What Is Data Migration?
Table of Contents
- By Greg Brown
- Aug 14, 2023
The rapid growth of big data has given rise to a new set of challenges when moving massive amounts of data from one computer system to another. Despite technological advancements, migrating data remains complex, requiring data mapping, validation, and cleansing, and the end result of data integrity.
The early days of moving data involved magnetic tapes or punch cards. The mainframe era brought new challenges like different file formats and data structures. The 1980s and 90s ushered in the relational database and client-server technology, which meant transferring data between the new database management systems. (DBMS) The rise of Enterprise Resource Planning (ERP) increased the complexity of consolidating data from legacy systems to new formats.
What is Data Migration?
Migrating data is the process of moving existing and historical data from one platform, format, or storage to another compatible environment. The process involves careful planning, preparation, implementation, and post-migration validation. The disparity of global computer systems has made extracting data and delivering a validated and workable dataset a multifaceted and intricate process.
Data migration took on a new dimension with the introduction of cloud computing. Software-As-A-Service (SaaS) proliferated by transferring on-premise data to the cloud for further manipulation.
Why is Data Migration Important?
- It is crucial for any organization planning a data migration to have clear goals and structures in place before the process begins. Assessing risk and determining the scope of a data move helps the organization establish the success criteria for the project. The organization needs to understand the infrastructure and format of the source system and the target. The early stages of the migration must include documentation of data mapping, extraction rules, transformation logic, and any data cleansing or enrichment processes.
- Before the extraction process begins, organization experts must assess data quality involving each dataset's accuracy, completeness, consistency, and relevancy. The goal is to ensure the migrated data is reliable and usable.
- Extracting the data from its current source involves removing databases, files, APIs, and any other resources associated with the data. Administrators must ensure security and data access controls are in place. After data extraction, it must often be transformed to match the structure of the target system. Transforming the data must ensure the migration is meaningful to the target system. Once all the migration's prerequisites and configurations have been met, the data must be loaded into the target system while protecting the relationships and data integrity.
- It is crucial to perform thorough testing and validation to verify the integrity of the migrated data. Constructing data reconciliation ensures the migration has met the expected results. Organizations can switch to the new environment once the data has been migrated and verifications have been met.
Data Migration Strategies
Depending on the company or individual, a host of data migration strategies are available. According to the source, the complexity and amount of data to be processed can have several steps or just a few for completion. Some data migrations can happen in a few hours, days, or even months to complete. Steps can be missed or ignored, causing unexpected delays from the source or the target.
Big Bang Migration
This strategy type involves transferring the entire dataset within a limited time window. During a big bang migration, live systems may be compromised while the data undergoes the extraction, transformation, load, and transition process. The advantages of this method are completing the entire migration in a limited time. However, there is intense pressure on the business's engineers and administrators as it continues to operate without one of its primary resources. There is a risk of compromised implementation if the planning and execution steps are not performed correctly. Preparation mitigates the risk before the migration is executed.
This type of data transfer involves moving the data incrementally in smaller chunks over a more extended period. Organizations choose subsets of data to move depending on their daily complexity and importance to the company. Trickle migration minimizes the immediate impact on live systems while reducing downtime risk. The approach offers more flexibility and is helpful if a phased approach is preferred. However, trickle data migration takes longer to complete than a big bang method. Data selection depends on factors such as business needs, project requirements, resource availability, downtime, and data volume.
Zero Downtime Migration
Zero downtime data migration allows a fallback position after the database migration is complete. After the switchover from the old database, the target database that is running Exadata Cloud Service, Exadata Cloud at Customer, or Oracle Cloud Infrastructure will become the primary database, and any on-premise dataset becomes the standby. This Oracle-based migration strategy is designed so that a website or application is never down or unstable during deployment. To achieve this migration, web servers do not serve the migrated code until the full deployment is complete.
Data Migration Best Practices
Data migration of any size takes careful planning and preparation to ensure a successful transfer. Here are a few best practices for a successful transfer.
- Data migration assessment is necessary before starting the process of data movement. The assessment should include identifying data sources, understanding data quality and integrity, and any known dependencies and relationships.
- Detailed migration plans should outline steps and a timeline for completion. The plan should include the various steps for execution, such as extraction, cleansing, and validation. Roles and responsibilities need clear definitions for everyone involved.
- Ensure every step and piece of data has a backup plan in place. This is crucial to mitigate data corruption during the move. Validation of the integrity and accuracy of the data must be documented.
- Test each step of the migration before execution on live systems. Use staging environments to validate results and identify any issues.
- Before finalizing the migration, involve end-users in testing and validation. Ensure migrated data meets all the requirements set out in the initial planning stages. Address any feedback or issues raised.
- Once the migration is complete, monitor and validate the migrated data. Ensure all data and processes are functioning correctly.