effective capture of metadata using ca e rwin data modeler 09232010

Effective Capture of Metadata Using CA ERwin Data ModelerMetadata –Data gets meaning.

Abstract

• In any data centric environment either Data warehouse or an OLTP data environment ,vital information anybody looks for is the purpose and content of the tables and columns. Its metadata of the data, provides more insight about the structure. In many organization, the lack of metadata has lead to redundant definition of table and columns , ignorance of real capability of your data centric system, inability to define standards and build the knowledge layer for the business. In the case of data warehouse its vital to capture the source and transformation rules along with dimensional model as it will help fixing incorrect mappings early in the life cycle, effective communication to ETL team and store the ETL rules close to the data model. Without metadata ,it will lead to individual's interpretation of data just like blind folded touching the elephant. In this webinar we will discuss about the various flexible features provided by CA ERwin Data Modeler for data warehouse and relational model.

Speaker Bio

• Sampath Kumar brings 11 years of experience in implementing small, medium and large scale data centric environments (both relational and data warehouse ) using CA ERwin Modeling suite of products. He is currently working for Infosys Technologies Limited as Technology Architect in their DW/BI practice group. Prior to that he was working with American Express Credit Cards as Sr System Analyst for their Worldwide Risk Information Management group. In all his experience he has worked extensively in various database products ,BI tools ,data modeling and related products offered by CA such as CA ERwin Data Modeler, CA ERwin Model Validator,CA ERwin Model Manager and CA ERwin Data Profiler.

Agenda

• Not going to focus on the known fundamentals and data jargons

• Effective capture of metadata in Data warehouse environment

– Case study using Customer_Dim

• Other Flexible Options to capture metadata into data model

– Using an example.

Effective capture of metadata in Data warehouse environmentCase study using Customer_Dim

Problem Statement

In any data warehouse development project, some of the major challenges include

• Effective capture of metadata information in data model such as data source ,transformation, enrichment and data synchronization rules etc.

• Keeping data model in synch with changing ETL rules and vice versa i.e. keeping ETL rules close to DW Data model (blueprint of your DW data)

• Early identification of incorrect ETL mappings in the complete lifecycle.

Problem Statement contd…

• Effective communication of captured metadata information by data modeler to other teams such as ETL

Background

3 Important pieces of information:

• Source of data

• Transformation rules-The method in which the data is getting extracted, transformed and loaded

• Frequency: The frequency and timing of data warehouse updates.

CA ERwin Features

CA ERwin Data Modeler supports the following salient features to capture the metadata information effectively.

• Data warehouse Sources Dialog

• Columns Editor

• Data Movement Rules Editor

Customer_Dim

Snapshot

– customer_SKID

– snapshot_Begin_Date

– snapshot_End_Date

– current_ind

• Basic Information

– Customer name

– Customer Date of Birth

– Driving License

• Address

– Mailing Address

– Physical Address

Communication

Email Address

Phone

Fax

Segmentation

Shopping

Behavior

Capturing Data Source

Creating Customer_Dim

Make it Dimensional Model

Make it Dimensional Model…

Data Source Enabled…

Data warehouse Source Selector

Data warehouse Source

Data warehouse Sources

The “Import other” provides three options to import the table structure

• Flat File

• Database/Script

• Model Manager

Importing table from CA ERwin® Model Manager

Customer

customer_id

customer_first_name

customer_last_name

dob

driving_license_nbr

driving_license_state

Customer_Address

customer_id (FK)

mailing_address_line1

mailing_address_line2

mailing_city

mailing_state

mailing_county

mailing_country

physical_address_line1

physical_address_line2

physical_county

physical_city

physical_state

physical_country

Customer_Segmentation

customer_id (FK)

behavioral_segment_nbr (FK)

shopping_segment_nbr (FK)

Behavioral_Segment

behavioral_segment_nbr

behavioral_segment_name

Shopping_Segment

shopping_segment_nbr

shopping_segment_name

Opens Model Mart Library

Import source tables

Source Tables Populated

Data warehouse Source Selector.Multiple

sources can be added

Transformation and business rules can be added here.

Source as Flat File

ETL Mapping Template-using Data Browser

Report Template Builder

Customize the Report

Generate the Metadata Report

ETL Mapping Sheet

Data Movement Rules

Documenting the rule

Attaching table to rule.

Export as Report

Other Flexible Options to capture metadata into data modelUsing simple example

Import from MS Excel

Using simple example

Metadata Capture from MS Excel

• When it would be useful

– Import the definitions available already into data model

– Import the definitions from business stakeholders for key columns to avoid wrong interpretation.

• Step 1: Store the model locally in the hard disk

• Step 2: Use the excel sheet “Import Definitions” or VBA macro provided by CA .

• Step 3 :Import the metadata into the model by running VBA code.

Person Table

Import Definitions

In this format

Final Step

Open the first sheet and click on “Update Entity Defns” which will update the definitions written for that particular table into the data model. Similarly click on the “Update Attribute Defns” which will update the attribute definitions.

Note:

• Keep the data model closed otherwise you will get error that it’s open.

• Make sure table and column names are exactly same as in the data model.

• It’s not only for business people but also for the data modelers who can enter the definitions in MS Excel and get the approval from the business or data management team, then it can uploaded separately using this utility.

Capture metadata in Data Browser.

Metadata Capture using Data Browser

Conclusion

• The metadata information such as “Data Source”, “Transformations rules” and “Data Movement rules” are very important for any Data warehousing efforts and it’s very critical to capture the correct information.

• Metadata from data management standpoint , reduces considerable amount of time while consolidating the attributes or entities or databases during acquisition or merger.

• Knowing the importance of metadata for the data model ,CA ERwin has provided these flexible options which can be leveraged to make the data model & data more meaningful.

Questions?

In case of any additional questions you can reach me in

[email protected]

[email protected]