With data of varying formats pouring in from different systems, the existing system may require upgrading to a larger one or migrating its data to another plan. But what is data migration? How does it work?
In short, it enables organizations to expand their data storage and management capabilities and fully use data to drive business decisions. It is a widely used process of moving data, and almost every organization undertakes one at some point.
According to the International Data Corporation (IDC), data migration represents 60% of any large enterprise IT project.
Following a proper plan to migrate your critical data using efficient tools is essential.
This article will discuss the definition of data migration, why it is needed, how to make an effective data migration plan, and what features one should look for when comparing data integration tools to migrate their business data. The blog will also clear the confusion amongst most data migration vs. data integration. Let’s begin with defining the process.
What is Data Migration?
Simply put, data migration refers to the transfer of data between different types of file formats, database schemas, and storage systems. However, data’ transfer’ is not the only aspect of the methodology. The migration includes mappings and transformations between source and target data if the data is diverse. This does not mean data migration and integration can be used interchangeably.
For example, an IT migration project could comprise a database conversion strategy, application migration, or software migration in which legacy systems are replaced, and new ones are introduced to work alongside the existing ones.
In the next section, we will look at the differences between migration and integration in greater detail.
Data quality must be assessed before migration to ensure a successful implementation without data loss. The success rate of any data migration project depends on the diversity, volume, and quality of data being transferred and the tool that makes it all possible.
Now, let’s explore how the migration process works through mapping and transformation. The following example illustrates a simple migration process flow diagram in which the migration of Orders data of an organization takes place from a delimited file format to an SQL server following sorting with respect to Customer ID.
Data migration process flow diagram. Source: Astera Centerprise Data Integrator
Data Migration vs. Data Integration
After discussing a data migration example, we can now discuss the difference between data migration vs. integration.
Data integration is the process of collecting and integrating data from multiple heterogeneous sources to unify data. On the other hand, data migration involves the movement of information. This transfer or movement could occur in numerous ways, such as data storage systems, system migration, and database migration. The main difference between the two processes is in data integration, disparate data is brought together. In contrast, in data migration, the data is transferred from one place to another using data migration software.
Why Need a Data Migration Software
The most common reason for data migration is moving data from one server to another or to a new system to scale up and accommodate volume growth. However, other circumstances may also prompt organizations to opt for a migration project. Some of the data integration examples and why it is done are as follows:
- To replace legacy systems that cannot keep up with the evolving performance requirements of the organization.
- To reduce data storage footprint by migrating to a system that consumes less power and space. Consequently, cutting down the operational costs.
- To stay competitive by adopting state-of-the-art technologies, paving the path for migration.
- To prepare a disaster recovery plan, you can secure a backup of all applications in another data center.
- To transfer data to the cloud, eliminating the cost of on-premises IT infrastructure.
Types of Data Migration Plans
Data migration technology can be broadly categorized into five types. Let’s look into how migration works in all these five types.
Database Migration
Database migration involves moving data between two database engines. However, when the original data changes, it simultaneously affects the data language or protocol. The change is reflected in the application layer as well. Free database migration tools modify data without modifying the schema.
The circumstances that demand this type of migration include:
- When the database migration software requires an update
- Cloud data migration of a database
- In case the organization needs to change database vendors
It requires careful planning and testing as several small tasks are involved in the process, such as determining the storage capacity of the target database, testing applications, and ensuring data confidentiality. Organizations prefer database migration services and software that can do the job seamlessly.
Application Migration
Application migration occurs when an organization switches from one platform or vendor application to another. Each application has a unique data model. Moreover, applications are not portable. Therefore, each application’s operating systems, virtual machine configurations, and management tools can differ in the development and deployment environment.
These factors can contribute to the complexity of the migration process. Introducing middleware products simplifies it, as they help bridge the technology gap making software migration easier.
Storage Migration
The store migration process involves moving data from one storage system to another, such as a hard disk or the cloud. The need for storage migration arises due to technology upgrades. During this process, it is easy to implement storage migration steps and data management features like data validation, cloning, reducing corrupt or old information, etc. In addition, storage migration enables cost-effective scaling and faster performance.
System Migration
What is system migration? Simply put, system migration aids the process of transferring resources to a different software platform or a new hardware infrastructure. Similarly, technology migration refers to upgrading technology to improve business processes.
Cloud Migration
Moving to the cloud ensures scalability, requires fewer storage resources, and is cost-effective. Therefore, cloud data migration has become one of the latest trends in the data management industry. The organization deploys its complete or partial information assets, applications, or services to the cloud or from one cloud to another during cloud data migration. This allows them to take advantage of the benefits of a cloud environment. The cloud’s firewall protects the migrated data, and the organization can easily implement it through cloud data migration tools.
Data Migration Techniques
A company may use robust data migration software or an ETL tool for data migration but would fail to leverage its full potential without an efficient migration plan. Several factors determine the correct migration technique for an organization, such as the available resources, data volume, data sensitivity, and business requirements. The method that offers a blend of reliability, migration efficiency, and minimum impact on users and business processes is the one that can best serve the organization’s needs.
TDWI did a survey to identify the technologies organizations use to migrate or consolidate databases. ETL emerged as the preferred technology, with 41% of respondents voting in its favor. Other answers included: manual coding (27%), database replication (11%), and Enterprise Application Integration (3.5%).
ETL Emerges as the Preferred Choice for Data Migration
Extract, Load, Transform (ETL)
ETL tools can handle the complex requirements of the data migration process. These include processing big data sets, in-depth data profiling, and integrating multiple platforms. Some ETL tools even provide the capability of automating standard ETL tasks, such as retrieving data from operational systems, transforming it into a unified format, and loading it into a destination database.
Planning to Migrate data? Steps to a Successful Enterprise Data Migration
Several steps include executing a successful data transfer technique and choosing the right data migration software. These include:
1. Design a Data Migration Strategy
Selecting a data migration strategy depends on the use case. Is it because the organization consolidates systems following an acquisition or a merger, or data overload? Solidify the business objectives, laying the groundwork for the plan.
In the next step of the data migration plan, the organization needs to decide whether to go for data migration services or enterprise-grade ETL tools designed to facilitate the process. Several service providers will identify the source and destination systems, generate workflows, and migrate the content. Alternatively, purchasing code-free data migration software allows you to transfer at your convenience, speeds up the project, and reduces risks. Simultaneously, it can lower the overall project cost.
By strategizing beforehand to either purchase enterprise-grade ETL tools or code-free migration software, the organization can inform business and IT users regarding the goals of the data migration project and ensure enterprise-wide successful adoption and implementation.
2. Assess and Analyze
The next step in the data migration plan is to analyze enterprise data. Answering the following questions can help with this assessment:
- How much of the enterprise data requires migration?
- Is the data structured or unstructured?
- Is data redundancy going to be an issue?
- How much of the data is ROT (redundant, obsolete, or trivial)?
- Is the information old or recent?
Combine all the workflows and shared drives that store data to determine its use throughout the organization’s data migration approach.
3. Collect and Cleanse data
This step involves removing ROT data, compressing the remaining content, and converting enterprise-wide data into a single format, for example, in Figure. One data profiling transformation is applied to the order data of a company to examine the available information and remove duplicate/redundant records. Refer to Figure. 2 for the details of the data set.
Fig. 1. Data Profiling
Fig. 2: Data Profiling Result
Manual execution of these tasks can be time and resource-intensive for large datasets. Therefore, finding migration software that automates these tasks can make the data migration process efficient and error-free.
4. Sort Data
Once you have profiled the data into a high-quality and usable form, the next phase is categorizing it according to the migration requirements. The categorization can be based on product type, ID, or other criteria. It makes it easier to route data into suitable buckets.
Figure 3 below shows the profiled data sorted with respect to the Product ID.
Fig.3: Applying Sort Transformation
5. Validate data
This step involves reviewing the execution process. You should assess the data rules, check if they work as intended, and map out any exceptions in your data flow. It is advisable to check the automated process to prevent unexpected challenges during the implementation of the data migration process.
After sorting the data, we will apply the data quality rules to determine the errors based on the defined rules. In this scenario, we use data quality rules to identify and eliminate the records in which the field ‘Quantity’ has a zero value.
Fig.4: Applying Data Quality Rules
6. Migrate
This is the final step in the data migration process in which everything falls into place. Once you follow the steps mentioned above, you will have well-organized and clean datasets. After that, you just need to migrate them from one system to another. Once you migrate the data to the target system, such as a database or a data repository like a data mart or a data warehouse, the process is complete. The employees can then access it to fuel data analytics, streamline workflows, enhance data security, and more.
In the example below, we migrate the data to the SQL server after profiling, sorting, and checking it for errors.
Fig. 5: Data Migrated to the Destination Database
Data Migration Software – An Overview
Data migration tools are divided into three types:
- On-premises: Installed on-premises, these tools enable data transfer within the organization.
- Open-source: Community-developed data migration tools available at a low price or free of charge.
- Cloud-based: Cloud data migration tools move data from different systems (including applications, cloud, on-premises data stores, and services) to the cloud.
Finding the Right Data Migration Software – Features to Consider
The key to selecting the right data migration software is research. Initially, browsing through review websites like Capterra, G2 Crowd, etc., will give you a fair idea of what options are available in the industry. The next step is to know the basic features that will help you perform successful enterprise data migration.
Here are the essential features that you should consider when selecting a data migration tool:
Easy Data Mapping
It is essential to map data from source to destination to transfer data seamlessly. To enable business personnel and data professionals to use these processes easily, it’s best to go for a tool that offers a code-free, drag-and-drop, graphical user interface.
Advanced-Data Integration and Transformation Capabilities
Executing a successful migration project involves extracting data from the desired source, identifying quality issues and errors through profiling, and transforming it to follow the destination schema. A data management solution that offers connectivity to a wide range of heterogeneous sources, ETL, data quality management, and data transformation features restructures data for targeted delivery and ensures that the enterprises can execute complex migration projects and get maximum value from the migrated data.
Enhanced Connectivity
A data migration tool should seamlessly connect with disparate source and destination data structures, such as XML, JSON, EDI, etc. If your business requires connectivity to business applications (Microsoft Dynamics CRM, Salesforce, etc.) or databases (IBM DB2, Teradata), look for a tool that offers pre-built connectors for these applications.
Automated Data Migration
The best data migration software incorporates workflow orchestration and job scheduling, eliminating manual work, streamlining data processes, and enabling faster data transfer. Especially if your migration project involves large data sets, automating the process offers additional benefits such as reduced implementation time, lower project costs, and improved ROI.
Make Your Data Migration Process Flawless!
Astera Centerprise is an all-inclusive data management solution that enables businesses to migrate data faster in an easy-to-use, code-free environment. With the capability to process large data volumes and complex hierarchal structures like EDI, XML, JSON, and more, Astera Centerprise has successfully carried out complex data migration projects for several Fortune 1000 companies.
Also, the data movement software offers a combination of automation, built-in data quality, profiling features, advanced transformation capabilities, and extensibility to add custom functions to the project.
Want to see how Astera Centerprise software can successfully create and execute your data migration process without coding? Try our 14-day free trial.
