Data Migration: On-Premise to Cloud - A Step-by-Step Guide

Last Updated on: May 19th, 2023, Published on: May 19th, 2023

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Migrating your organization’s data from on-premise to a cloud-based infrastructure is a multi-step process. It requires a thorough understanding of your current data setup, meticulous planning, and careful execution.

In this blog, we will break down the process of moving your data to the cloud into simple and manageable steps.

Let’s dive in!

Table of contents

  1. Drawing the roadmap for migrating data from on-premise to cloud: First steps
  2. Migrating data from on-premise to cloud: A detailed walkthrough
  3. Visualize the migration process
  4. Implementing data migration: Impending challenges and how to face them
  5. How data governance plays a crucial role in cloud migration?
  6. Rounding it up all together
  7. Data migration on-premise to cloud: Related reads

Drawing the roadmap for migrating data from on-premise to cloud: First steps

Here’s a high-level roadmap for this migration:

  1. Understand your current state
  2. Define the migration strategy
  3. Choose your cloud provider
  4. Data modeling and architecture
  5. Security and compliance
  6. Prepare for migration
  7. Data migration
  8. Validate the migration
  9. Transitioning systems
  10. Monitoring and optimization

Now, let us look into each of the above steps in brief:

1. Understand your current state


Identify the data that you are currently storing and processing. This includes not just the data itself, but also the workflows, data pipelines, and systems that are using that data. Understanding dependencies is crucial.

2. Define the migration strategy


You’ll need to decide whether to lift and shift (rehosting), refactor (platforming), or rewrite (refactoring) your applications. The best approach will depend on many factors including time, budget, application architecture, and future goals.

3. Choose your cloud provider


AWS, Google Cloud, and Microsoft Azure are leading providers with robust data platforms. The choice of a data platform depends on the features, cost, compatibility with your existing systems, and the skill set of your team.

4. Data modeling and architecture


Design your data architecture in the cloud. This may include creating data warehouses, data lakes, or a combination of both. You should also model your data in a way that supports your business’s analytical needs.

5. Security and compliance


Define your security and governance model. Understand the regulatory requirements applicable to your data and ensure that the selected cloud platform can meet these requirements.

6. Prepare for migration


Cleanse your data. Ensure that your data is accurate, complete, and formatted correctly before starting the migration process.

7. Data migration


Migrate your data. Tools such as AWS Data Migration Service, Google’s Transfer Service, or Azure’s Data Box can be used to move your data.

8. Validate the migration


Verify that the data was migrated correctly. This can involve checking record counts, data integrity, or more detailed data auditing.

9. Transitioning systems


Switch over your systems and workflows to use the new cloud-based data storage and processing services.

10. Monitoring and optimization


Implement monitoring to ensure the system is working as expected, and regularly optimize for performance and cost.

This process isn’t strictly linear and often requires revisiting previous steps as you discover new information or if issues arise. It’s also not a one-size-fits-all approach. Your organization’s specific needs may require adjusting this general plan.

Migrating data from on-premise to cloud: A detailed walkthrough

Now, let us look into each of the above steps in the previous section in greater detail:

1. Understand your current state


This is the discovery phase. Interview key stakeholders, system owners, and users to understand their needs.

Map out data sources, consumers, and flows between systems. Document the format, frequency, and size of your data. Identify which systems or services depend on each other, as these dependencies will need to be considered during the migration.

2. Define the migration strategy


This strategy will be guided by your current and future business requirements. It would depend on the nature of your data and applications, budget and time constraints, and the technical capabilities of your team.

Some applications may be best suited to a lift-and-shift approach. In this case, you will need to move them to the cloud with minimal changes. While others may need to be refactored or rewritten to fully take advantage of cloud features.

3. Choose your cloud provider


AWS, Azure, and Google Cloud each have their strengths and weaknesses. Research their offerings and pricing models.

Consider the skills and experience within your team - if your team has deep knowledge of a particular platform, this could influence your decision. You may also want to consider using multiple cloud providers to avoid vendor lock-in.

4. Data modeling and architecture


Design how your data will be stored, processed, and accessed in the cloud. Data lakes store raw data in a flexible, schema-on-read model, while data warehouses use a structured, schema-on-write model.

You may also want to consider hybrid models. Create a plan for backing up and restoring data. Your architecture should support both your current use cases and anticipated future needs.

5. Security and compliance


Make sure your cloud provider supports all necessary data protection and privacy regulations applicable to your business (like GDPR, CCPA, etc).

Implement encryption for data at rest and in transit. Define roles and permissions to restrict access to sensitive data. Consider using tools for monitoring and alerting on security incidents.

6. Prepare for migration


This step involves the practicalities of moving your data. Clean up your data - remove duplicates, fill in missing values, correct inaccuracies, and standardized formats.

Determine a migration schedule that minimizes downtime. Choose the right tools to move your data, such as cloud providers’ data transfer services.

7. Data migration


This is the execution phase where you’ll move data from on-premise to the cloud. It can be done all at once (big bang), incrementally, or via parallel run where both systems run simultaneously for a period.

It’s advisable to migrate non-critical data first to test the process before moving more sensitive or mission-critical data.

8. Validate the migration


After migration, verify the integrity and consistency of your data. Check that data has not been lost, corrupted, or incorrectly transformed during the migration. This can be a simple count of records or a more in-depth comparison of sample data.

9. Transitioning systems


Gradually transition your workflows, applications, and users to use the new cloud-based system. Monitor the system closely to detect and address any issues. Be ready to revert to your old system if necessary.

10. Monitoring and optimization


After successful migration, continuously monitor system performance, data integrity, and cost. Optimize your use of cloud resources to balance performance and cost. Regularly review your security and compliance posture.

Remember, these steps are not strictly sequential and you may need to loop back through earlier steps as circumstances change.

Visualizing the migration process: An ASCII process chart

A diagrammatic blueprint is always beneficial to discuss a topic. So here’s a basic ASCII process chart to depict the various stages of the migration process. Keep in mind that this is a simplified version and the actual process may involve more steps and iterations.

The migration of data is broken down into the following steps:


+------------------+     +------------------------+     +------------------+
| Understand your  | --> | Define Migration       | --> | Choose your      |
| Current State    |     | Strategy               |     | Cloud Provider   |
+------------------+     +------------------------+     +------------------+
                                     |
                                     v
                           +------------------+
                           | Data Modeling &  |
                           | Architecture     |
                           +------------------+
                                     |
                                     v
                           +------------------+
                           | Security &       |
                           | Compliance       |
                           +------------------+
                                     |
                                     v
                           +------------------+
                           | Prepare for      |
                           | Migration        |
                           +------------------+
                                     |
                                     v
                           +------------------+
                           | Data Migration   |
                           +------------------+
                                     |
                                     v
                           +------------------+
                           | Validate the     |
                           | Migration        |
                           +------------------+
                                     |
                                     v
                           +------------------+
                           | Transitioning    |
                           | Systems          |
                           +------------------+
                                     |
                                     v
                           +------------------+
                           | Monitoring &     |
                           | Optimization     |
                           +------------------+

This should help communicate the overall flow to your team. However, it’s important to remember that each of these steps can involve multiple sub-steps and iterations.

Implementing data migration: Impending challenges and how to face them

As you embark on the implementation of your data migration from on-premise to the cloud, it is crucial to be prepared for the challenges that may arise along the way.

In this section, we will delve into the key obstacles you may encounter and the essential tools you need to overcome them. Let’s explore the common hurdles and the tools that will empower you to tackle them effectively.

Here are some of the potential challenges to keep in mind during the cloud migration process:

  1. Data loss and integrity issues
  2. Downtime
  3. Security risks
  4. Cost overruns
  5. Compliance
  6. Skills gap
  7. Vendor lock-in

Now, let us look into each of the above challenges in brief:

1. Data loss and integrity issues


The risk of data loss or corruption is a key concern during migration. It’s essential to back up your data and have recovery plans in place.

2. Downtime


Depending on the approach taken, you may have to experience some downtime during the migration process. Careful planning is needed to minimize this.

3. Security risks


Moving data can expose it to potential security vulnerabilities. Ensure you’ve implemented adequate encryption and access controls.

4. Cost overruns


Cloud services pricing can be complex. Without careful management and monitoring, your cloud expenses can quickly exceed your budget.

5. Compliance


If you’re dealing with sensitive data (like financial data or personal data), you must ensure the migration process. Additionally, the new cloud environment complies with relevant regulations (e.g., GDPR, CCPA, etc.)

6. Skills gap


Your team may require training to effectively use the new cloud technologies and tools.

7. Vendor lock-in


Depending on the cloud services and architectures you use, you might find it challenging to change providers in the future.

Now that we understand the benefits of migrating from on-premise to cloud, let us now examine popular migration tools that can help us do it successfully.

Migration tools are essential for moving your data. Examples include AWS Data Migration Service, Google Cloud’s Transfer Service, and Azure Data Migration Service.

While tooling would depend largely on the cloud provider you choose, but here are some commonly used tools:

1. ETL tools


You may need to transform your data as part of the migration. AWS Glue, Google Cloud Dataflow, and Azure Data Factory are popular examples.

2. Data warehousing tools


Data warehousing tools provide powerful capabilities for managing and analyzing large volumes of data.

Examples include Google BigQuery, AWS Redshift, and Azure Synapse Analytics.

3. Data lake tools


Data lake tools empower organizations to efficiently store, process, and analyze diverse and large-scale data. It enables data-driven decision-making and innovation.

AWS S3, Google Cloud Storage, and Azure Data Lake Storage are common choices.

4. Monitoring tools


Monitoring tools enable organizations to proactively track, analyze, and optimize various aspects of their systems and applications. It ensures optimal performance and reliability.

AWS CloudWatch, Google Stackdriver, and Azure Monitor can help you keep an eye on your systems and manage costs.

5. Security tools


Security tools provide robust protection and defence mechanisms to safeguard systems, networks, and data from potential threats and breaches.

AWS Security Hub, Azure Security Center, and Google Cloud Security Command Center provide a wide range of security monitoring and management features.

How data governance plays a crucial role in cloud migration?

Now, we will see how data governance plays a key role in cloud migration.

Data governance is the overall management of the availability, usability, integrity, and security of the data employed in an enterprise.

As you plan your data migration from on-premises to the cloud, it is essential to address the crucial aspect of data governance. With the shift to the cloud, it becomes paramount to establish a robust data governance solution that ensures data integrity, security, and compliance.

In this section, we will explore the importance of data governance in the context of cloud migration. We will discuss key considerations to effectively manage and protect your data in the cloud environment.

Let’s delve into the world of data governance and discover how it plays a vital role in a successful and secure migration to the cloud.

Here are some points to consider:

  1. Data quality
  2. Metadata management
  3. Data privacy and compliance
  4. Data security
  5. Data cataloging
  6. Master data management

Let us look into each of the above aspects in brief:

1. Data quality


Moving to the cloud is an excellent opportunity to tackle any existing data quality issues. Data governance can help ensure that the data is accurate, consistent, and in the right format.

2. Metadata management


As part of your data governance plan, consider how you will manage metadata. This includes information about data sources, transformations, and where and how the data is used.

3. Data privacy and compliance


Depending on your industry, you may have to comply with certain regulations like GDPR, CCPA, or HIPAA. A good data governance strategy can help ensure you meet these requirements.

4. Data security


Data governance includes defining who has access to what data and implementing the necessary controls.

5. Data cataloging


A data catalog helps your team find the data they need. It can provide context, usage instructions, and other helpful information.

6. Master data management


This involves creating a single source of truth for key data like customer, product, or supplier data.

Rounding it up all together

In the process of migrating data from on-premise to the cloud, a well-defined roadmap and the ability to overcome challenges are essential. Data governance is a crucial component in the cloud migration journey, encompassing aspects such as data quality, metadata management, privacy and compliance, security, data cataloging, and master data management.

By embracing effective data governance practices, you can ensure the availability, integrity, and security of your data in the cloud, setting the stage for a successful and secure migration. With careful planning and the right tools, your organization can harness the power of the cloud and unlock new opportunities for growth and innovation.

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