What is Data Migration?

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Data Migration Definition

Data migration is the process of moving your data from one location in a distinct format to another location in another format. While seemingly simple, data migration can be a highly complex and orchestrated process involving storage and database applications.

For organizations who are beginning their nomadic data migration journey, the most common data migration journeys typically explore the move from an on-premise system to the cloud. 

Preparing for Data Migration – Common Hurdles to Overcome

Many organizations will decide to undertake a data migration project for various reasons, including: a total system overhaul, database upgrade, new or updated data storage – such as a data warehouse or data lake – or merging and/or acquiring data sources. 

Typically, prior to a data migration project, an organization will face one or more of the following challenges:

  1. Your data infrastructure can’t scale to handle data volumes or velocity.
  2. Your business users and data scientists are complaining about slow or lack of data access.
  3. It takes too long to incorporate new data sources or support changing business requirements.
  4. You can’t insure data security or govern access consistently.
  5. The business has mandated a move to the cloud.

The Top Two Data Migration Challenges

With any change you make in life, risk follows right behind you. Although this shift may sound daunting at first, it doesn’t need to be. For those who don’t make the move with confidence, they may fall victim to the repercussions that are synonymous with the following situations:

  1. Poor preparation – Preparation begins with the ETL process. After all, data governance is what fuels data migration. If your data is hurting from loose definitions, how will you be able to prepare it for the move? In a previous blog post, we explored data transformation and the need to have your data converted from “its previous form into the form it needs to be in so that it can be placed into another database.” You should never assume that all of your data is translated properly post-extraction. 
  2. Lack of Team Support and Foggy Objectives – What is the end goal? Be clear with what you want to accomplish with this migration and what needs to be done in order to get there. Have your team establish ownership over different parts of the process, especially during testing phases. 

Planning for a Data Migration

Once an organization has committed to a data migration project, it is important to plan accordingly and consider all options to determine which method works in the best interest of your organization. 

These methods include:

  1. Application Migration – You are porting business applications from pointing to your existing data infrastructure to another. 
  2. Data Migration – You are seeking to modernize your data infrastructure to handle increasing scale and new data types.
  3. Cloud Migration – You are seeking to migrate your on-premise infrastructure to a public or hybrid cloud.

Strategies for Risk-Free Data Migration

There are four practical steps for a risk-free cloud migration to take to consider to enable a successful cloud data migration:

  1. Establish Data Platform Independence – “Instead of managing data platforms, manage how the company accesses the data, with an emphasis on abstracting the data’s source location.”
  2. Centralize Access to Data – “Provide users with a centralized location to locate, discover, and work with the data of the EDW.”
  3. Transform Data with Business Logic – “Use metadata to guide how fields from different data sources and with different granularities should be combined, to prevent misinterpretation of joined data.” 
  4. Ensure Continuous and Unified Hybrid Cloud Security, Governance, and Compliance Management – “By checking the source database for security policies, intelligent virtualization preserves security and privacy information all the way to the user by tracking the data’s lineage and the user’s identity.”

How AtScale Can Help 

The AtScale Semantic Layer platform can reduce the risks and cost for your data migration efforts by:

  1. Eliminating much of the manual data engineering that makes data migration risky and time consuming.
  2. Delivering a centrally managed security and governance layer that ensures consistent, secure data access.
  3. Making the cost of storing and processing data in the cloud manageable and predictable. 
  4. Eliminating most of the manual performance tuning that’s required when moving to new data platforms 
  5. Allowing you to test your changes without having to commit code with our semantic layer and virtual cubes 

Additional Resources:

The Practical Guide to Using a Semantic Layer for Data & Analytics
Semantic Layer - diagram