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How Does Data Migration Work?

how does data migration work: orange trucks as a metaphor

 

 

Data migration is now a necessary task for data administrators and other IT professionals. A few data migration examples include:

  • Application migration, in which an entire application is moved from an on-premises environment to the cloud.
  • Cloud migration, which involves moving either data or an entire application and its data to the cloud.
  • Database migrations, which involves moving data from an existing database to a new platform.
  • Storage migration, where an organization moves its backups for disaster recovery or other purposes to a new data storage medium, such as the cloud.

 

But how does data migration work?

 

What does data migration mean & why is it important?

 

Data migration is simply the process of moving data from one place to another. Data migration is becoming increasingly important because the systems we use to run our businesses are generating more and more data. The bottom line is that if we’re going to create all this data, we have to be able to move it around as our application and storage needs change.

 

Organizations might need to migrate data:

  • When combining data assets from separate organizations after a merger or acquisition
  • After implementing a new application.
  • When moving to a new database or data warehouse
  • After adopting a cloud-based data platform
  • Moving from an on-premises data center to the cloud
  • After adding data-intensive applications such as a database, data warehouse, or data lake.

 

What is the data migration process?

 

How does data migration work? Your data migration plans might include the following steps:

 

Analysis Analyze the data that you’ll be migrating. Factors to consider are the format of the data in the source system versus the format of the data in the target system. Compatibility issues can cause problems later on. Another issue to consider is whether your data migration project will involve data integration, which entails combining data from multiple data sources into a single target system.

 

Backups To minimize the chances of data loss during the data migration process, perform backups of all the data that will be migrated. Complete, up-to-date backups will be invaluable in the event of a disaster recovery situation.

 

Initial testing Using a copy of your production environment, test your migration code or application. Validate the data on the new source system.

 

Extraction Extract the data from the source system using a tool such as a data loader or ETL application.

 

Transformation If necessary, transform the data to the format needed on the target system.

 

Loading Load the data into the new system using the tool you’ve selected.

 

Verification and testing Confirm that your data transfer has been successful by testing the data on the new system. You’ll want to make sure that data is complete and accessible.

 

Communication Communicate with the data owners and data owners throughout the process.

Retirement If your migration plans involve decommissioning an application or platform, one of the final data migration stages is to retire the old system. It’s best to wait for a few weeks or even a couple of months to make sure that there are no issues with the new system. Your data migration process is now complete.

Learn more about how to have a successful data migration.

 

How long does data migration take?

 

The timeframe for a data migration can vary widely based on multiple factors.

 

The size of your data migration project. There’s a big difference between migrating one database to the cloud and migrating nine different applications plus their data to multiple target systems. When estimating your timelines, consider the quantity of data you will be moving.

 

The type of migration you are planning to do. Migrating all of your data at the same time, also known as the “Big Bang” strategy, involves completing your data migration within a limited timeframe. The biggest benefit to this approach is speed. However, the trade-off is that this approach almost always requires system downtime. Going with a Big Bang migration completes the full transfer within a limited time window. There is some downtime during data processing and movement, but the project is completed quickly.

Trickle migration involves completing the project in phases, including running source and target systems in parallel. Migrating data incrementally will take longer, but it can usually be performed without having to shut down key systems. Trickle migration is more complex than Big Bang and takes longer but has less downtime and more testing opportunities. It’s important to pick the data migration strategy that’s right for your organization and your users.

Whether or not you will write your own code. Do you plan to write your own code or use an existing data migration application? If you plan to do your own coding, you will need to include additional development time in your plan.

The amount of data transformation needed. If your migration is a simple transfer of data from one location to another, it will take less time than if you need to transform the data into a different format while you’re moving it.

 The complexity of the data that’s being moved. Unfortunately, it’s rarely as simple as moving your data from source system X to target system Y. You’ll need to analyze the data in the source system to determine its complexity and evaluate how that will impact the move.

 

How to perform data migration & create your data migration plan

 

One of the most important data migration stages is actually the planning stage. Spending time to carefully create your data migration plan is one of the keys to a successful project. The more time you spend planning, the greater your chances of success. To create a data migration plan, consider the following steps.

 

  • Take a data inventory. Take a careful inventory of all of the data that needs to be migrated, identifying where the data is located, what format it’s in, and what format it will need to be in when it reaches the target system. You will be extracting the data, potentially transforming it and then loading it into the destination system. If it’s a simple move, without transformation, a data loader may be the right tool. If data transformation is required, you may need a more robust ETL

 

  • Identify data owners and data users. After you have identified where the data is located, the next step is to figure out who is responsible for managing the systems that contain the data as well as who uses the data. You’ll need to work with the data owners to gain access to the data systems. Data owners can help you figure out how to how to perform data migration with minimal impact to the data’s users and to critical business processes.

 

  • Create a team. After you have identified data owners, create a team to work together on the data migration project. Look at data migration examples together. You’ll need to work with the data owners to gain access to the data in the source systems, and to validate data quality after it has migrated to the target system.

 

  • Plan to back up your data before you start. Before you begin the migration project, make sure that your data has been backed up recently and confirm that the backups are complete. With complete data backups in place, you’ll be positioned to recover quickly in case of an issue during migration.

 

Hopefully that answers the question, How does data migration work? and gives you an idea of how to get started.

Want to learn more about data migration?

 

Given the increasing volume and complexity of data, and the speed and scale needed to handle it, the only place you can compete effectively—and cost-effectively—is in the cloud. Matillion provides a complete data integration and transformation solution that is purpose-built for the cloud and cloud data warehouses.

Only Matillion is purpose-built for Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Delta Lake on Databricks, enabling businesses to achieve new levels of simplicity, speed, scale, and savings. Trusted by companies of all sizes to meet their data integration and transformation needs, Matillion products are highly rated across the AWS, GCP, and Microsoft Azure Marketplaces.

Matillion Data Loader is a free SaaS-based data integration tool that seamlessly loads valuable business data into your cloud data warehouse. With a code-free, wizard-based pipeline builder to common data sources like Salesforce, Google Analytics, and more, Matillion Data Loader can help make your first data migration (and every one after) quick and easy. It’s also free. Sign up today to try Matillion Data Loader and kickstart your data migration project.

Matillion ETL software is cloud-native, purpose-built to support leading cloud data warehouse environments, including Snowflake, Amazon Redshift, Google BigQuery, Microsoft Azure Synapse and Delta Lake on Databricks. From extracting and loading your data to performing powerful data transformations, Matillion cloud ETL solutions offer cloud-native architecture and performance compared to legacy ETL tools. With no hardware or software requirements, Matillion leverages the performance and scale of the cloud, making complex data transformation fast, secure, and cost efficient.

See the power of Matillion for yourself. Request a demo to learn more about how you can unlock the potential of your data with Matillion’s cloud-based approach to data transformation.

 

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