CA Ppm Data Warehouse Jasper Soft

8/10/2019 CA Ppm Data Warehouse Jasper Soft

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ca Intellicenter

Hands-On Lab:

CA PPM Data WarehouseBryan Temple

Session Number ICX07L #CAWorld

CA PPM

CA Technologies


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Hands On Lab: CA PPM Data Wareho


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4 2014 CA. ALL RIGHTS RESERVED.

Agenda

DATA WAREHOUSE OVERVIEW AND ARCHITECTURE

LOADING THE DATA WAREHOUSE

DATA WAREHOUSE DEMO

DATA WAREHOUSE STANDARDS

DATA WAREHOUSE DIMENSIONS AND FACTS

DATA WAREHOUSE ROADMAP

1

3

4

5

6

7

DATA WAREHOUSE SETUP2


5/385 2014 CA. ALL RIGHTS RESERVED.

Subject Oriented

Modeled on the STAR schema and includes thefollowing master objects: Investment (AllTypes), Resource, Portfolio and Timesheet

Integrated

Consistent naming conventions,encoding structures

Non-Volatile

Separate schema optimized for businessdecision making and analytics

Time Variant

Predefined, yet configurable, tim

1 year back/forward for weekl

3 years back/forward for mont

Data Warehouse

Data Warehouse Overview




Dimensions are the descriptive fields on an object (Examples: IInvestment Name, Investment Manager, etc.).

Facts are the metrics on an object (Examples: Total Cost, Actua

Star Schema is a type of database design. A simple Star would table with a few direct links to dimension tables.

A Snowflake is a dimension table that can be indirectly linked t

Common Terms



CA PPM Application

CA PPM Reporting Architecture

Load Data

Warehouse job

(embedded Pentaho

Data Integration)Data

Warehouse

Jaspersoft Reports,

Ad Hoc Views &

Domains

CA PPM

Database

o Lightweig

business

o Out of th

domains Resource

Timeshee

o Pentaho Data Integrator is

embedded within CA PPM. The

data transformation and load runs

as a CA PPM job.

o The Data W

a STAR sch

covering t

and their a




Reports and portlets run against transactional data. The data warehouse schema resides on another database server t

off the transactional CA PPM database.

Relational database makes queries very complex. The data warehouse carries keys and descriptive values in the dim

fewer joins are required. Facts are combined into summary and pe

Finding the data with 1000+ tables With the exception of configuration and meta tables, the data warare user friendly to report against.

Table name inconsistencies Similar tables are grouped together by the table prefix and the nam

descriptive.

Addresses Reporting Challenges


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Time slice requests

Specific time slice requests are set up to populate the data warehouse. Defaults are smodified.

Column naming

Columns are consistently named across tables.

Resource ID versus user ID

In the CA PPM database, manager points to the user ID and resource points to the re

which makes it inconsistent. In the data warehouse, resource columns (manager_key

are always the resource_key.

Date/time storage

In the CA PPM database, the finish/end dates do not always match those displayed in

functions in queries must be leveraged to determine the correct date. In the data wa

finish/end dates always match CA PPM.



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Code versus ID In the CA PPM financial tables, codes are used instead of IDs. The

always uses the numeric key of the dynamic lookups.

Database tuning Since the data warehouse is separate from the CA PPM database,

can be tuned differently for optimal performance.

Studio attributes are not available in Business Objects Universecustomization. The data warehouse is extendable without customization. A flag h

to Studio objects and attributes that control whether the data warautomatically adds custom objects and attributes.



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Data Warehouse Setup

The CSA data warehouse properties allow you to configure the baswarehouse credentials and settings. This database can be on the sserver, a different instance on the same server, or on a different sedepends on the size of the CA PPM database.

CSA Configuration


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Administration/General Settings/System Options: The languages selected determine which localizations are included in the d

(more languages means more disk).

The entity chosen determines which fiscal periods are used when aggregat

The entity selected does not restrict the investment data included in the dathat entity.

System Options


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Time slices with the Data Warehouse flag checked determine the facts in the data warehouse.

Defaults Months: 3 years back and forward

Weeks: 1 year back and forward

Daily: 1 year back and forward

Fiscal: 3 years back and forward

Verify these ranges work for your company. If not, you can update thPeriods in the time slice request.

All monthly time slices should have the same From Date and Number(The same applies for Weekly, etc.).

Time Slice Requests


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Custom objects can be incthe data warehouse via StSimply check the box for Ithe Data Warehouse.

The attributes of the objec

need to be selected manuinclusion in the data wareh

Custom Objects


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Custom attributes can be iin the data warehouse via Simply check the box for Ithe Data Warehouse.

Boolean, String, Number, M

Date, Calculated, Formula,and Multi-Valued Lookup aare supported. Calendar aTSVs are supported for releattributes.

Custom Attributes


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Loading the Data Warehouse

Two jobs exist in CA PPM for loading the data warehouse. These joindependent of one another.

Load the Data Warehouse Security Privileges: Loads the security for iresources. The table is truncated and rebuilt each time.

Load the Data Warehouse: This is the core job that analyzes the metanew objects and attributes (if needed), loads the dimensions, lookups

Parameter:

Data Warehouse Full Reload If checked, this will truncateand rebuild the data warehouse. Otherwise, onlyincremental changes are processed.

ETL Jobs


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Loading the Data Warehouse

Reports and Jobs

Load the Data Warehouse Security Privileges. Loads the investment/resource security for the system users

Separate job the security job is not incremental, the table gets truncated

Load the Data Warehouse. Loads the complete data warehouse

ETL job steps:

Runs scripts the data warehouse is dependent upon: calendar population, WBS hhierarchy

Builds the meta data that determines the data warehouse structure

Checks/corrects any data warehouse structure changes

Loads/updates the lookup tables

Loads/updates the dimension tables

Loads/updates the fact tables

ETL Jobs


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Data Warehouse Table Prefix Standards

DWH_CFG - Configuration tables used to supply the data warehouse log and au

DWH_CMN - Common database objects used across most areas

DWH_CMP - Company database objects

DWH_FIN - Financial management database objects

DWH_INV - Investment management database objects

DWH_LKP - Lookup database objects

DWH_META - Meta data tables that help determine the data warehouse structur

DWH_ODF - Custom database objects

DWH_PFM - Portfolio management database objects

DWH_RES - Resource management database objects

DWH_RIM - Risk, issue and change management database objects

DWH_TME - Time management database objects

DWH_X - Internal database objects used to help populate the fact tables


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Data Warehouse Static Lookup Standards

Static Lookups in CA PPM can be confusing because they are stored in one table and you need

the lookup_type. In the data warehouse, each lookup is its own table. The lookup values are

different languages chosen for the data warehouse. If, for example, the data warehouse is sto

Spanish, two records exist for each lookup value.

Column Data Type Description

[lookup_name]_key Number or

Varchar(30)

The key value of the lookup. If the hidden key in CA PPM is lo

in the data warehouse will be populated with the lookup_enu

lookup_code.

Example: investment_status_key

language_code_key Number ID from the CA PPM languages table

language_code Varchar(30) Unique language code from the CA PPM languages table

[lookup_name] Varchar(255) Descriptive n ame of the lookup: Example: investment_status

sort_order Number Sort order is used to specify a specific order in which the user

is_active Number Is the current lookup value active

clarity_updated_date Date Last time the record was updated in CA PPM

dw_updated_date Date Last time the record was updated in the data warehouse


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Data Warehouse Dynamic Lookup Standards

Dynamic Lookups in CA PPM are determined by NSQL statements. In the data warehouse, a ta

dynamic lookup that is used. Each table structure can be different depending on the lookup. I

language dependent, then language_code_key and language_code will be stored. Otherwise,

record per value.

Column Data Type Description

[lookup_name]_key The key value of the dynamic lookup. D epends on the NSQLs

language_code_key Number ID from the CA PPM languages table if applicable

language_code Varchar(30) Unique language code from the CA PPM languages table if app

[lookup_name] Descriptive name of the lookup: Example: investment_status

Miscellaneous columns specific to the lookup

clarity_updated_date Date Last time the record was updated in CA PPM

dw_updated_date Date Last time the record was updated in the data warehouse

Basic Dynamic Lookup Structure


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Data Warehouse Dimension Standards

Dimension Language Tables If the dimension has language dependent lookups, a table ending with _ln carries th

dependent descriptions.

Below is a simple example using Investment_status. The key is carrieinvestment table while the language dependent description is carried investment language table.

h d d


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Data Warehouse Fact Standards

Fact table names end with _facts.

Fact tables with _period_ in the name store facts by defined periods.

Fact tables with _summary_ in the name store summarized facts.

The fact table keys all have referential integrity.

Calculated facts are stored in the tables to help with consistency.

Summary rollups exist in the data warehouse.

Assignments roll up to tasks, tasks roll up to investments.

Data warehouse time slice requests aggregate the data into weekly, mfiscal periods.

Fiscal aggregation is new to the data warehouse.

F P i d A i T bl


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Fact Period Aggregation Tables

Fact Description Fact Table Aggregation

Financial Transaction Facts dwh_fin_transaction_facts Daily

Time Entry Facts dwh_tme_entry_facts Daily

Financial Benefit Facts dwh_fin_benefit_period_facts Fiscal Period

Financial Plan Facts dwh_fin_plan_period_facts Fiscal Period

Task Assignment Facts dwh_inv_assign_period_facts Fiscal Period, Weekly, Monthly

Investment Task Facts dwh_inv_task_period_facts Fiscal Period, Weekly, Monthly

Investment Team Facts dwh_inv_team_period_facts Fiscal Period, Weekly, Monthly

Investment facts dwh_inv_period_facts Fiscal Period, Weekly, Monthly

Resource Facts dwh_res_period_facts Fiscal Period, Weekly, Monthly

F t S T bl d I t l F t T bl


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Fact Summary Tables and Internal Fact Tables

Summary tables exist for many of the facts. If matching summary numbers to period facts, qualify the period facts by

Internal Fact Tables start with a dwh_x_. These tables are used to poperiod and summary fact tables in the most efficient way. They are no

consumption.

Fact Description Fact Table

Financial benefit facts dwh_fin_benefit_summary_facts

Financial plan facts dwh_fin_plan_summary_facts

Task assignment facts dwh_inv_assign_summary_facts

Investment task facts dwh_inv_task_summary_facts

Investment team facts dwh_inv_team_summary_facts

Investment facts dwh_inv_summary_facts

E l I t t P i d F t T bl


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Example: Investment Period Facts Table

The Investment period facts table contains over 110 different facts. Investment_key is a foreign key to the investment table.

Period_key is a foreign key to the periodic table.

Dw_updated_date is the last date this record was updated.

E l I t t T T bl


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Example: Investment Team Table

DWH_INV_TEAM

DWH_INV_TEAM_LN

Table contains language translations.

If the data warehouse is set up for English and Spani

would be two records for every one record in dwh_i

E ample Old Team Q er (CA PPM Database)


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Example: Old Team Query (CA PPM Database)

SELECT m.full_name investment_manager,

i.name investment_name,

r.full_name resource_name,

rr.full_name role_name,

tl.name booking_status,

t2.name request_status,

s1.slice_date period_start_date,NVL(s1.slice,0) alloc_hours,

NVL(s2.slice,0) alloc_cost

FROM inv_investments i

INNER JOIN prTeam t ON i.id = t.prProjectID

LEFT OUTER JOIN srm_resources m ON i.manager_id = m.user_id

LEFT OUTER JOIN srm_resources r ON t.prResourceID = r.id

LEFT OUTER JOIN srm_resources rr ON t.prRoleID = rr.id

LEFT OUTER JOIN cmn_lookups_v tl ON t.prBooking = tl.lookup_enum

AND tl.lookup_type = 'BOOKING_STATUS_LIST'

AND tl.language_code = 'en'

LEFT OUTER JOIN cmn_lookups_v t2 ON t.prBooking = t2.lookup_enum

AND t2.lookup_type = 'REQUEST_STATUS_LIST'

AND t2.language_code = 'en'LEFT OUTER JOIN prj_blb_slices s1 ON t.prID = s1.prj_object_id

AND s1.slice_request_id IN (SELECT id

FROM prj_blb_slicerequests

WHERE request_name = 'MONTHLYRESOURCEALLOC

LEFT OUTER JOIN prj_blb_slices s2 ON t.prID = s1.prj_object_id

AND s1.slice_request_id IN (SELECT id

FROM prj_blb_slicerequests

WHERE request_name = 'team::alloccost_cur

AND s1.slice_date = s2.slice_date

WHERE s1.slice_date BETWEEN TO_DATE('01/01/2014','MM/DD/YYYY') AND TO_DATE('12/31/2014','MM/DD/YYYY')

Need to know look

Inconsistent joins b

(resource_id or us

Inconsistent colum

Multiple joins to th

different informati

Not intuitive

Example: New Team Query (Data Warehouse)


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Example: New Team Query (Data Warehouse)

No need to join to

Consistent joins be

(always resource_id Consistent column

Intuitive

SELECT i.investment_manager,i.investment_name,t.resource_name,t.role_name,

tl.booking_status,tl.request_status,p.period_start_date,tf.alloc_hours,tf.alloc_cost

FROM dwh_inv_team tINNER JOIN dwh_inv_team_ln tl ON t.team_key = tl.team_keyINNER JOIN dwh_inv_investment i ON t.investment_key = i.investment_keyINNER JOIN dwh_inv_team_period_facts tf ON t.team_key = tf.team_keyINNER JOIN dwh_cmn_period p ON tf.period_key = p.period_key

WHERE SYSDATE BETWEEN p.year_start_date AND p.year_end_dateAND p.period_type_key = 'MONTHLY'AND tl.language_code = 'en'

Financial Plan Facts


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Financial Plan Facts

Combines the periodic

Calculates forecast fac Numerous slices used

these facts

Summarizes the period

Calculates forecast fac

Investment Team Facts


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Investment Team Facts

Combines the team fac

by period

Calculates costs

Summarizes the period

Task Assignment Facts


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Task Assignment Facts

Combines the assignm

period Calculates costs

Summarizes the periodassignment facts

Investment Task Facts


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Investment Task Facts

Summarizes assignmen

task by period Formulas calculated fo

Summarizes task facts Contains earned value

Investment Period Facts


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Investment Period Facts

Summarizes investmen

Formulas calculated fo

Comprehensive investm


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Data Warehouse Items Included


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Data Warehouse Items Included

Change Request Management Issue Management WBS Structure

Exchange Rates OBS Hierarchy WIP Financial Transactions

Financial Benefit Plans Portfolio (High Level) Facts by Weekly/Monthly/Fisc

Financial Budget/Cost Plans Resource Assignments Summary Facts

Investment Applications Resources All Associated Lookups

Investment Assets Resource User Security Custom Attributes

Investment Ideas Risk Management TSV Values

Investment Other Work Team Allocations Summary Investment Earned V

Investment Products Time Entry Current Baseline Data

Investment - Projects Time Entry Notes PMO Accelerator

Investment Services Time Sheets DBLINK for Missing Data

Investment User Security Time Sheet Notes New Cost Slices ETC, Allocati

Data Warehouse Items Under Consideration


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Data Warehouse Items Under Consideration

Additional Objects

Baseline History

Earned Value History

Incidents

Portfolio Management

Resource Skills

Scenarios

Snapshots for Trending

Slowly Changing Dimensions

For More Information


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