Data Migration Flashcards
What is data migration?
The process of transferring data between computer storage types or file formats.
What are the types of data migration?
Storage migration
Database migration
Application migration
Business process migration
What are the stages of data migration?
Analysis & Discovery
Sampling & Profiling
Data Cleansing
Business Rules & Process Validation
Data Load
Reconciliation & Business Sign-off
What are the key steps in pre-migration?
Source Data Exploration: Document systems, map data, and manage gaps.
Data Assessment: Profile data, clean records, and identify the “Golden Record.”
Design & Build: Create a low-level design, build ETL jobs, and validate.
What are the main steps during migration?
Preparation: Agree on a cutover plan, code freeze, and resourcing.
Execution: Run migration jobs.
What are the key post-migration activities?
Reconcile data and manage errors.
Address remaining records.
Plan for legacy system retirement.
What are some common issues in data migration?
Legacy data not fitting into new systems.
Moving target due to continuously updated source systems.
Lack of collaboration.
Insufficient knowledge of source systems.
What are the resolutions to common issues?
Modify architecture for legacy data or accept fallout.
Agree on a cut-off date and code freeze.
Use collaborative tools like stand-ups and cross-training.
Involve businesses in design and cleansing.
What is an Agile lifecycle for data migration?
An iterative approach using sprint sessions to deliver value slices collaboratively.
What is a Waterfall lifecycle for data migration?
A sequential approach where progress flows linearly through initiation, design, execution, and maintenance.
What are the advantages of cloud data migration?
Cost savings on servers and storage.
Increased collaboration, scalability, and reliability.
Enhanced disaster recovery options.
What are the disadvantages of cloud data migration?
Platform dependency.
Limited storage options by vendor.
Costs increase with large data transfers.
Security concerns.
What are key parameters of good quality data?
Completeness, conformity, consistency, accuracy, duplication, and integrity.
What features are essential in a Data Quality (DQ) tool?
Parsing & standardization
Cleansing & matching
Profiling & monitoring
Enrichment
What are key parameters for choosing an ETL tool?
Support for multiple data sources
GUI-based environment
Team development capabilities
Built-in data profiling
Metadata management
Scheduling and error handling
What are key parameters for selecting a BI tool?
Ease of setup and usability
Intuitive UI
Powerful analytics and real-time insights
Options for charts, graphs, and customizable dashboards
Mobile BI support
What was the objective in the UK’s leading gas and electricity company migration?
To migrate asset and user management data for 40k engineers with zero downtime.
What was the approach in the grocery company migration?
Business involvement in identifying disparate sources.
Profiling and cleansing to de-duplicate data.
Oracle Golden Gate for CDC and Microstrategy for reporting
