Data modernization refers to the process of upgrading and transforming data systems, infrastructure, and processes to meet the demands of modern data-driven organizations. It involves the adoption of new technologies and techniques to increase data quality, speed, scalability, and agility. To help organizations navigate this complex process, several data modernization patterns have emerged that provide a framework for modernizing data systems.

In this episode, you’ll learn:

The types of challenges that come with building a data warehouse
How customers can embrace modernization
The challenges an organization may face going on-premises or to the cloud

Some questions we ask:

How does modernization data differ from traditional data warehouses?
What are some of the major challenges that customers face today?
Is the data warehouse dead?

Guest bio

Entrepreneur and International Business Management Executive Jeeva Akr leads the Cloud Scale Analytics go-to-market for Microsoft, growing the global sales of Azure Cloud Scale Analytics offerings, including Azure Synapse, Azure Databricks, Azure Stream Analytics, Azure Data Factory, Microsoft Purview, and more. He leads a direct team of sales strategists, program owners, go-to-market leaders, and partner development leaders, providing thought leadership and managing sales execution of the entire global business.

Resources:

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Demystifying Data Modernization Patterns