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Speaker: Matei Zaharia, Original Creator of Apache Spark™ and MLflow; Chief Technologist, Databricks

Matei Zaharia, Original Creator of Apache Spark™ and MLflow and Chief Technologist at Databricks open sourced Unity Catalog live onstage at the Data + AI Summit 2024 in San Francisco.

Speakers: Matei Zaharia, Original Creator of Apache Spark™ and MLflow; Chief Technologist, Databricks Darshana Sivakumar, Staff Product Manager, Databricks

Organizations are looking for ways to securely exchange their data and collaborate with external partners to foster data-driven innovations. In the past, organizations had limited data sharing solutions, relinquishing control over how their sensitive data was shared with partners and little to no visibility into how their data was consumed. This created the risk for potential data misuse and data privacy breaches. Customers who tried using other clean room solutions have told us these solutions are limited and do not meet their needs, as they often require all parties to copy their data into the same platform, do not allow sophisticated analysis beyond basic SQL queries, and have limited visibility or control over their data.

Organizations need an open, flexible, and privacy-safe way to collaborate on data, and Databricks Clean Rooms meets these critical needs.

See a demo of Databricks Clean Rooms, now in Public Preview on AWS + Azure

Speaker: Matei Zaharia, Original Creator of Apache Spark™ and MLflow; Chief Technologist, Databricks

Summary: Data sharing and collaboration are important aspects of the data space. Matei Zaharia explains the evolution of the Databricks data platform to facilitate data sharing and collaboration for customers and their partners.

Delta Sharing allows you to share parts of your table with third parties authorized to view them. Over 16,000 data recipients use Delta Sharing, and 40% are not on Databricks—a testament to the open nature.

Databricks Marketplace has been growing rapidly and now has over 2,000 data listings, making it one of the largest data marketplaces available. New Marketplace partners include T-Mobile, Tableau, Atlassian, Epsilon, Shutterstock and more.

To learn more about Delta Sharing features and the expansion of partner sharing ecosystem, see the recent blog: https://www.databricks.com/blog/whats-new-data-sharing-and-collaboration

Speaker: Matei Zaharia, Original Creator of Apache Spark™ and MLflow; Chief Technologist, Databricks

Speakers: - Alexander Booth, Asst Director of Research & Development, Texas Rangers - Ali Ghodsi, Co-Founder and CEO, Databricks - Bilal Aslam, Sr. Director of Product Management, Databricks - Darshana Sivakumar, Staff Product Manager, Databricks - Hannes Mühleisen, Creator of DuckDB, DuckDB Labs - Matei Zaharia, Chief Technology Officer and Co-Founder, Databricks - Reynold Xin, Chief Architect and Co-Founder, Databricks - Ryan Blue, CEO, Tabular - Tareef Kawaf, President, Posit Software, PBC - Yejin Choi, Sr Research Director Commonsense AI, AI2, University of Washington - Zeashan Pappa, Staff Product Manager, Databricks

About Databricks Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

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Hear from three guests. First, Matei Zaharia (co-founder and Chief Technologist, Databricks) on AI governance and Unity Catalog. Second guest, Scott Starbird (General Counsel, Public Affairs and Strategic Partnerships, Databricks) on Ethics in AI. Third guest, Bryan Saftler (Industry Solutions Marketing Director, Databricks) on industry perspectives and solution accelerators. Hosted by Ari Kaplan (Head of Evangelism, Databricks) and Pearl Ubaru (Sr Technical Marketing Engineer, Databricks)

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0:00 Open 6:08 Ali Ghodsi & Marc Andreessen 32:06 Reynold Xin 48:09 Michael Armbrust 1:00:00 Matei Zaharia & Panel 1:27:10 Hannes Muhleisen 01:37:43 Harrison Chase 01:49:15 Lin Qiao 02:05:03 Jitendra Malik 02:21:15 Arsalan & Eric Schmidt

0:00 Opener 01:18- Ali Ghodsi, Databricks 06:53 - Satya Nadella, Microsoft 15:50 Ali Ghodsi, Databricks 20:40 Larry Feinsmith, JP Morgan Chase 41:09 Ali Ghodsi, Databricks 45:07 Matei Zaharia, Databricks 52:31 Weston Hutchins, Databricks 58:36 Ali Ghodsi, Databricks 1:02:05 Naveen Rao, MosaicML 1:12:15 Patrick Wendell, Databricks 1:27:57 Kasey Uhlenhuth, Databricks 1:39:18 Sai Pradhan Ravuru, Jetblue 01:47 Ali Ghodsi, Databricks 1:49:20 Reynold Xin, Databricks 2:05:07 Ali Ghodsi, Databricks 2:09:26 Matei Zaharia, Databricks 2:17:24 Caryl Yuhas, Databricks 2:24:12 Zaheera Valani, Databricks 2:39:55 Wassym Bensaid, Rivian

Data + AI Summit Keynote talk from Matei Zaharia on Data Governance and Sharing on Lakehouse

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Join the Day 1 keynote to hear from Databricks co-founders - and original creators of Apache Spark and Delta Lake - Ali Ghodsi, Matei Zaharia, and Reynold Xin on how Databricks and the open source community is taking on the biggest challenges in data. The talks will address the latest updates on the Apache Spark and Delta Lake projects, the evolution of data lakehouse architecture, and how companies like Adobe and Amgen are using lakehouse architecture to advance their data goals.

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Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation