Lessons Learned from Operating an Exabyte Scale Data Lake at Microsoft

Data lakes are the new best thing. Companies want to transform their business by harnessing the power of data in their data lake to get transformational insights and leapfrog the competition. Our team at Microsoft has operated possibly the world’s largest data lake for more than a decade. In this session, attendees will learn about our journey in building and operating a multi-exabyte data lake, what users care about, and what went well and what didn’t go so well. Attendees will also learn about what has changed since we started our journey, and how we’ve adapted as the big data ecosystem has evolved.

Topics Covered

Azure Data Lake Storage

Data Lake Storage

Speakers

Raji Easwaran

Raji Easwaran leads the Product Management team responsible for the strategy, design, development and implementation for Microsoft’s data lake storage platforms and services. Raji and her team are focused on delivering core storage platform services for big data analytics that allow data scientists, data engineers and developers to successfully develop, deploy and manage big data applications on Azure. Prior to working on the product management team that builds analytics storage services, Raji led the teams responsible for capacity management, operations and cost efficiencies for Microsoft’s internal big data platform powering analytics for several Microsoft internal divisions such as Bing, Office and Windows.

Ready to Get Started? Here Are Some Resources to Help

Webinars

Smart Data – Smart Factory with Octotronic and Dremio

Guides

What Is a Data Lakehouse?

The data lakehouse is a new architecture that combines the best parts of data lakes and data warehouses. Learn more about the data lakehouse and its key advantages.

Whitepaper

Simplifying Data Mesh for Self-Service Analytics on an Open Data Lakehouse

The adoption of data mesh as a decentralized data management approach has become popular in recent years, helping teams overcome challenges associated with centralized data architecture.