Comprehending Data Migration: Key Principles and Types to Follow

May 20, 2023

Transitioning data from one system to another may seem like a straightforward task in our daily lives, but it becomes more complex when dealing with massive amounts of data. Unfortunately, some companies underestimate the challenges of large-scale data migration, leading to unexpected costs and delays. Recent studies have shown that a significant number of data migration projects exceeded their budget, and many failed to meet expectations. To avoid falling into the same pitfalls, it's crucial to gain a comprehensive understanding of the critical aspects of data migration, including its triggers and final stages.

Understanding the Concept of Data Migration

Data migration involves the complex process of transferring historical data from one storage system or file format to another. While it may seem straightforward, successful data migration requires careful planning, backup creation, quality testing, and validation of the migrated data. The process is considered complete only when the previous system or database is deactivated. Data migration is often part of larger initiatives, such as legacy software modernization, system expansion, centralized database adoption, cloud migration, or consolidation of IT landscapes in merger and acquisition scenarios.

It is important to distinguish data migration from other processes like data integration and data replication, as they serve different purposes. Data migration is often a component of broader projects aimed at achieving interoperability, eliminating data silos, and improving system efficiency.

Distinguishing Between Data Migration and Data Integration

Data migration and data integration are two distinct processes in the realm of data management. While data migration involves the transfer of internal company data to a new system or format, data integration focuses on merging data from various sources to create a unified view. 

Data integration is essential for accurate analysis, business insights, and reporting as it enables connectivity between systems and allows access to content across different subjects. It plays a critical role in a company's data management strategy by facilitating the consolidation of datasets.

Unlike data migration, which is a one-time process that concludes once all data is transferred to the intended destination, data integration can be an ongoing process. It involves streaming real-time data and sharing information across multiple systems to ensure continuous synchronization and accessibility.

Distinguishing Data Migration from Data Replication

When considering data replication and data migration, the key distinction lies in their approach to data transfer. Data migration involves moving all data from one system or database to a new location, rendering the old system obsolete. On the other hand, data replication entails regularly copying data to a new location while preserving the original source. Replication has a starting point but lacks a definitive endpoint.

While data replication can be a component of data integration, it may eventually transition into data migration if the source storage is retired. However, in this article, our focus is solely on data migration, which resembles a one-time, unidirectional process akin to moving to a new residence and vacating the old one.

Exploring Various Types of Data Migration

There are six main categories of data migration, and these categories can sometimes overlap. For example, a specific data transfer could fall under multiple types, such as database migration and cloud migration, or it may involve both application and database migration simultaneously.

1.Storage Migration

Storage migration involves moving data from one physical medium or environment to another. This type of migration is commonly performed when a company upgrades its technology infrastructure. Examples include converting paper files to digital documents, transitioning from hard disk drives (HDDs) to more advanced solid-state drives (SSDs), or shifting data from mainframe computers to cloud storage.

2.Database Migration

Database migration entails transferring data from one database management system (DBMS) to another. This could involve upgrading to the latest version of the same DBMS (homogeneous migration) or migrating to a different DBMS from a different provider (heterogeneous migration). The latter scenario is often more challenging, especially when the source and target databases have different data structures or when migrating from legacy databases.

3.Application Migration

Application migration involves transferring data from one computing environment to another while adopting new enterprise software. For example, a hotel may decide to implement a new property management system, or a hospital may replace its legacy electronic health record (EHR) system. One of the main challenges in application migration is dealing with the differences in data models and formats between the old and new infrastructures.

4.Data Center Migration

Data center migration refers to the process of relocating critical applications and data from one physical location to another. This can include moving IT equipment like servers, switches, and networks to a new data center or transferring digital resources such as business applications and data to new servers and storage.

5.Business Process Migration

Business process migration is driven by factors such as mergers and acquisitions, business optimization, or reorganization efforts. In these cases, the transfer of business applications and databases containing customer, product, and operational data to a new environment may be necessary.

6.Cloud Migration

Cloud migration encompasses all the previous types, involving the movement of data from on-premises systems to the cloud or between different cloud environments. As cloud technology becomes increasingly prominent, organizations are adopting cloud-based solutions. The duration of a cloud migration project depends on factors such as data volume and differences between the source and destination environments. The complexity of the project and the costs associated with downtime influence the specific approach to the migration process, which can range from minutes to months or even years.

Exploring Different Approaches to Data Migration

Choosing the appropriate approach for data migration is crucial to ensure a successful project execution with minimal complications.

Big Bang Migration

One strategy commonly used for data migration is the Big Bang migration. It has its own set of advantages and disadvantages that need to be carefully evaluated before implementation.

The pros of the Big Bang migration approach include lower costs, reduced complexity, shorter timeframe, and implementation of all changes at once. However, there are also cons to consider, such as a higher risk of costly failure and the requirement for system downtime.

In the Big Bang migration approach, all data is transferred from the source to the destination environment in a single operation within a specified timeframe. During this process, the systems are typically unavailable to users as the data is transformed to meet the requirements of the new infrastructure. This migration usually takes place during holidays or weekends, assuming minimal user activity.

While the Big Bang approach allows for quick completion of the migration without the need to work across old and new systems simultaneously, it has limitations. With the increasing volume of Big Data, even medium-sized companies accumulate significant amounts of information, and network throughput and API gateways have their limits that need to be considered from the outset. Therefore, the Big Bang approach may not be suitable for mission-critical applications that require 24/7 availability. In summary, while the Big Bang approach may work for small companies or those dealing with small data volumes, other approaches may be necessary for larger organizations.

Trickle Data Migration

Another approach to data migration is phased or iterative migration, often referred to as trickle data migration. This strategy aims to minimize unexpected failures and eliminate downtime. It involves breaking down the migration process into smaller sub-migrations, each with its own objectives, timelines, scope, and quality checks. Although this approach requires more time and resources to maintain both the old and new systems, it offers a more reliable option with a lower risk of failure.

In a trickle migration, both the old and new systems run in parallel, and data is transferred in incremental portions. This method ensures zero downtime, allowing users to continue using the application without interruptions. However, this approach can be complex and time-consuming, as the migration team needs to track the already transferred data and enable users to switch between the two systems to access the required information.

An alternative way to execute trickle migration is to keep the old application fully functional until the migration process is completed. This ensures that users can continue using the old system while all data is transferred to the new environment. However, it adds complexity for the migration team, as they need to ensure real-time data synchronization between the two systems. This means that any changes made in the source system should automatically update the target system.

In conclusion, trickle migration is suitable for medium to large-sized organizations that cannot afford extended downtime and possess sufficient technical expertise to handle potential challenges.

Essential Guidelines for a Successful Data Migration Project

Achieving a successful data migration project requires businesses to adhere to essential guidelines and best practices. By following these principles, organizations can navigate the challenges and ensure a smooth transfer of their valuable data assets:

1. Embrace data migration as an opportunity to improve data quality: Use the migration process to identify and address data quality issues, setting high standards to enhance data and metadata.

2. Build a dedicated migration team: Assemble a team of data migration specialists who can effectively manage and oversee the project.

3. Streamline data by excluding unnecessary information: Reduce the amount of data to be migrated by identifying and excluding any unnecessary or irrelevant data.

4. Conduct thorough profiling of source data: Prior to creating mapping scripts, perform a comprehensive analysis of the source data to gain insights and understand its structure and characteristics.

5. Allocate sufficient time for the design phase: Dedicate ample time to the design phase of the project, as careful planning and consideration can significantly impact its overall success.

6. Avoid rushing the process: Instead of hastily turning off the old system, allow for flexibility in case of any issues or failed attempts. Consider performing rollbacks and retries to ensure a successful migration.

It's crucial to shift the perception of data migration from a mandatory and negative process to a valuable opportunity for innovation and improvement. By approaching data migration with the attention it deserves, businesses can overcome challenges and achieve a successful outcome.

Conclusion

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