Data migration used to be a long and complicated ordeal. Data loss was a common, very real concern. However, the evolution of data migration tools has made the process faster, easier, and less risky. Let’s take a closer look at data migration and how this once-complex the journey has changed for the better.
Data migration is a one-time process of transferring internal data from one storage system to another. Projects that require data migration range from upgrading a server to moving to a new data center and from launching a new application to integrating the resources of a newly acquired company.
Data migration can sometime be confused with data replication or data integration, but each process is a different kind of data management. Data replication is the periodic copying of data from a data source on one platform to a destination on another, while data integration combines data from disparate sources in a data warehouse destination or analysis tool.
There are six types of data migration:
The remaining two types of data migration — cloud migration and database migration — merit deeper explanations.
Cloud migration is the fastest-growing type of data migration. It involves moving on-premises data or applications to a cloud environment — common options include public clouds, private clouds, and hybrid clouds, although some organizations also use multi-cloud environments. IT experts predict that the majority of large businesses will be operating in the cloud by 2030.
Database migration is an example of specialized workload migration. Simple database migration might involve moving from one version of a database management system (DBMS) to a newer version. More complex database migrations involve a move where the source DBMS and the target DBMS have different data structures, also known as schema.
Ideally, moving data to a new platform, location, or architecture can be completed with no data loss, minimal manual data manipulation or re-creation, and little-to-no downtime. The ETL process extracting the data, transforming the data, and then loading the data can be especially helpful with complex migrations that involve huge datasets.
There are three phases to every data migration project:
When creating your data migration plan, you can consider either the big bang data migration approach or trickle data migration approach:
Organizations can write their own data migration scripts or use off-the-shelf on-premises or cloud-based tools. Self-scripted data migration is a do-it-yourself, in-house solution that may suit small projects, but it doesn't scale well. On-premises tools work well if all the data storage is contained within a single site. Cloud-based data migration tools may be a better choice for organizations moving data to a cloud-based destination.
IT pros can write software to migrate data, but that process can be taxing, time-consuming, and therefore not cost-efficient. Hand-coding big data integrations may also result in manual integration tasks and re-implementation of machine learning algorithms.
Using data migration software is a better way to go. The software does the heavy lifting, although it’s important that data engineers still understand what data they are migrating, how much will be migrated, and the differences between the source and destination platforms and schemas. In addition to defining the migration strategy and running the migration, they must also test the results and resolve any issues.
Selecting a data migration tool is a key component in the planning process, and should be based on the organization's use case and business requirements.
The number and kind of data sources and destinations is an important consideration. Self-scripting may be able to support any source system and destination, but self-scripting is simply not scalable. It may work for small projects, but coding data extraction scripts for hundreds of sources is inefficient and wastes precious IT resources.
One caveat for on-premises tools is that the supported sources and destinations may vary depending on the operating system on which your tool runs. Most on-premises and cloud-based data migration tools have compatibility with a variety of data sources and as well as popular destinations such as AWS and Microsoft. However, cloud-based SaaS tools also don't have OS limitations, and vendors upgrade them to support new versions of sources and destinations automatically.
Cloud-based data migration tools have little to no downtime due to their highly redundant architectures. Matching that reliability with on-premises tools is a difficult — if not impossible — ask.
Cloud-based migration tools perform exceptionally well. Compute power and storage in the cloud can scale to meet dynamic data migration requirements. On-premises tools cannot automatically scale up and down as needed because they're limited by the hardware on which they run.
Data migration tools may have to meet security and compliance requirements. This may rule out some cloud-based tools, but many are compliant with SOC 2, HIPAA, GDPR, and other governance regulations. Others may offer valuable, related features such as disaster recovery services.
Many factors affect pricing, including the amount of data, number and types of sources and destinations, and service level. No particular type of data migration tool will always be the lowest-cost solution for any given data migration project.
Cloud-based data migration tools typically have pay-as-you-go pricing. For most data migration projects, a cloud solution provides the best pricing — and some cloud services even offer a free tier of service for some organizations. Because some of the pricing models can be a bit confusing, however, it’s important to be sure that you’re comparing apples to apples when it comes to cloud-based solutions.
Planning a data migration or replication? Stitch offers an easy-to-use, cloud-first, ETL tool that can replicate or migrate data from sources to destinations without compromising data quality. Automation makes the job of getting data for analysis faster, easier, and more reliable. Stitch streams all your data directly to your analytics warehouse so that business stakeholders can get the most value from their data analysis and from business intelligence programs that draw on a variety of datasets.