1. Enhance BI Applications and Simplify Development
Oracle Analytic Views
Enhance BI Applications and Simplify Development
William (Bud) Endress Director, Product Management Oracle Database Server Technology October 25, 2018

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3. Oracle Analytic Views
What are they?
A new (as of 12.2) type of view in the Oracle Database
Like other views, they access data from other row sources in the database (tables, views, external tables, etc.)
Analytic views enhance data sets with
A dimensional business model
A dimensional and hierarchical expressions
A query transformation engine

4. Oracle Analytic Views
What can you do with them?
Moves business logic (aggregations, hierarchies, calculations) back into database
One definition, accessed by many applications
Simple SQL for complex analytic queries
no joins or GROUP-BY clauses necessary
Optimize query performance
Smart query transformation engine
Simple aggregate management

5. Analytic View Layers and Features
SQL views with standardized set of columns
OLE DB for OLAP provider (MDX, Excel)
Access Layer
Rich dimensional business model: hierarchies, levels, attributes, measures
Accessible vis the Oracle Data Dictionary
Business Model
Generates highly optimized SQL to execute AV queries
Deep knowledge of data sources and SQL optimizations
SQL Execution

6. Oracle Analytic Views Organized, Enhanced & Optimized Simple SQL & MDX
Easier Access To Your Data
CSV
SQL
MDX / OLE DB for OLAP
Analytic View
Organized,
Enhanced & Optimized
Simple SQL & MDX
Your
Applications
Your Data

7. Analytic Views For the data warehouse architect and developer
Something for Everyone
For the data warehouse architect and developer
Easily extend star schema with aggregate data and calculations
Performance optimization
For the application developer
Simplifies metadata management and SQL generation
Easily build applications in Oracle Application Express
For the business user
Built-in, browser-based data visualization via APEX application
Excel Pivot Table interface

8. Analytic Views How would you build this application?
Analysis of health insurance coverage rates in the United States
Coverage rates by time, counties and states
Geographic comparisons
Measure improvement over time
Interactive data visualization tools for end users
Health Insurance Coverage Rates by State, 2014

9. Analytic Views This application can be built with 5 SQL statements
Create 2 hierarchies (4 SQL statements)
Create 1 analytic view (1 SQL statement)
Is instantly accessible via APEX based application
Is all in the Database
Simple SQL
Analytic View
Data
Tables, Views, etc.

10. Analytic Views Simple SQL
SELECT time_hier.member_name AS TIME,
geog_hier.member_name AS GEOGRAPHY,
pct_insured
FROM insured_av HIERARCHIES(time_hier,geog_hier)
WHERE time_hier.level_name = 'YEAR'
AND geog_hier.level_name = 'STATE')
ORDER BY time_hier.hier_order ,
geog_hier.hier_order;
Fact data is selected from analytic views using SQL
Analytic views are views on top of a star schema.
No storage structures

11. No JOIN or GROUP BY clauses in analytic view queries
Analytic Views
Simple SQL
SELECT time_hier.member_name AS TIME,
geog_hier.member_name AS GEOGRAPHY,
pct_insured
FROM insured_av HIERARCHIES(time_hier,geog_hier)
WHERE time_hier.level_name = 'YEAR'
AND geog_hier.level_name = 'STATE'
ORDER BY time_hier.hier_order ,
geog_hier.hier_order;
The HIERARCHIES clause specifies the dimensions and hierarchies for this query
No JOIN or GROUP BY clauses in analytic view queries

12. Analytic Views Simple SQL
SELECT time_hier.member_name AS TIME,
geog_hier.member_name AS GEOGRAPHY,
pct_insured
FROM insured_av HIERARCHIES(time_hier,geog_hier)
WHERE time_hier.level_name = 'YEAR'
AND geog_hier.level_name = 'STATE'
ORDER BY time_hier.hier_order ,
geog_hier.hier_order;
Standardized columns such as ‘member_name’ are selected from the hierarchies
Standardized columns such as ‘member_name’ are selected from the hierarchies

13. Levels of aggregation are specified in the WHERE clause
Analytic Views
Simple SQL
SELECT time_hier.member_name AS TIME,
geog_hier.member_name AS GEOGRAPHY,
pct_insured
FROM insured_av HIERARCHIES(time_hier,geog_hier)
WHERE time_hier.level_name = 'YEAR'
AND geog_hier.level_name = 'STATE'
ORDER BY time_hier.hier_order ,
geog_hier.hier_order;
Levels of aggregation are specified in the WHERE clause
When filtering on the level ‘State’ for the time hierarchy, the member named will include California, New York, etc

14. The calculations automatically use new hierarchy levels.
Analytic Views
Simple SQL
SELECT time_hier.member_name AS TIME,
geog_hier.member_name AS GEOGRAPHY,
pct_insured
FROM insured_av HIERARCHIES(time_hier,geog_hier)
WHERE time_hier.level_name = 'YEAR'
AND geog_hier.level_name = 'COUNTY'
ORDER BY time_hier.hier_order ,
geog_hier.hier_order;
To drill, just update the WHERE clause. Everything else remains the same.
The calculations automatically use new hierarchy levels.

15. To select a calculation, just select columns
Analytic Views
Simple SQL
SELECT time_hier.member_name AS TIME,
geog_hier.member_name AS GEOGRAPHY,
pct_insured_diff_us_avg
FROM insured_av HIERARCHIES(time_hier,geog_hier)
WHERE time_hier.level_name = 'YEAR'
AND geog_hier.level_name = 'COUNTY'
ORDER BY time_hier.hier_order ,
geog_hier.hier_order;
To select a calculation, just select columns
Calculations are express in the analytic view so they can just be selected in the query

16. Analytic Views Easily create new measures Embedded Calculations
Simplified syntax based on business model
Includes dimensional and hierarchical functions
We've already seen that calculation measures are easy to query. They are also very easy to define using syntax that references objects within the business model (rather than referencing tables and columns).
Look at Sales Year to Date and note the references to the Time Hierarchy and Year level. This measure will return Sales Year to Date for any level within the Time Hierarchy, across all other dimensions. Since the database understands this to be a time series calculation, it will automatically densify data along the time hierarchy (e.g., partition outer join) and automatically expand filters to access prior or future time periods (security rules permitting, of course).
The Product Share of Parent will return Sales for any product, at any level of the Product Hierarchy, as a ratio Sales of the Parent product (across all other dimensions).
Add Percent Uninsured Difference from US Average with a single line of code
SHARE_OF(pct_uninsured HIERARCHY
geog_hier
MEMBER country ['USA']) – 1)

17. Add time series calculations with a single line of code
Analytic Views
Embedded Calculations
Add time series calculations with a single line of code
We've already seen that calculation measures are easy to query. They are also very easy to define using syntax that references objects within the business model (rather than referencing tables and columns).
Look at Sales Year to Date and note the references to the Time Hierarchy and Year level. This measure will return Sales Year to Date for any level within the Time Hierarchy, across all other dimensions. Since the database understands this to be a time series calculation, it will automatically densify data along the time hierarchy (e.g., partition outer join) and automatically expand filters to access prior or future time periods (security rules permitting, of course).
The Product Share of Parent will return Sales for any product, at any level of the Product Hierarchy, as a ratio Sales of the Parent product (across all other dimensions).
LAG_DIFF_PERCENT(pct_insured)
OVER (HIERARCHY time_hier
OFFSET 1 ACROSS ANCESTOR AT LEVEL year)

18. Analytic Views Descriptive metadata available in data dictionary
Descriptive names and descriptions
Translatable
Measure formatting
User/application extensible

19. “Standard” and Analytic Views
“Standard” View
Analytic View
Data Sources (FROM)
Yes
Joins
Business Model-Based Calculations No
Automatic Hierarchical Columns
Automatic Multi-Level Aggregation
Automatic Filter Expansion
Automatic Outer Join
Automatic Order of Calculation
Presentation Metadata
Another way of thinking about analytic views is to compare them with “standard” relational views. While you can certainly create a standard view that returns just about any data (aggregation, calculations and so on), let’s look at what is easy and what comes “for free” with each.
Both can select from tables, other views, external tables and so on.
Both can join dimension and fact tables.
Only analytic views
Define calculations using business-model based syntax that works at any level of aggregation and across any dimension.
Include system-generated hierarchical columns that simplify SQL generation.
Automatically return multiple levels of aggregation without multiple passes, UNIONS or other complicated SQL.
Automatically expand filters to access data for prior or future periods, parents, ancestors, children and descendants.
Automatically densify time hierarchies for time series calculations.
Automatically order calculations. For example aggregate then calculate measure, or calcuate measures then aggregate.

20. Learn More at LiveSQL.Oracle.com