Data Migration
Our Data Migration Services
Data Assessment and Planning
Comprehensive Analysis
Before any data is moved, a thorough assessment is conducted to understand the volume, nature, and sensitivity of the data.
Customized Migration Strategy
Planning involves defining the migration strategy, timelines, and identifying potential risks and mitigations.
Data Extraction and Transformation
Data Cleansing
Before migration, data often needs to be cleaned to remove duplicates, correct errors, and ensure consistency. This step is crucial for maintaining data quality in the new system.
Data Mapping
This involves mapping data fields from the source to the destination system, ensuring that data lands in the correct place and remains usable post-migration.
Data Migration Execution
The Migration Process
This is the actual process of moving data from the source to the destination. It may involve transforming data into a new format, transferring it, and then loading it into the new system.
Testing and Validation
After migration, thorough testing is conducted to ensure data integrity, completeness, and that all system functionalities are working as expected.
Post-Migration Support
Validation and Reconciliation
This step involves validating the migrated data against the original source to ensure accuracy and completeness. Data reconciliation techniques are used to identify and correct any discrepancies.
Go-live Support and Post-migration Assistance
Once testing and validation are complete, the new system goes live. Support is provided to address any issues arising from the migration and to assist users in adapting to the new system.
Data Migration Benefits
Improved Data Quality and Consistency
Migration projects often involve cleaning up the data, which can significantly improve its quality.
Cost-Efficiency
Reduce capital expenditures and optimize operational costs by leveraging cloud services and paying only for what you use.
Flexibility And Agility
Quickly adapt to changing business needs, experiment with new solutions, and accelerate time-to-market for products and services.
Improved Security And Compliance
Leverage advanced security features and compliance frameworks offered by cloud providers to enhance data protection and regulatory compliance.
How it Works
01.Planning Phase
-
Assessment:Evaluate existing data infrastructure, including volume, format, and quality.
-
Requirement Gathering: Identify stakeholders' needs and expectations for the new data environment.
- Risk Analysis:Identify potential risks such as data loss, downtime, or compatibility issues.
03. Migration Strategy Development
- Selection of Tools and Technologies:Choose appropriate tools for extraction, transformation, and loading (ETL) based on requirements.
-
Data Mapping:Define mapping rules to transform data from source to target systems.
- Incremental vs. Full Migration:Determine whether to migrate all data at once or incrementally based on priority or dependencies.
05.Testing and Validation
-
Data Integrity Checks:Perform checks to ensure data integrity and consistency post-migration.
-
Functionality Testing:Test the functionality of the new data environment against predefined criteria.
- User Acceptance Testing (UAT):Involve stakeholders to validate whether the migrated data meets their expectations.
02.Data Profiling and Cleansing
-
Profiling: Analyze data sources to understand their structure, relationships, and quality.
-
Data Cleansing:Identify and rectify errors, inconsistencies, and duplicates in the data.
-
Normalization: Ensure data conforms to defined standards and formats for consistency.
04. Execution Phase
-
Data Extraction:Extract data from the source systems while ensuring data integrity and security.
-
Transformation:Convert data into the required format, applying mapping rules and transformations.
- Loading:Load transformed data into the target system, verifying completeness and accuracy.
01. Strategy
- Clarification of the stakeholders’ vision and objectives
- Reviewing the environment and existing systems
- Measuring current capability and scalability
- Creating a risk management framework.
02. Discovery phase
- Defining client’s business needs
- Analysis of existing reports and ML models
- Review and documentation of existing data sources, and existing data connectors
- Estimation of the budget for the project and team composition.
- Data quality analysis
- Detailed analysis of metrics
- Logical design of data warehouse
- Logical design of ETL architecture
- Proposing several solutions with different tech stacks
- Building a prototype.
03. Development
- Physical design of databases and schemas
- Integration of data sources
- Development of ETL routines
- Data profiling
- Loading historical data into data warehouse
- Implementing data quality checks
- Data automation tuning
- Achieving DWH stability.
04. Ongoing support
- Fixing issues within the SLA
- Lowering storage and processing costs
- Small enhancement
- Supervision of systems
- Ongoing cost optimization
- Product support and fault elimination.