In the age of digitization, data is commonly referred to as the “digital gold”. The majority of processes undertaken by organizations in all industries across the world rely on effective data management. The recording, storing, tracking, and analysis of digital databases allows organizations to cater to their customers and carry out a range of business processes.
Over time, more and more organizations are switching from traditional data warehouses to cloud-based platforms. These platforms users with high flexibility, accessibility, and scalability to manage their databases. They also provide businesses with centralized platforms to manage all data-related activities.
One of the most important processes associated with seamless data management in the age of digitization is that of data migration.
Data Migration – An Introduction
In simple words, data migration is the process of transferring records from one storage system to another. It also involves the movement of data between different applications and data formats. CRM platforms like Salesforce allow users to move their data between different platforms through data migration practices. Salesforce data migration helps businesses import data from various sources to their Salesforce org and export records from Salesforce to external destinations. CRM users can also use data migration practices to integrate two platforms and obtain a centralized user interface for performing specific business practices.
Typically, the data migration process involves data preparation, data extraction, and data transformation. These processes are generally undertaken while introducing a new system or application within an organization.
Here are some of the most common circumstances that would ideally call for seamless data migration:
- Consolidating a website
- Replacing, upgrading, and expanding storage systems
- Upgrading or replacing legacy software platforms
- Moving from a local storage system to cloud-based platforms
- Installing new applications that coexist and leverage the existing applications sharing a single database
- Maintaining the IT infrastructure of your organization
- Switching from a fragmented approach to a centralized database
- Consolidating your information systems
- Relocating data centers
Types Of Data Migration Processes
Based on your specific requirements, you can choose one of the following major data migration processes to manage your records:
This is the type of data migration process where an organization migrates its records from one storage location to another. This is a fairly simple and straightforward process of moving data from one physical medium to another.
Ideally, organizations undertake storage migration to upgrade the storage equipment to more modern equipment. Essentially, this encompasses the movement from paper to digitized platforms, tapes to HDD, HDD to solid-state drives, and traditional hardware-based storage platforms to cloud-based platforms.
As opposed to other types of migration processes, storage migration is not driven by the need to expand the storage space but the need to upgrade the storage technology. Typically, it does not change the content or format of the datasets involved. Users can undertake processes like data validation, data cloning, and data cleaning while undertaking storage migration.
A database can be defined as a storage medium in which data is contained in a structured and organized manner. These media are managed with the help of suitable database management systems (DBMS). Database migration is a process that involves moving records from one database management system to another. It can also involve users upgrading from an older DBMS to its newer counterpart.
As the name suggests, this is a type of data migration practice that is undertaken when an organization undergoes a change in application software or switches to a different application vendor. Here, the users are required to move data from one computing environment to another.
It is possible for a new application platform to need a radical transformation because of new application interactions after data migration. Here, users may encounter a major challenge from the old and target infrastructures containing distinctive data models. In such cases, vendors can provide you with suitable application programming interfaces (APIs) for protecting the integrity of your database.
This is one of the most relevant and prominent types of data migration processes. It involves the movement of data from an on-premise storage platform to a cloud-based platform or from one cloud-based platform to another.
In essence, this is a special kind of storage migration where one or more cloud-based platforms are involved. As more and more companies are switching to cloud platforms for the past few years, IT experts are witnessing a steady increase in cloud migration processes.
Business Process Migration
This is the type of data migration process that involves the movement of business applications and data stored on business processes and metrics to a new storage environment. These metrics can include products, customers, and operational information pertaining to an organization. Such migration processes are often instigated by business optimization/reorganization and mergers and acquisitions. Businesses undertake these data migration processes to enter into new markets and remain competitive in the industry.
Data Center Migration
This data migration process involves the migration of a data center infrastructure to a different physical location. It also involves the movement of datasets from old infrastructure equipment to new infrastructure equipment in the same physical location.
Essentially, a data center is the home of a data storage infrastructure that maintains the critical applications pertaining to an organization. It contains network routers, servers, computers, switches, storage devices, and related data equipment.
Different Approaches To Data Migration
There are two major approaches to data migration
Big Bang Data Migration Approach
The big bang data migration approach involves the migration of all datasets in a single operation from the source location to the target location. It is a simple and less complicated approach to migrating your data from one environment to another.
Implementing this approach implies that all your systems will be down and unavailable for your team members as long as the migration takes place. Such migration should, therefore, be conducted during days when your employees are not working on the systems.
The benefits provided by this approach are often offset by the risk of an unexpected and expensive failure because of the involvement of big data. Big data is likely to overwhelm the network during the transmission of your datasets. This is why the big bang data migration approach is suitable for smaller organizations that need to migrate smaller volumes of data. Also, this approach should never be used on systems that are not capable of sustaining downtime.
Trickle Data Migration Approach
This is a phased approach to migrating data from one environment to another. It involved breaking down the migration process into smaller sub-processes that transfer data in smaller increments. Here, the old systems stay operational and run parallel with the data migration processes.
The biggest advantage of using the trickle data migration approach is that there is no downtime in the live system. Moreover, this approach is less susceptible to failures and errors.
However, the iterative nature of this approach often makes the data migration process more complicated and longer. The entire process involves synchronizing data between the old and the new environments. Such an approach is ideal for bigger enterprises that manage big data and cannot afford any downtime within their systems.
The Data Migration Process
Here are the major steps involved in the process of undertaking data migration:
This is the stage that involves a thorough evaluation of the existing datasets to ensure a stable and secure migration. Here, the source and target systems are analyzed and data standards are set to spot potential data problems. Also, important decisions regarding the use of a suitable data migration approach are taken at this stage. This is the stage where the data migration budgets, schedules, timelines, and deadlines are charted out.
This is the stage where the scope of datasets to migrate is inspected to look for anomalies, data quality, and duplications. If the volume of your data is high, you can implement dedicated software platforms for cleaning the data to be migrated.
This is the important stage of backing up all the important datasets that need to be migrated to the desired target location. This allows you to guard your system against migration failures that may cause you to lose your valuable data.
Migration Process Design
This stage involves the stipulation of migration testing procedures, acceptance criteria, and other relevant personal responsibilities. This is the stage where organizations hire ETL developers or data engineers to handle the migration project. Companies also hire other specialists such as system and business analysts in this stage.
Execution And Validation
This is the stage where the execution of the migration process is rolled out after initiation. This is also the stage when the ETL processes go live.
Decommissioning And Monitoring
Finally, this post-migration stage involves shutting down and commissioning the old system.
The Final Word
These were some of the most important aspects to consider while venturing into the field of data migration for beginners. However, it is important to note that data migration is a fairly vast topic and requires a deep dive into the domain if you are willing to build a career in the same.