TABLE OF CONTENTS
2. Data Migration- Process Flow
• System Discovery-Source, Target
• Field Mappings –Source, Target
• Data Extraction
• Data Transformation
• Staging System
• Data Load
• Data Verification
3. Data Migration Tools
4. Data Migration Checklist
What is Data Migration
Typically part of a large
program and is often
triggered by a merger or
Acquisition, a business
decision to standardise
systems or modernization
of an organisation’s system .
Why it’s required
Data migration is the
transferring data between
storage types, formats, or
Source and Target System Discovery
Source and Target
(Positive forces for change) (Obstacles to change)
Migration Hardware and software
environment details collected.
Number of Source Systems involved.
Size of the legacy system data.
More critical data elements with
respect to target application system.
Whether aim is to convert all the
legacy application data at once or in
Data Cleansing involvement in the
migration of data from legacy system
to target system with/without data.
Redundant data during migration.
Level of Data cleansing required to
maximize the benefit of conversion
Field Mappings- Source, Target
Mapping performed with the information derived from source, target and business rules.
Document maintained containing the following :
• Change Description (Indicated mapping changes)
• Key Indicator(Indicates whether the field is primary key or not
• Source Field Name
• Source Table/File Name
• Source Field Data Type
• Source Field Length
• Source Field Description
• Business Rule
• Target Table Name
• Target Field Name
• Target Data Type
• Target Field Length
Export Features of the source
Identified data extracted in
System Discovery phase.
Scripts generated to export the data.
• Systems provide the feasibility to export data
into CSV formats
Salesforce,Siebel CRM, Microsoft Dynamics.
Microsoft Access provides .accdb formats.
SQL provides the .ldf , .mdf and .bak files.
Transact -SQL stored
Data type conversion
• Detects incomplete parts of data.
• Modifies/Eliminates inaccurate records.
• Detects Anomalies
Data Sampling- (*) of the rows for a department column needs
to be counted
• Column Property Enforcement
Null values in required columns.
Numeric values that fall outside of expected high and lows.
Columns whose lengths are exceptionally short/long.
Business Rule Application.
In Staging, data is dumped to a location (Staging Area )
Minimizes ETL activity on both Source and
Used by the transformation phase
Can be used for next processing phase
Transformation performed on rational database
server separate from the source databases
and target database.
• Target system loaded with data.
• Cumulative data may overwrite the existing data.
• The extracted data is updated.
• Bulk load facility is available in most DBMS.
• Schedule the load/import process.
• The speed of loading can be influenced by many factors
such as table size, proportion of updates and inserts.
Has an impact
the subset of
use cases Use
Data Migration Tools
Pentaho Data Integration Dataloader
Jitterbit Dataloader Ab Initio
Cognos Decisionstream Talent Open Studio
SAS Open Studio Microsoft SQL Server
Informatica PowerCenter Oracle ETL
Source System Information
Target System Information
Test cases development for Data
Data Migration Checklist
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