Get started with data migration
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The document provides an overview of the data migration process. It discusses the key steps which include discovering the source and target systems, mapping data fields between the systems, extracting and transforming the data, loading it into a staging system, and then loading it into the target system. It also discusses verifying the data and common tools used for data migration projects.
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Get started with data migration
- 1. DATA MIGRATION OVERVIEWGET STARTED WITH DATA MIGRATION
- 2. TABLE OF CONTENTS 1. Introduction 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
- 3. 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 Data migration is the process of transferring data between storage types, formats, or computer systems. Introduction
- 4. Process Flow Source and Target System Discovery Source and Target Field mapping Data Transformation Data Load Data Extraction
- 5. System Discovery (Positive forces for change) (Obstacles to change) System Discovery 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 Phases. 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 and migration.
- 6. 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 • Comments
- 7. Export Features of the source systems used. Data Extraction 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.
- 8. Transformations achieved with scripting Transact -SQL stored procedures,functions,SQL blocks Data type conversion Transaction Rules Implemented Data Accuracy Data Validation Data Transformation
- 9. Data Cleansing • 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. Data Transformation
- 10. In Staging, data is dumped to a location (Staging Area ) 1 Minimizes ETL activity on both Source and Target systems Used by the transformation phase 2 3 4 Staging System Can be used for next processing phase Transformation performed on rational database server separate from the source databases and target database.
- 11. • Target system loaded with data. • Cumulative data may overwrite the existing data. Data Load Load Update Schedule • 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.
- 12. Involves testing for datatype conversions. Check the Table Row Count. Monitor Database Integrity. Has an impact on the operational performance.Testing of Translational rules implementation. Tester should fulfil these tests against the subset of data. Black Box Testing- Business Cases, Data samplings use cases Use cases. Data Verification
- 13. Data Migration Tools Pentaho Data Integration Dataloader Jitterbit Dataloader Ab Initio Cognos Decisionstream Talent Open Studio SAS Open Studio Microsoft SQL Server Integration Services Informatica PowerCenter Oracle ETL
- 14. Migration Planning Datamap Document Source System Information Target System Information Efficient Scripting Migration execution Test cases development for Data Verification Data Migration Checklist
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- 16. Get in Touch with us : +16124545031 [email protected] www.zen4orce.com THANK YOU !!