Keynote Speaker: Ali Ghodsi
Open source, open standards, multicloud — lakehouse was made for the way the world works today. Hear Databricks co-founder and CEO Ali Ghodsi talk about his vision for the Databricks Lakehouse.
Data Management & Engineering
Speaker: Michael Armbrust
Turning data into a strategic asset is a top priority for many organizations. Organizations have realized that data management and engineering are key to providing timely, reliable and governed data for analytics or machine learning. Hear from Michael Armbrust on how data teams can successfully leverage the Databricks Lakehouse to rapidly turn data into insights with quality and governance.
SQL Analytics and Performance
Speaker: Reynold Xin
Databricks Chief Architect Reynold Xin talks about performance improvements, simplified administration and user experience available in Databricks SQL. Get a first-class SQL development experience backed by the Photon query engine for state of the art performance on all query types.
Speaker: Clemens Mewald
Operationalizing data science and machine learning can be difficult with a miscellany of data sources, ML tools and workflows. The Databricks lakehouse changes that. Discover how you can bring together data engineers, data scientists, and lines of business to collaborate on an open platform, and operationalize the full machine learning lifecycle at scale.
Speaker: David Meyer
After hearing about key capabilities the Databricks Lakehouse Platform offers, from data management and engineering to SQL Analytics and performance, to data science and machine-learning, David Meyer, SVP Products, will share exciting announcements coming to the platform.
Speaker: Kate Hopkins
In this keynote talk, AT&T will share the story of their Lakehouse journey, from the drivers behind their shift to this new paradigm, to lessons learned along the way. From a starting point of a siloed, data warehouse centric architecture that had inherent challenges with scalability, performance and data duplication, AT&T has standardized upon Databricks to serve as an open and unified Lakehouse platform to deliver insights at scale, democratizing data through the rapid deployment of AI and BI use cases across their operations.
Panel: Best Practice for Data Platforms
Unlike companies reliant on legacy technologies, cloud-native companies like Edmunds and Bread Finance have the opportunity to build their internal data platforms in the cloud from the ground up. They also often have strong engineering talent, giving them the opportunity to build using open source technologies or to choose commercial offerings. In this panel, we’ll talk with leading-edge engineers from cloud-native companies about how they’ve made build vs. buy decisions and best practices learned as they built their data platforms on lakehouse technologies.