CA Ppm Data Warehouse Jasper Soft
Transcript of 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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Agenda
DATA WAREHOUSE OVERVIEW AND ARCHITECTURE
LOADING THE DATA WAREHOUSE
DATA WAREHOUSE DEMO
DATA WAREHOUSE STANDARDS
DATA WAREHOUSE DIMENSIONS AND FACTS
DATA WAREHOUSE ROADMAP
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3
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DATA WAREHOUSE SETUP2
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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
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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
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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
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Data Warehouse Overview
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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Data Warehouse Overview
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.
Addresses Reporting Challenges
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Data Warehouse Overview
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.
Addresses Reporting Challenges
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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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Data Warehouse Setup
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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Data Warehouse Setup
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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Data Warehouse Setup
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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Data Warehouse Setup
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
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