Data Migration – The Why, The What, and The How

By |2020-06-12T21:47:26+00:00January 15th, 2019|

With data of varying formats pouring in from different systems, the existing system may require an upgrade to a larger server. Data migration enables organizations to expand their data store and management capabilities and make full use of data to drive business decisions. It is a widely used process of moving data, and almost every organization undertakes one at some point.

What Is Data Migration?

According to the International Data Corporation (IDC), data migration represents 60% of any large enterprise IT project. But what is it?

Simply put, it refers to the data transfer between different types of file formats, databases, and storage systems. However, ‘transfer’ is not the only aspect of data migration methodology. If the data is diverse, the migration process includes mappings and transformations between source and target data. Above all, data quality must be assessed before migration to ensure a successful implementation. The success rate of any data migration project is directly dependent on the diversity, volume, and quality of data being transferred.

The following example illustrates a simple data migration flow in which the migration of Orders data of an organization takes place from a delimited file format to SQL server following sorting with respect to Customer ID.

Data Migration Software and Techniques - what is data migration

Data Migration Example

Situations That Prompt Data Migration

The most common reason for migration is the need to move data to a new system to scale up and accommodate volume growth. However, other circumstances may also prompt organizations to opt for a data migration project. The reasons include:

  • To replace legacy systems that are unable to keep up with the evolving performance requirements of the organization.
  • To reduce 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 data migration.
  • To prepare a disaster recovery plan; as data migration can help create a secure
  • To transfer data to the cloud, eliminating the cost of on-premise IT infrastructure.

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Types of Data Migration

Data migration can be broadly categorized into four types:

Database Migration

This type of 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. Database migration modifies data without modifying the schema.

The circumstances that demand this type of migration include:

  • When the database software requires an update
  • To migrate a database to the cloud
  • In case the organization needs to change database vendors

It requires careful planning and testing as there are several small tasks involved in the process, such as determining the storage capacity of the target database, testing applications, and ensuring data confidentiality.

Application Migration

This type of 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, the operating systems, virtual machine configurations, and management tools of each application can be different in the development and deployment environment.

These factors can contribute to the complexity of the migration process. Introducing middleware products in the process simplifies it; as they help bridge the technology gap.

Storage Migration

The process deals with 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. It is easy to implement data management features like data validation, cloning, reducing corrupt or old information, etc. during this process. In addition, storage migration enables cost-effective scaling and faster performance.

Cloud Migration

Moving to the cloud ensures scalability, requires fewer storage resources, and is cost-effective and therefore, cloud migration has become one of the latest trends in the data management industry. In cloud migration, an organization’s complete or partial information assets, applications, or services are deployed to the cloud. The cloud’s firewall protects the migrated data.

Data Migration Techniques

Several factors determine the right 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 conducted a survey to identify the technologies organizations use to migrate or consolidate databases. ETL emerged as the preferred technology with 41% respondents voting in its favor. Other answers included: manual coding (27%), database replication (11%), and Enterprise Application Integration (3.5%).

Data Migration Software and Techniques 1

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 integration between multiple platforms. Some ETL tools even provide the capability of automating the 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? Steps to A Successful Enterprise Data Migration

Data migration is more than just moving data for one repository to another. Executing a successful transfer includes several steps. These include:

1.      Design a Strategy

Selecting a data migration strategy depends on the need for data migration. Is it because the organization is consolidating systems following an acquisition or a merger or is it because of data overload? Solidifying the business objectives will lay the groundwork for the data migration plan.

Next, the organization needs to decide whether to go for data migration services or enterprise-grade ETL tools designed to facilitate data migration. There are several service providers that will identify the source and destination systems, generate workflows, and migrate the content. Alternatively, purchasing a code-free data migration solution gives you the liberty to carry out the transfer at your convenience, speeds up the project, and reduces risks. Simultaneously, it can lower the overall project cost.

By strategizing beforehand, the organization can inform business and IT users regarding the goals of the migration project and ensure enterprise-wide successful adoption and implementation.

2.      Assess and Analyze

The next step 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?

Comb through all the workflows and shared drives that store data to figure out its use throughout the organization.

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. 1 data profiling transformation is applied on the order data of a company to examine the available information and remove duplicate/redundant records. Figure. 2 shows the details of the data set.

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Fig. 1. Data Profiling

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Fig. 2: Data Profiling Result

Manual execution of these tasks can be time and resource intensive for large datasets. Therefore, finding a solution that automates these tasks can make the 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 to categorize it according to the migration requirements. The categorization can be done on the basis of product type, ID, or any other criteria. It makes easier to route data into the right buckets.

Continuing the above-mentioned example, figure. 3 shows the profiled data sorted with respect to the Product ID.

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Fig.3: Applying Sort Transformation

5.      Validate Data

This step involves reviewing the execution process. Assess the data rules and check if they are working the way they are supposed to, and map out any exceptions in your dataflow. Even if the process is automated, it’s best to keep a check on it to ensure that you don’t encounter any unexpected challenges when implementing the process.

After sorting the data, we will apply the data quality rules to determine the errors based on defined rules. In this scenario, we are using data quality rules to identify and eliminate the records in which the field ‘Quantity’ has a zero value.

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Fig.4: Applying Data Quality Rules

6.      Migrate

This is the final step in which everything falls into place. The steps mentioned above will give you well-organized and clean datasets; all you have to do is migrate them from one system to another. Once the data is migrated to the target system, which can be a database or a data repository, such as a data mart or a data warehouse. The employees can then access it to fuel data analytics, streamline workflows, enhance data security, and more.

In the example below, the data is migrated to SQL server after being profiled, sorted, and checked for errors.

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Fig. 5: Data Migrated to the Destination Database

Data Migration Tools – An Overview

Data migration tools are divided into three types:

  • On-premise: Installed on-premise to enable data transfer within the organization.
  • Open-source: Community-developed tools available at a low price or free of charge.
  • Cloud-based: Available on the cloud, these tools move data from different systems (including applications, cloud and on-premise data stores, services) to the cloud.

Finding the Right Migration Software – Features to Consider

The key to selecting the right data migration tool is research. 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 about the basic features that will help you perform successful enterprise data migration.

Here are the important features that you should consider when selecting a data migration tool:

Easy Data Mapping

To carry out a successful data migration process, it’s essential to correctly map data from source to destination. To enable business personnel and data professionals 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, and data quality and transform 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

Features like workflow orchestration and job scheduling eliminate manual work, streamline data processes, and enable faster data migration. 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.

Astera Centerprise – Making the Data Migration Process Painless

Astera Centerprise is an all-inclusive data management solution that enables businesses to accomplish faster data migrations 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 software offers a combination of automation, built-in data quality and profiling features, advanced transformation capabilities, and extensibility to add custom functions.

Want to find out how Astera Centerprise can successfully create and execute your data migration strategy without coding? Download the free trial version and experience for yourself!