Automating Data Transformations

The modern data stack has evolved rapidly in the past decade. Yet, as enterprises migrate vast amounts of data from on-premises platforms to the cloud, data teams continue to face limitations executing data transformation at scale. Data transformation is an integral part of the analytics workflow--but it's also the most time-consuming, expensive, and error-prone part of the process.

In this report, Satish Jayanthi and Armon Petrossian examine key concepts that will enable you to automate data transformation at scale. IT decision makers, CTOs, and data team leaders will explore ways to democratize data transformation by shifting from activity-oriented to outcome-oriented teams--from manufacturing-line assembly to an approach that lets even junior analysts implement data with only a brief code review.

With this insightful report, you will:

• Learn how successful data systems rely on simplicity, flexibility, user-friendliness, and a metadata-first approach
• Adopt a product-first mindset (data as a product, or DaaP) for developing data resources that focus on discoverability, understanding, trust, and exploration
• Build a transformation platform that delivers the most value, using a column-first approach
• Use data architecture as a service (DAaaS) to help teams build and maintain their own data infrastructure as they work collaboratively

About the authors:

Armon Petrossian is CEO and cofounder of Coalesce. Previously, he was part of the founding team at WhereScape in North America, where he served as national sales manager for almost a decade.

Satish Jayanthi is CTO and cofounder of Coalesce. Prior to that, he was senior solutions architect at WhereScape, where he met his cofounder Armon.