8 Best Practices for a Seamless Data Migration to the Cloud

By Michael Meyer

Published on August 15, 2023

cloud chip showcased as a data chip

Organizations continue to accelerate their digital transformation initiatives to remain agile and competitive. Over the last few years, companies increasingly migrated their data to the cloud because these modern data architectures deliver business agility, innovation, and security. By aggregating data in the cloud, departments can eliminate silos and unlock the full value of data across the organization. 

To successfully modernize cloud transformation processes, follow these best practices for migrating data to the cloud. 

1. Evaluate existing data

To plan an effective migration, it’s essential to understand the data. At a high level, this means identifying and analyzing:

  • Legacy databases

  • Applications using legacy data

  • Interdependencies between applications and databases

  • Review of data archival policies to remove old data before migrating 

The evaluation process enables the organization to understand the data’s quality and accuracy so that redundancies can be eliminated and errors fixed prior to the migration. 

Further, the assessment enables the organization to develop a plan that includes the following:

  • Moving data between legacy databases and the cloud

  • Reconciling any data integration concerns

  • Setting the budget

  • Prioritizing most used data first

  • Identifying key stakeholders of the data 

  • Creating a timeline that minimizes possible downtime and business operational impact

2. Choose the right cloud provider

As with all technologies, each cloud provider offers different benefits. If a company is already invested in a specific provider’s application ecosystem, then using that cloud provider might seem the most obvious. However, the obvious and best are not always the same thing. 

Some considerations when choosing a cloud provider include:

  • Security: Does the basic offering provided with the subscription align with the organization's needs? Will the organization need to purchase additional security solutions?

  • Compliance: Does the provider offer reporting to help achieve compliance with data protection laws like HIPAA and GDPR?

  • Service-level agreements (SLAs): What are the contractual agreements about uptime, support, and maintenance?

  • Cost: What are the provider's upfront, usage pricing, and volume discounts?

  • FinOps: What does the provider have to help visualize and optimize cloud costs? 

Since Azure, AWS, and Google Cloud offer pricing calculators, organizations can define their use-case scenarios, get estimates, and compare them. 

3. Plan a migration strategy

With a well-defined cloud data migration strategy, an organization accelerates the process while experiencing as little business disruption as possible. 

Most organizations choose one of the following six strategies:

  • Rehosting: The “lift and shift” model transfers a complete copy of legacy data, but it can be cost-prohibitive while providing little business value.

  • Replatforming: The “move and improve” model moves a slightly adjusted version of the basic data architecture to the cloud to enable scalability and performance, but it is unable to leverage the cloud’s capabilities entirely. 

  • Rearchitecing: The organization builds an all-new infrastructure and processes to obtain all the benefits of cloud computing, but the process can be more time-consuming, labor-intensive, and expensive. 

When choosing the migration strategy, stakeholders should calculate costs associated with maintaining their cloud infrastructure requirements to right-size their costs. 

4. Optimize data for the cloud

Optimizing data reduces migration costs by reducing the amount and size of the data transferred. Some common optimization techniques include:

  • Data compression: Using algorithms to reduce the size of data to make data transfers and storage efficient and cost-effective.

  • Data deduplication: Comparing data to find and remove duplicate copies to reduce overall storage needs and improve processing times.

  • Data archival: Removing any old data that is out of retention policy or that is no longer used. 

5. Ensure data security

Every data migration strategy needs to incorporate data protection. Evolving into a data-driven organization typically means the company collects and processes sensitive data including:

  • Names

  • Email addresses

  • Physical addresses

  • Dates of birth

  • Bank account information

Organizations need to reduce security and privacy risks to protect themselves from the costs associated with data breaches and to comply with mission-critical data protection laws. Across industry verticals, nearly every organization must comply with data protection regulations such as HIPAA, GDPR, CPRA, or SOX.

Companies use this information to understand customer preferences, determine new product lines, and optimize sales cycles. When planning the migration, they need to incorporate data security and privacy protections that mitigate risks, like:

  • Using VPN and other network techniques to avoid transferring data over the public internet.

  • Encrypting data-at-rest and in-transit to make data useless to malicious actors, even if they gained access.

  • Limiting user access according to the principle of least privilege to mitigate risks arising from people with legitimate credentials accessing sensitive data they do not need to do their jobs. Implement data masking where appropriate.

  • Creating a secure, encrypted backup to protect data integrity.

6. Test the migration

When testing the migration, the organization compares an application’s on-premises performance to its cloud-based performance to ensure that it works as expected. Using these tests, the organization can identify and quantify issues before moving into production. 

Some essential tests to run include:

  • Data validation: ensure results from queries, reports, and dashboards are accurate. If running in parallel with the legacy database system, cross reference results.

  • Functional validation: proves that the data pipelines are working as intended and producing correct results.

  • Performance: simulates real-world activity to measure data volumes, capacity loads, and response time.

  • Integration: verifies that connections between applications and databases work as intended.

7. Train the team

Training users and stakeholders are critical. A cloud migration is more than a lift-and-shift; it changes the organization’s data culture. Across the organization, users and stakeholders need to understand, develop, and adopt best practices for data management. However, different types of users require additional training and skills. 

For example, developers should receive training like:

  • Fundamentals of the chosen cloud platform

  • Hands-on labs for experience with technologies, cloud data warehouses, and security

  • Programming languages like Java, Python, HTML, SQL, and serverless computing

However, business users need to learn how to work with the technologies, so they would receive training like:

  • Data management

  • Data manipulation

  • Data modeling

  • Hands-on labs for using databases, data manipulation, and data analytics

  • The fundamentals of data science and machine learning 

  • The impact cloud technologies have on operations

  • Cost optimization strategies

8. Monitor and optimize cloud environments

After completing the migration, the optimization work begins. Application data access may behave differently in the cloud than what it did when it was on-premises. Additionally, the organization should monitor to ensure they stay within their budgets. 

Some best practices for monitoring and optimizing cloud environments include:

  • Implementing a robust data governance strategy to maintain data integrity, confidentiality, accessibility, accuracy, and quality 

  • Creating monthly budgets to verify costs againstIntegrating cost management tools for visibility into cloud-resource use 

  • Determine the right size of compute resources needed for data pipelines and also data access

  • Look for opportunities where pre-aggregating data can optimize compute time

How Alation can help drive a seamless data migration

Cloud data migration is critical as organizations work to reduce costs and gain a competitive edge. With Alation, teams gain the visibility necessary for a successful data migration to the cloud. Leaders can use Alation’s data catalog to identify critical data assets with a single view of enterprise data across all locations. Creating a unified view of all enterprise data and its context accelerates the cloud transformation strategy while minimizing disruptions that can impact business users. 

Alation’s cloud data migration capabilities allow teams to conduct a deep impact analysis, proactively minimizing risk and ultimately optimizing cloud investments. 

  • 1. Evaluate existing data
  • 2. Choose the right cloud provider
  • 3. Plan a migration strategy
  • 4. Optimize data for the cloud
  • 5. Ensure data security
  • 6. Test the migration
  • 7. Train the team
  • 8. Monitor and optimize cloud environments
  • How Alation can help drive a seamless data migration
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