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Data Integration Tools and Practices for Data Migrations Involving the Cloud

Webinar Speaker: Philip Russom, Senior Research Director for Data Management

Date: Wednesday, March 18, 2020

Time: 9:00 a.m. PT, 12:00 p.m. ET

Organizations of any size or maturity will already have a data warehouse deployed and in operation. Likewise, they will have data lakes, analytics sandboxes, and other data sets in production. Modernizing an incumbent warehouse or similar data solution regularly involves migrating data from platform to platform, increasingly from on-premises to the cloud. This is because replatforming is a common strategy, whether you will replace the DW’s primary platform or augment it with additional data platforms. Even in an augmentation strategy, “data balancing” is an inevitable migration task as you redistribute data across the new combination of old and new platforms. In a different direction, some data warehouse modernization or replatforming strategies simplify redundant portfolios of databases (or take control of rogue data marts) by consolidating them onto fewer platforms, with cloud data platforms as a preferred target.

In all these data migration scenarios, data integration plays a mission-critical role. After all, data’s journey to the cloud almost always leads through a data integration platform or toolset. As we’ll see in our discussion of migration types, most involve complex joins, multistep merges, and sophisticated transformations of data, which are best designed and executed with a modern and feature-rich data integration tool.

Attendees will learn about:

  • Why data integration is the leading success factor for data migrations, followed by related data management tools and disciplines for quality, modeling, and metadata.
  • General best practices for data migration, including team skills, team management, and project plans for data migration.
  • Specific project details and technology requirements for data migrations that involve clouds.
  • Types of data migrations to cloud, each with varying data integration involvement: Big bang versus phased projects. Migrating whole data architectures versus picking and choosing a few pieces to move. The myth of lift and shift. Re-architecting and transforming data as you migrate it.

Philip Russom, Ph.D.

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