Cloud Data Migration Process: A Step-by-Step Guide to Transferring Data to the Cloud

Cloud Data Migration Process: A Step-by-Step Guide to Transferring Data to the Cloud

Mateusz Łach - January 31, 2023 - updated on January 30, 2023

Are you considering a data migration project? Whether you’re looking to upgrade to a new system, centralize your data, or move to the cloud, data migration can be a complex and challenging process. But with proper planning and execution, it also brings significant benefits to your organization.

In this article, we’ll explore the basics of data migration, including common use cases, benefits, and challenges. We’ll also provide best practices and tips for a successful data migration strategy that will enable you to unlock the full potential of your digital assets.

What Is Data Migration?

Data migration is the process of transferring data from an existing source to another destination. This might involve anything from moving data from one cloud service to another, migrating data from a legacy application to a new one, or transforming data within the same platform.

Migration can also include data conversion, cleansing, data validation, reorganization, data security, and archiving. Each of these steps plays a vital role in guaranteeing the accuracy and usability of data migrations in the target system.

This differs from data integration, which involves combining data from multiple data sources and providing a single view of them in the new destination.

Reasons for Conducting a Data Migration Project

Organizations use data migration for a variety of reasons and to serve various data-related objectives. Some of the more popular use cases of this type of data management include:

  • Upgrading to a new system – storage migration to keep data safe and up-to-date while also making it easier to access and manage.
  • Migrating data from legacy systems to the cloud – provides increased scalability and reduced costs, as well as improved security (i.e., protection against cyber-attacks).
  • Merging data from multiple data sources – allows viewing across several storage locations, such as database migration combining data from a data warehouse and an operational database, to create a more comprehensive data set.
  • Streamlining data visibility and access – for easier analysis thanks to database migration to a modern system, where it can be seen and used by all stakeholders to improve business processes towards making quicker, data-driven decisions.
  • Data archiving – facilitates data retention and compliance (that is, meeting data privacy regulations).
  • Consolidating data into one system or location – data center migration to decrease storage costs.
  • Improving data quality and integrity – applying various data conversion processes to guarantee quality for different types of data, for consistency across various systems and sources, and thanks to policies preventing data loss (e.g., through regular backups).

Types of Data Migration

Data migrations can involve moving data from one type, format, or structure to another. Here are some of the more common data migration types:

Structural Changes to the Data or Database

The first type of data migration involves modifying a data model or database schema. It also includes converting data from one type to another, such as changing data from XML to JSON or vice versa.

Furthermore, there is data cleansing (filling in missing data or fixing errors), normalization (standardizing data across multiple data sources), and segmentation (dividing data into smaller pieces for better management).

Finally, logical changes to the data or database are modifications without changing the structure. This includes data mapping (matching data from one source to data in another source) and data harmonization (ensuring data from different sources is consistent).

Upgrading to a New System or Software

This might mean to physically transfer data from one computing system (the source) to another (the destination). This includes data replication, transfer, synchronization, backup, recovery, and archiving.

Additionally, it’s necessary to test the data after the upgrade has been completed, as data integrity and accuracy must be confirmed to verify usability in the target system.

Consolidating or Centralizing Data From Multiple Sources

Combining data from several different databases, warehouses, data lakes, and/or data silos into a single repository where it will be structured for easier access and analysis. Again, the data must be checked for integrity, accuracy, consistency, completeness, and validity.

Moving Data to a Cloud Environment

One of the most popular data migration projects, since it offers several advantages over on-premise data storage and management. Data transfers to the cloud require drafting a migration plan, data cleansing and preparation, data synchronization, and testing.

The end result is data that is secure, accessible from anywhere, and can be scaled up or down depending on the data processing needs of the organization.

Moving on-premise data to a cloud environment (private cloud or public cloud)

Moving on-premise data to a cloud environment (private cloud or public cloud)

Data Migration Process

The data migration process itself, regardless of the data type or data source, consists of a series of steps. Keep in mind that, like any software development project, data migration has to have a clear set of business goals laid out and shared with all stakeholders.

Step 1: Data Strategy, Planning, and Preparation

This is when data requirements and business objectives are analyzed, data cleansing strategies are developed, data mapping is carried out (if necessary), security measures are put into place, and a migration plan is drafted.

Assessing Your Data and Storage Needs

In this step, data is evaluated for its usefulness and relevance to determine what needs to be migrated (as well as what doesn’t) and its storage requirements. Get to know and comprehend what data you will migrate to the target system and how it will fit there.

Identifying the Sources and Destinations of the Data

This involves data mapping (matching data from one source to that in another) and data harmonization (checking that data from different sources is consistent) to guarantee that data is moved to and from the correct places.

Developing a Data Migration Plan and Timeline

The data migration plan should outline the steps that will be taken for data migration, including an estimated schedule for each and the overall completion of the process. For the data migration strategy, also consider user access levels and privileges for everyone who will be working with the data.

Step 2: Data Extraction and Transformation

The data is extracted from the source system, prepared, and transformed (if necessary).

Extracting Data From the Source System

After first choosing which data will be migrated, the next step is pulling data from the source system. This can be done manually or via an automated data extraction tool.

Cleaning and Formatting the Data for the Destination System

Fixing errors and confirming consistency with the target system, which might require the data to be split, merged, normalized (standardized across multiple data sources), segmented (divided into smaller pieces for better management), and/or reformatted to fit the target data structure.

Mapping Data Fields Between the Source and Destination

Data migration tools can be used to map data fields and structures so that data is transferred to the right place and that in the correct order.

Step 3: Data Load and Validation

Data is loaded into the target system or cloud environment to confirm its integrity.

Transferring the Data to the Destination System

This can be done either by an automated data migration tool or via manual data entry (by writing dedicated migration scripts).

Verifying Data Integrity and Completeness

Data validation is key to migration success and includes quality checks such as data profiling – that is, detecting patterns to look for errors.

Ensuring Data Consistency and Accuracy

Data validation and reconciliation are also carried out at this stage to confirm that data is consistent between the source and the target.

Step 4: Testing and Go-Live

Before fully switching over to the cloud, the migrated data is examined in the new environment.

Testing the Migrated Data in a Staging Environment

Data is tested in a staging environment to establish whether or not everything works in the cloud as it did in the source system.

Switching Over to the New System

Once data has been successfully tested in the staging environment, the switch to cloud computing can be fully made, and the migration process is complete.

Monitoring and Maintaining the Migrated Data

Data in the cloud environment needs to be observed over time to guarantee quality and accuracy. This could include data cleansing, archiving, backing up, putting recovery procedures in place, de-duplication (removing repeating records), implementing security protocols, and regular auditing.

Moreover, your plan should be periodically reviewed and updated so that any future migrations stay current and up-to-date with the latest technology and trends to maintain data accuracy, security, and reliability in the cloud.

Benefits of Data Migration

Migrating to the cloud is associated with a whole host of advantages compared to data stored on-premises, and some of the main plus points are explored in detail below.

Improved Data Quality and Accuracy

Since data maintained in the cloud is easier to manage, it is often more accurate and up-to-date than that which is stored on-premises. Also, since the data is subject to the abovementioned validation and reconciliation processes, its accuracy and consistency across data sources is continually monitored and verified.

Enhanced Data Security and Compliance

Data stored in the cloud is generally better protected from data breaches, virus attacks, and other cyber threats, especially if it is maintained in a secure data center that is managed by a reliable third-party provider. Furthermore, cloud storage takes advantage of advanced data protection measures and meets various privacy compliance standards, such as GDPR and HIPAA.

Increased Efficiency and Productivity

Migrating to the cloud may improve businesses’ decision-making since information is more accessible and easier to analyze. It also enables data-driven processes such as automation and data mining, which in turn leads to increased efficiency and productivity.

Reduced IT Costs and Maintenance Burden

Moving data to the cloud eliminates the need for in-house IT infrastructure, significantly reducing expenditure in that area. Additionally, when maintenance is handled by the data migration solution, organizations are able to save on both labor costs and the associated effort.

Challenges of Data Migration

The cloud is not without its obstacles, and some of the key issues to consider are outlined below.

The Complexity of the Migration Process

Moving to the cloud can be difficult and time-consuming, especially if there are large volumes of data involved. The lack of proper planning might therefore result in security issues or integrity problems.

Risk of Data Loss or Corruption

When data is migrated, there is always a risk that it could be corrupted or lost due to data center downtime, synchronization failure, or conversion errors, just to name a few potential difficulties.

Downtime and Disruptions to Business Operations

When data is being migrated, it may be inaccessible for a certain period of time. This can lead to the disruption of services, ultimately impacting the ability to conduct normal operations until the data is successfully transferred to the cloud.

Cost and Resources Required for the Migration

Despite the long-term savings mentioned above, actually moving data to the cloud may be expensive – from purchasing storage space to the implementation of security procedures.

The process also requires a significant amount of resources in terms of people, hardware, and software, etc.

Data Migration Best Practices

When cloud migration is planned and executed properly, organizations can minimize the associated risks and maximize the return on investment. That’s why it’s worth following some tips to complete the process as smoothly as possible.

Planning and Preparation Are Key

A data migration project should start with an in-depth assessment of data size, sources, and the current environment. These details will contribute to determining the best data migration strategy and selecting the most suitable cloud provider.

Test and Validate the Migrated Data

It’s crucial to run diagnostic checks after completing the migration process in order to verify that data was properly transferred, is accurate, and secure. Without ticking off this step, data integrity may be compromised.

Use Automated Tools and Processes When Possible

Automation saves time and effort, with the right data migration software enabling quick and accurate transfer. Additionally, organizations should look for solutions that provide data synchronization and ongoing data management to further automate the monitoring stage of the process.

Implement Proper Data Governance and Security Measures

Data must be well-structured and organized for it to be easily managed and accessed. Businesses migrating their data to the cloud ought to also employ data encryption, access control policies, data loss prevention procedures, and other relevant security protocols.

Data Migration: The Key to Unlocking Your Digital Potential

Moving to the cloud is a complex process, but when done right, it can provide data-driven companies with a secure, efficient, and cost-effective way to store and access data. Following the migration steps and best practices in this article will allow your organization to reap the benefits of cloud data migration whilst dealing with any challenges that may arise.

Leveraging the power of data migration lets you unlock the digital potential of your business, so why not get started today with assistance from nexocode engineers? Happy migrating!

About the author

Mateusz Łach

Mateusz Łach

AI & Digital Business Consultant

Linkedin profile

Mateusz is a digital strategist and innovation enthusiast. He enjoys building new products and concepts, often with the help of AI. Mateusz joined Nexocode with the mission to consult startups, mid-size companies, and enterprises on their digital transformation journey and help them benefit from custom artificial intelligence solutions.
Responsible for overall business development and sales activities. A geek of new technologies.

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