Query Rewrite Considerations: Text Match
- 12.9.5 Query Rewrite Considerations: Aggregates
- 12.9.6 Query Rewrite Considerations: Grouping Conditions
- 12.9.7 Query Rewrite Considerations: Expression Matching
- 12.9.8 Query Rewrite Considerations: Date Folding
- 12.9.9 Query Rewrite Considerations: Statistics
- 12.9.10 Query Rewrite Considerations: Hints
-
12.1
How Oracle Rewrites Queries
-
13
Attribute Clustering
-
13.1
About Attribute Clustering
- 13.1.1 Methods of Clustering Data
- 13.1.2 Types of Attribute Clustering
- 13.1.3 Example: Attribute Clustered Table
- 13.1.4 Guidelines for Using Attribute Clustering
- 13.1.5 Advantages of Attribute-Clustered Tables
- 13.1.6 About Defining Attribute Clustering for Tables
- 13.1.7 About Specifying When Attribute Clustering Must be Performed
-
13.2
Attribute Clustering Operations
- 13.2.1 Privileges for Attribute-Clustered Tables
- 13.2.2 Creating Attribute-Clustered Tables with Linear Ordering
- 13.2.3 Creating Attribute-Clustered Tables with Interleaved Ordering
-
13.2.4
Maintaining Attribute Clustering
- 13.2.4.1 Adding Attribute Clustering to an Existing Table
- 13.2.4.2 Modifying Attribute Clustering Definitions
- 13.2.4.3 Dropping Attribute Clustering for an Existing Table
- 13.2.4.4 Using Hints to Control Attribute Clustering for DML Operations
- 13.2.4.5 Overriding Table-level Settings for Attribute Clustering During DDL Operations
- 13.2.4.6 Clustering Table Data During Online Table Redefinition
-
13.3
Viewing Attribute Clustering Information
- 13.3.1 Determining if Attribute Clustering is Defined for Tables
- 13.3.2 Viewing Attribute-Clustering Information for Tables
- 13.3.3 Viewing Information About the Columns on Which Attribute Clustering is Performed
- 13.3.4 Viewing Information About Dimensions and Joins on Which Attribute Clustering is Performed
-
13.1
About Attribute Clustering
-
14
Using Zone Maps
- 14.1 About Zone Maps
-
14.2
Zone Map Operations
- 14.2.1 Privileges Required for Zone Maps
- 14.2.2 Creating Zone Maps
- 14.2.3 Modifying Zone Maps
- 14.2.4 Dropping Zone Maps
- 14.2.5 Compiling Zone Maps
- 14.2.6 Controlling the Use of Zone Maps
- 14.2.7 Maintaining Zone Maps
- 14.3 Refresh and Staleness of Zone Maps
- 14.4 Performing Pruning Using Zone Maps
- 14.5 Viewing Zone Map Information
-
5
Basic Materialized Views
- Part III Data Movement/ETL
- 15 Data Movement/ETL Overview
- 16 Extraction in Data Warehouses
- 17 Transportation in Data Warehouses
-
18
Loading and Transformation in Data Warehouses
- 18.1 Overview of Loading and Transformation in Data Warehouses
- 18.2 Loading Mechanisms for Data Warehouses
- 18.3 Transformation Mechanisms in Data Warehouses
- 18.4 Error Logging and Handling Mechanisms
- 18.5 Loading and Transformation Scenarios
- Part IV Relational Analytics
-
19
SQL for Analysis and Reporting
- 19.1 Overview of SQL for Analysis and Reporting
-
19.2
Ranking, Windowing, and Reporting Functions
-
19.2.1
Ranking Functions
-
19.2.1.1
RANK and DENSE_RANK Functions
- 19.2.1.1.1 Ranking Order in RANK and DENSE_RANK Functions
- 19.2.1.1.2 Ranking on Multiple Expressions
- 19.2.1.1.3 Example: Difference Between RANK and DENSE_RANK
- 19.2.1.1.4 Ranking Within Groups: Example
- 19.2.1.1.5 Example: Per Cube and Rollup Group Ranking
- 19.2.1.1.6 Examples: Treatment of NULLs in Ranking Functions
- 19.2.1.2 APPROX_RANK Function
- 19.2.1.3 Bottom N Ranking Functions
- 19.2.1.4 CUME_DIST Function
- 19.2.1.5 PERCENT_RANK Function
- 19.2.1.6 NTILE Function
- 19.2.1.7 ROW_NUMBER Function
-
19.2.1.1
RANK and DENSE_RANK Functions
-
19.2.2
Windowing Functions
- 19.2.2.1 About Treatment of NULLs as Input to Window Functions
- 19.2.2.2 Windowing Functions with Logical Offset
- 19.2.2.3 Centered Aggregate Function
- 19.2.2.4 Windowing Aggregate Functions in the Presence of Duplicates
- 19.2.2.5 Varying Window Size for Each Row
- 19.2.2.6 Windowing Aggregate Functions with Physical Offsets
- 19.2.2.7 Parallel Partition-Wise Operations with Windowing Functions
- 19.2.3 Reporting Functions
- 19.2.4 LAG/LEAD Functions
- 19.2.5 FIRST_VALUE, LAST_VALUE, and NTH_VALUE Functions
-
19.2.1
Ranking Functions
- 19.3 Advanced Aggregates for Analysis
- 19.4 Pivoting Operations
- 19.5 Unpivoting Operations
- 19.6 Data Densification for Reporting
- 19.7 Time Series Calculations on Densified Data
- 19.8 Miscellaneous Analysis and Reporting Capabilities
- 19.9 Limiting SQL Rows
-
20
SQL for Aggregation in Data Warehouses
- 20.1 Overview of SQL for Aggregation in Data Warehouses
- 20.2 ROLLUP Extension to GROUP BY
- 20.3 CUBE Extension to GROUP BY
- 20.4 GROUPING Functions
- 20.5 GROUPING SETS Expression
- 20.6 About Composite Columns and Grouping
- 20.7 Concatenated Groupings and Data Aggregation
- 20.8 Considerations when Using Aggregation in Data Warehouses
- 20.9 Computation Using the WITH Clause
- 20.10 Working with Hierarchical Cubes in SQL
-
21
SQL for Pattern Matching
- 21.1 Overview of Pattern Matching in Data Warehouses
- 21.2 Basic Topics in Pattern Matching
-
21.3
Pattern Matching Details
- 21.3.1 PARTITION BY: Logically Dividing the Rows into Groups
- 21.3.2 ORDER BY: Logically Ordering the Rows in a Partition
- 21.3.3 [ONE ROW | ALL ROWS] PER MATCH: Choosing Summaries or Details for Each Match
- 21.3.4 MEASURES: Defining Calculations for Use in the Query
- 21.3.5 PATTERN: Defining the Row Pattern to Be Matched
- 21.3.6 SUBSET: Defining Union Row Pattern Variables
- 21.3.7 DEFINE: Defining Primary Pattern Variables
- 21.3.8 AFTER MATCH SKIP: Defining Where to Restart the Matching Process After a Match Is Found
- 21.3.9 Expressions in MEASURES and DEFINE
- 21.3.10 Row Pattern Output
- 21.4 Advanced Topics in Pattern Matching
- 21.5 Rules and Restrictions in Pattern Matching
- 21.6 Examples of Pattern Matching
-
22
SQL for Modeling
- 22.1 Overview of SQL Modeling in Data Warehouses
-
22.2
Basic Topics in SQL Modeling
- 22.2.1 Base Schema for SQL Modeling Examples
- 22.2.2 MODEL Clause Syntax
- 22.2.3 Keywords in SQL Modeling
- 22.2.4 About Cell Referencing in SQL Modeling
- 22.2.5 About Rules for SQL Modeling
- 22.2.6 Order of Evaluation of SQL Modeling Rules
- 22.2.7 Global and Local Keywords for SQL Modeling Rules
- 22.2.8 UPDATE, UPSERT, and UPSERT ALL Behavior
- 22.2.9 Treatment of NULLs and Missing Cells in SQL Modeling
- 22.2.10 About Reference Models in SQL Modeling
-
22.3
Advanced Topics in SQL Modeling
- 22.3.1 FOR Loops in SQL Modeling
- 22.3.2 Iterative Models in SQL Modeling
- 22.3.3 Rule Dependency in AUTOMATIC ORDER Models
- 22.3.4 Ordered Rules in SQL Modeling
- 22.3.5 Analytic Functions in SQL Modeling
- 22.3.6 Unique Dimensions Versus Unique Single References in SQL Modeling
- 22.3.7 Rules and Restrictions when Using SQL for Modeling
- 22.4 Performance Considerations with SQL Modeling
-
22.5
Examples of SQL Modeling
- 22.5.1 SQL Modeling Example 1: Calculating Sales Differences
- 22.5.2 SQL Modeling Example 2: Calculating Percentage Change
- 22.5.3 SQL Modeling Example 3: Calculating Net Present Value
- 22.5.4 SQL Modeling Example 4: Calculating Using Simultaneous Equations
- 22.5.5 SQL Modeling Example 5: Calculating Using Regression
- 22.5.6 SQL Modeling Example 6: Calculating Mortgage Amortization
-
23
Advanced Analytical SQL
-
23.1
Examples of Business Intelligence Queries
- 23.1.1 Business Intelligence Query Example 1: Percent Change in Market Share of Products in a Calculated Set
- 23.1.2 Business Intelligence Query Example 2: Sales Projection that Fills in Missing Data
- 23.1.3 Business Intelligence Query Example 3: Customer Analysis by Grouping Customers into Buckets
- 23.1.4 Business Intelligence Query Example 4: Frequent Itemsets
-
23.1
Examples of Business Intelligence Queries
-
19
SQL for Analysis and Reporting
- Part V Analytic Views
-
24
Overview of Analytic Views
- 24.1 What Are Analytic Views?
- 24.2 Privileges for Analytic Views
- 24.3 Application Programming Interfaces for Analytic Views
- 24.4 Compilation States of Analytic Views
- 24.5 Validation of Data
- 24.6 Classifications for Analytic Views
- 24.7 Share Analytic Views with Application Containers
- 24.8 Alter or Drop an Analytic View Object
- 24.9 Data and Scripts for Examples
- 25 Attribute Dimension and Hierarchy Objects
- 26 Analytic View Objects
-
24
Overview of Analytic Views
- Glossary
- Index
Search
Download
PDF for offline and print