Sessions

Keynote

Keynotes: Lakehouse Data Architecture, Data Engineering, and Analytics

Ali Ghodsi, Databricks • Bill Inmon, Father of Data Warehouse • Michael Armbrust, Databricks • Rohan Dhupelia, Atlassian • Matei Zaharia, Databricks • Matt Garman, AWS • Reynold Xin, Databricks • Rohan Kumar, Microsoft • Malala Yousafzai

Join the Wednesday morning keynote to hear from Databricks co-founders and original creators of popular projects Apache Spark, Delta Lake, and MLflow on how the open source community is tackling the biggest challenges in data. Stay tuned for them to reveal some of the latest innovations in data engineering and data analytics to simplify and scale your work.

Ali Ghodsi
Databricks

Bill Inmon
Father of Data Warehouse

Michael Armbrust
Databricks

Rohan Dhupelia
Atlassian

Matei Zaharia
Databricks

Matt Garman
AWS

Reynold Xin
Databricks

Rohan Kumar
Microsoft

Databricks Platform

Databricks Lakehouse Overview Training

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Databricks Platform

Lakehouse with Delta Lake Deep Dive Training

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Industry Programming

Comms, Media, and Entertainment Industry Forum

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Industry Programming

Manufacturing and Distribution Industry Forum

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Industry Programming

Government Industry Forum

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Data Engineering and Data Architecture

Building Data Quality pipelines with Apache Spark and Delta Lake

Darren Fuller, Elastacloud Ltd • Sandy May, Elastacloud

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Data Science, Deep Learning and ML

Creating an 86,000 Hour Speech Dataset with Apache Spark and TPUs

Daniel Galvez, MLCommons

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Apache Spark Internals and Best Practices

Data Distribution and Ordering for Efficient Data Source V2

Anton Okolnychyi, Apple

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AI Use Cases

Scaling AI At H&M

Bjorn Hertzberg, H&M

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SQL Analytics and BI

Becoming a Data Driven Organization with Modern Lakehouse

Vini Jaiswal, Databricks

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Industry and Business Use Cases

RWE & Patient Analytics Leveraging Databricks – A Use Case

Harini Gopalakrishnan, Sanofi • Martin Longpre, Sanofi

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Data Science, Deep Learning and ML

Catch Me If You Can: Keeping Up With ML Models in Production

Shreya Shankar

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Data Engineering and Data Architecture

Customer Experience at Disney+ Through Data Perspective

Rekha Bachwani, Disney+ • Martin Zapletal, Disney+

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Open Source Data and ML Tools

KFServing, Model Monitoring with Apache Spark and a Feature Store

Jim Dowling, Logical Clocks AB • Javier de la Rúa Martínez, Logical Clocks

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Data Science, Deep Learning and ML

Building Data Science into Organizations: Field Experience

Joseph Bradley, Databricks • Chris Robison, Databricks

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Data Science, Deep Learning and ML

Scaling Online ML Predictions At DoorDash

Hien Luu, DoorDash • Arbaz Khan, DoorDash

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Data Engineering and Data Architecture

Delta Lake Streaming: Under the Hood

Shasidhar Eranti, Databricks

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AI Use Cases

Detecting Anomalous Behavior with Surveillance​ Analytics​

Vinamre Dhar, Kushagramati Analytics • Rishan Sanjay, Kushagramati Analytics

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Data Engineering and Data Architecture

Introducing Delta Live Tables: Make Reliable ETL Easy on Delta Lake

Awez Syed, Databricks • Bryce Bartmann, Shell

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Industry and Business Use Cases

Empower Splunk and other SIEMs with the Databricks Lakehouse for Cybersecurity

George Webster, HSBC • Monzy Merza, Databricks, and more

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Data Engineering and Data Architecture

Databricks on Databricks: AMA with Data Engineering SMEs

Jason Pohl, Databricks • Mike Hamilton, Databricks, and more

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Eisai’s Secret to Data Access Control in Databricks Lakehouse with SQL Analytics

Matt Vogt, Immuta • Sean Jacobs, Eisai

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Low-Code Apache Spark

Raj Bains, Prophecy.io • Maciej Szpakowski, Prophecy.io

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Engagement

AMA about Lakehouse!

Data Science, Deep Learning and ML

Graph-Powered Machine Learning

Jorg Schad, ArangoDB

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Data and ML Research

Magnet Shuffle Service: Push-based Shuffle at LinkedIn

Chandni Singh, LinkedIn • Min Shen, LinkedIn

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Databricks Production Use Cases

DevOps for Databricks

Anna Wykes, Advancing Analytics

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Data Engineering and Data Architecture

Data Security at Scale through Spark and Parquet Encryption

Gidon Gershinsky, Apple • Tim Perelmutov, Apple

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Open Source Data and ML Tools

Funnel Analysis with Apache Spark and Druid

Etti Gur, Nielsen • Itai Yaffe, Imply

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Industry and Business Use Cases

Keeping Identity Graphs In Sync With Apache Spark

Ruben Berenguel, Hybrid Theory

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Data Engineering and Data Architecture

Automating Data Quality Processes at Reckitt

Richard Chadwick • Karol Sawicz, Reckitt

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Data Science, Deep Learning and ML

Creating Reusable Geospatial Pipelines

Dan Corbiani, Pacific Northwest National Lab

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Data Science, Deep Learning and ML

Experimentation to Industrialization: Implementing MLOps

Al McEwan, Thorogood Associates • Deb Lee, Thorogood Associates

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Apache Spark Internals and Best Practices

Monitor Apache Spark 3 on Kubernetes using Metrics and Plugins

Luca Canali, CERN

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AI Use Cases

BOTS TESTING BOTS: From manual to automated testing for conversational AI

Christoph Börner, Botium GmbH

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Industry and Business Use Cases

SCOR’s NonLife Risk Modelling (NORMA) on Databricks Apache Spark

Sahand Razzaghi, SCOR • Luca Valentino, SCOR

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The Shift to Data Mesh

Dael Williamson, Avanade, Inc. • Dave Baxter, Avanade, Inc.

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Azure Databricks Security Best Practices

Premal Shah, Microsoft

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Data and ML Research

AMA about Delta Lake!

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Keynote

Wednesday Afternoon Keynotes

Michael Lewis • Charity Dean, The Public Health Company • Sol Rashidi, Estée Lauder • Shafi Goldwasser, MIT and UC Berkeley

Join the afternoon keynotes, where we bring together the community to learn from thought leaders in Data + AI.  In conversation with Michael Lewis and Charity Dean: Michael Lewis, NYT best-selling author and Charity Dean, a real life“character” of his upcoming new book, The Premonition, will give our audience insight on how a group of scientific misfits used technology and data to anticipate the pandemic and how to make sense of it quickly.  Sol Rashidi will be speaking on becoming a CJO.  Sol is currently the Chief Analytics Officer at Estee Lauder and has held executive roles on data strategy at Merck, Sony, Royal Caribbean, EY, and IBM.  She's joining us to share insights on how practitioners in the Data + AI community should adopt a product-centric mindset.

Michael Lewis

Charity Dean
The Public Health Company

Sol Rashidi
Estée Lauder

Shafi Goldwasser
MIT and UC Berkeley

Industry and Business Use Cases

Commercializing Alternative Data

Jay Bhankharia, S&P Global • Srinivasa Podugu, S&P Global

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Open Source Data and ML Tools

Image Processing on Delta Lake

Jason Robey, Databricks • Rohit Gopal, Databricks

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Data Science, Deep Learning and ML

Productionizing Machine Learning in Our Health and Wellness Marketplace

Genna Gliner, Mindbody • Brandon Davis, Mindbody

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Data Science, Deep Learning and ML

How We Scaled Bert To Serve 1+ Billion Daily Requests on CPU

Quoc Le, Roblox • Kip Kaehler, Roblox

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Apache Spark Internals and Best Practices

Enabling Vectorized Engine in Apache Spark

Kazuaki Ishizaki, IBM

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AI Use Cases

Advanced Natural Language Processing with Apache Spark NLP

David Talby, John Snow Labs

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Data Science, Deep Learning and ML

CI/CD in MLOps – Implementing a Framework for Self-Service Everything

Cara Phillips, Artis Consulting • Wesly Clark, J.B. Hunt

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Data Engineering and Data Architecture

Code Once Use Often with Declarative Data Pipelines

Anthony Awuley, Flashfood • Carter Kilgour, Flashfood

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SQL Analytics and BI

Northwestern Mutual Journey – Transform BI Space to Cloud

Madhu Kotian, Northwestern Mutual • Keyuri Shah, Northwestern Mutual

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Data Science, Deep Learning and ML

Auto-Train a Time-Series Forecast Model With AML + ADB

Moritz Steller, Microsoft

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Databricks Production Use Cases

Bring Your Own Container: Using Docker Images In Production

Harin Sanghirun, Condé Nast • Max Cantor, Condé Nast

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Data Engineering and Data Architecture

Building End-to-End Delta Pipelines on GCP

Himanish Kushary, Databricks • Molly Nagamuthu, Databricks

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AI Use Cases

FrugalML: Using ML APIs More Accurately and Cheaply

Lingjiao Chen, Stanford University

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Data Engineering and Data Architecture

Intro to Delta Lake

Himanshu Raj, Databricks • Brenner Heintz, Databricks, and more

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Open Source Data and ML Tools

Delta Sharing AMA—with Matei Zaharia and Databricks’ Engineers

Matei Zaharia, Databricks • Todd Greenstein, Databricks, and more

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Creating a Lakehouse on AWS

Denis Dubeau, Databricks • Igor Alekseev, AWS

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SQL Analytics and BI

Why Lakehouse Architecture Now?

Justin Borgman, Starburst • Kamil Bajda-Pawlikowski, Starburst, and more

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Azure Databricks Security Best Practices

Premal Shah, Microsoft

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Data Science, Deep Learning and ML

A Vision for the Future of ML Frameworks

Soumith Chintala, Facebook AI

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Data and ML Research

Ask Me Anything about Photon/SQLA

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Databricks Production Use Cases

Massive Data Processing in Adobe Using Delta Lake

Yeshwanth Vijayakumar, Adobe, Inc.

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SQL Analytics and BI

Accelerate Data Science Initiatives: Databricks & Privacera

Don Bosco Durai, Privacera

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Data Science, Deep Learning and ML

The Critical Missing Component in the Production ML Stack

Alessya Visnjic, WhyLabs, Inc.

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Data Science, Deep Learning and ML

PandasUDFs: One Weird Trick to Scaled Ensembles

Paul Anzel, H-E-B

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Data and ML Research

Predicting Optimal Parallelism for Data Analytics

Rathijit Sen, Microsoft • Vishal Rohra, Microsoft

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Industry and Business Use Cases

Improving Power Grid Reliability Using IoT Analytics

Jim Brown, Neudesic • Minjie Yu, DTE Energy

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Data Engineering and Data Architecture

Modern Config Driven ELT Framework for Building a Data Lake

Fred Kimball, Northwestern Mutual • Josh Reilly, Northwestern Mutual

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Data Engineering and Data Architecture

Empowering Zillow’s Developers with Self-Service ETL

Derek Gorthy, Zillow • Yuan Feng, Zillow

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Data Science, Deep Learning and ML

Tensors Are All You Need: Faster Inference with Hummingbird

Karla Saur, Microsoft • Matteo Interlandi, Microsoft

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Industry and Business Use Cases

AI Modernization at AT&T and the Application to Fraud with Databricks

Mark Austin, AT&T Chief Data Office • Prince Paulraj, AT&T Chief Data Office

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SQL Analytics and BI

Databricks SQL Analytics Deep Dive for the Data Analyst

Doug Bateman, Databricks • Kate Sullivan, Databricks

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Decoupling Your Legacy On-Premise Data into Operationalized Lakehouses

Jordan Martz, Qlik • Ted Orme, Qlik

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Accelerate Your Data Engineering Pipeline Development on Databricks and Govern Your Delta Lakehouse

Rodrigo Sanchez Bredee, Informatica

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Data Engineering and Data Architecture, SQL Analytics and BI

You Can Do It in SQL

Drew Banin, Fishtown Analytics

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Industry and Business Use Cases

Play Head Time Analysis On OTT Video At Scale

Adam Liu, Conviva • Biplab Chattopadhyay, Conviva

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Data Science, Deep Learning and ML

FlorenceAI: Reinventing Data Science at Humana

David Mack, Humana, Inc.

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Data Science, Deep Learning and ML

Model Monitoring at Scale with Apache Spark and Verta

Manasi Vartak, Verta Inc

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Data Science, Deep Learning and ML

Productionalizing Machine Learning Solutions with Effective Tracking, Monitoring, and Management

Sumanth Venkatasubbaiah, Intuit • Pankaj Rastogi, Intuit

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Data Engineering and Data Architecture

Building Source of Truth Place Data at Scale

Nan Zhu, SafeGraph

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Open Source Data and ML Tools

Koalas: How Well Does Koalas Work?

Takuya Ueshin, Databricks • Xinrong Meng, Databricks

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Data Science, Deep Learning and ML

Object Detection with Transformers

Liam Li, Determined AI

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AI Use Cases

Accelerate Analytics On Databricks

Jeff King, Microsoft • Paul Scott-Murphy, WANdisco

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Data Engineering and Data Architecture

How Adobe uses Structured Streaming at Scale

Yeshwanth Vijayakumar, Adobe, Inc.

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AI Use Cases

Drug Repurposing using Deep Learning on Knowledge Graphs

Alexander Thomas, Wisecube AI • Vishnu Vettrivel, Wisecube AI

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Data and ML Research

Extending Machine Learning Algorithms with PySpark

Karen Feng, Databricks • Kiavash Kianfar, Databricks

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SQL Analytics and BI

Delivering Insights from 20M+ Smart Homes with 500M+ Devices

Sameer Vaidya, Plume Design Inc. • Raghav Karnam, Plume Design Inc.

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Turbocharge Spark with Samsung SmartSSD® CSDs powered by Xilinx

Seong Kim, Xilinx • Steve Tuohy, Bigstream

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SQL Analytics and BI

Radical Speed for SQL Queries on Databricks: Photon Under the Hood

Greg Rahn, Databricks • Alex Behm, Databricks

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Advancing GPU Analytics with RAPIDS Accelerator for Apache Spark and Alluxio

Adit Madan, Alluxio

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Deliver Consumer-Grade Analytics For Your Lakehouse

Lisa Aguilar, ThoughtSpot • Sean Zinsmeister, ThoughtSpot

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Databricks Production Use Cases

Scaling and Modernizing Data Platform with Databricks

Richa Singhal, Atlassian • Esha Shah, Atlassian

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AI Use Cases

AI Data Acquisition and Governance: Considerations for Success

Kirsten Gokay, Appen

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Data Science, Deep Learning and ML

A Collaborative Data Science Development Workflow

Nicholas Hale, Trillion Technology Solutions

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Open Source Data and ML Tools

Large Scale Geospatial Indexing and Analysis on Apache Spark

Felix Cheung, SafeGraph

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Data Science, Deep Learning and ML

Advanced Model Comparison and Automated Deployment Using ML

Charu Kalra, T-Mobile • Connor McCambridge, T-Mobile

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Industry and Business Use Cases

AI-Driven Personalized Email Marketing

Xi Liang, Endeavour Drinks

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Data Engineering and Data Architecture

Automated Metadata Management in Data Lake – A CI/CD Driven Approach

Josh Reilly, Northwestern Mutual • Keyuri Shah, Northwestern Mutual

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Data Science, Deep Learning and ML

Wizard Driven AI Anomaly Detection with Databricks in Azure

Naomi Kaduwela, Kavi Global • Rajesh Inbasekaran, Kavi Global

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Data Science, Deep Learning and ML

Analytics-Enabled Experiences: The New Secret Weapon

Jorge Lozano, Steelcase • Kevin Tian, Steelcase

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Databricks Production Use Cases

Managing Millions of Tests Using Databricks

Yin Huai, Databricks

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Data Engineering and Data Architecture

A High Performance Mutable Engagement Activity Delta Lake

Heng Zhang, Salesforce • Zhidong Ke, Salesforce

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AI Use Cases

Offer Recommendation System with Apache Spark at Burger King

Kai Huang, Intel Corporation • Luyang Wang, Burger King Corporation

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Productionizing Unstructured Data for AI and Analytics

Nick Lee, Labelbox • Chris Amata, Labelbox

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Data Engineering and Data Architecture

Gain 3 Benefits with Delta Sharing

Razi Sharir, Tableau • Blair Hutchinson, Tableau

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Industry Programming

Financial Services Industry Forum

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Keynote

Keynotes: Data Science and Machine Learning

Ali Ghodsi, Databricks • Reynold Xin, Databricks • Brooke Wenig, Databricks • Clemens Mewald, Databricks • Kasey Uhlenhuth, Databricks • Matei Zaharia, Databricks • Patrick Baginski, McDonald’s • Abhi Bhatt, McDonald’s • Rajat Monga, TensorFlow • Bill Nye

The pursuit of AI is one of the biggest priorities in data today. The Thursday morning keynote will be led by Databricks Cofounder and CEO Ali Ghodsi and cover advances in data science, machine learning, MLOps and more in both open source and the Databricks Lakehouse Platform. We’ll also be joined by data leaders from McDonalds and Microsoft, as well as the legendary Bill Nye, a scientist, engineer, comedian and author.

Ali Ghodsi
Databricks

Reynold Xin
Databricks

Brooke Wenig
Databricks

Clemens Mewald
Databricks

Kasey Uhlenhuth
Databricks

Matei Zaharia
Databricks

Patrick Baginski
McDonald’s

Abhi Bhatt
McDonald’s

Open Source Data and ML Tools

Semantic Image Logging Using Approximate Statistics & MLflow

Leandro Almeida, Whylabs

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Data Science, Deep Learning and ML

Consolidating MLOps at One of Europe’s Biggest Airports

Floris Hoogenboom, Royal Schiphol Group • Sebastiaan Grasdijk, Royal Schiphol Group

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Apache Spark Internals and Best Practices

Choose Your Weapon: Comparing Spark on FPGAs vs GPUs

Roop Ganguly, Bigstream Solutions Inc

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Data Science, Deep Learning and ML

Productionzing ML Model Using MLflow Model Serving

Nitin Raj Soundararajan, Cognizant Worldwide Limited • Nagaraj Sengodan, HCL Technologies

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Open Source Data and ML Tools

Role of Data Accessibility During Pandemic

Vini Jaiswal, Databricks • Isaac Lee, Carnegie Mellon University

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SQL Analytics and BI

Getting Started with Databricks SQL Analytics

Simon Whiteley, Advancing Analytics

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Data Science, Deep Learning and ML

Bootstrapping of PySpark Models for Factorial A/B Tests

Ondrej Havlicek, DataSentics

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Apache Spark Internals and Best Practices

Structured Streaming Use-Cases at Apple

Kristine Guo, Apple • Liang-Chi Hsieh, Apple

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Data Science, Deep Learning and ML

Anomaly Detection at Scale!

Opher Dubrovsky, Nielsen • Max Peres, Nielsen

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Data Engineering and Data Architecture

Bridging the Completeness of Big Data on Databricks

Chao Yang, Verisk • Yanyan Wu, Verisk

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Industry and Business Use Cases

Modelling Customer Lifetime Revenue for Subscription Business

Rajkumar Kaliyaperumal, Conde Nast • Leeladhar Nagineni, Condé Nast

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Open Source Data and ML Tools

YOLO with Data-Driven Software

Brooke Wenig, Databricks • Tim Hunter, ABN AMRO

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AI Use Cases

From Chatbots to Augmented Conversational Assistants

Frederic Jacquet, Talan • Lao-dja Songdou Tchala, Talan

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Databricks Platform

Databricks Lakehouse Platform Governance and Security Fundamentals

Abhinav Garg, Databricks • Tianyi Huang, Databricks

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Data Science, Deep Learning and ML

Learn to Use Databricks for Data Science

Sean Owen, Databricks • Austin Ford, Databricks

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Low-Code Apache Airflow

Raj Bains, Prophecy.io • Maciej Szpakowski, Prophecy.io

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Data Science, Deep Learning and ML

What’s New with Databricks Machine Learning

Clemens Mewald, Databricks • Kasey Uhlenhuth, Databricks

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Industry Programming

Health and Life Sciences Industry Forum

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Location Intelligence Unlocks Insights Enabling Data-driven Decisions with Databricks & Precisely

Tim McKenzie, Precisely

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Industry Programming

Retail and CPG Industry Forum

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Engagement

Women In Unified Data Analytics [A conversation about Allyship] In collaboration with WIBD

Maggie Chu, Databricks • Terell Sterling, Go Paladin, and more

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Data Science, Deep Learning and ML

Drifting Away: Testing ML Models in Production

Chengyin Eng, Databricks • Niall Turbitt, Databricks

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Data Science, Deep Learning and ML

Building A Product Assortment Recommendation Engine

Ethan Dubois, Anheuser Busch • Justin Morse, Anheuser Busch

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Open Source Data and ML Tools

Unified MLOps: Feature Stores & Model Deployment

Jack Ploshnick, Splice Machine • Monte Zweben, Splice Machine

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Data Engineering and Data Architecture

ChakraView – A 360° Approach to Data Quality

Keerthika Thiyagarajan, Flipkart • Shankar Manian, Recko, Inc.

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Apache Spark Internals and Best Practices

Portable UDFs: Write Once, Run Anywhere

Tejas Patil, Facebook

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Databricks Production Use Cases

Empowering Real Time Patient Care Through Spark Streaming

Arnav Chaudhary, Takeda

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Data Science, Deep Learning and ML

How Machine Learning and AI Can Support the Fight Against COVID-19

Dmitry Soshnikov, Microsoft • Francesca Lazzeri, Microsoft

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Data Engineering and Data Architecture

Accelerating Data Ingestion with Databricks Autoloader

Simon Whiteley, Advancing Analytics

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Apache Spark Internals and Best Practices

The Rise of ZStandard: Apache Spark/Parquet/ORC/Avro

Dongjoon Hyun, Apple • Pang Wu, Apple

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SQL Analytics and BI

Considerations for Data Access in the Lakehouse

Zachary Friedman, Immuta

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AI Use Cases

Conversational AI with Transformer Models

Rajesh Shreedhar Bhat, Walmart Global Tech India • Dinesh Ladi, Walmart Global Tech India

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How Databricks Leverages the Lakehouse and Fivetran for Marketing Analytics

Chris Klaczynski, Databricks • Myles McDonald, Fivetran

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Accelerate Spark-Based Analytics by Minimizing the Barriers of Large, Complex Hadoop Migration

Timur Mehmedbasic, Avanade, Inc. • Tony Velcich, WANdisco

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Azure Databricks AMA with Yatharth Gupta and David Meyer

Yatharth Gupta, Microsoft • David Meyer

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Open Source Data and ML Tools

Speed up UDFs with GPUs using the RAPIDS Accelerator

Jason Lowe, NVIDIA

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Apache Spark Internals and Best Practices

Tale of Scaling Zeus to Petabytes of Shuffle Data @Uber

Mayank Bansal, Uber Inc

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Open Source Data and ML Tools

Growing the Delta Ecosystem to Rust and Python with Delta-RS

R Tyler Croy, Scribd

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Data Science, Deep Learning and ML

Credit Card Fraud Detection Using ML In Databricks

Badrish Davay, Capital One • Neil Allen, Capital One

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Apache Spark Internals and Best Practices

Modernize Your Analytics Workloads for Apache Spark 3.0 and Beyond

William Benton, NVIDIA

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Data Science, Deep Learning and ML

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Raghu Ganti, IBM Research

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Data Science, Deep Learning and ML

Importance of ML Reproducibility & Applications with MLfLow

Gray Gwizdz, Databricks • Mary Grace Moesta, Databricks

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Data Engineering and Data Architecture

Building the Artificially Intelligent Enterprise

Mike Ferguson, Intelligent Business Strategies

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Data Engineering and Data Architecture

How Databricks Leverages Auto Loader to Ingest Millions of Files an Hour

Pranav Anand, Databricks

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Data Science, Deep Learning and ML

Databricks Machine Learning: AMA with Product Team

Clemens Mewald, Databricks • Kasey Uhlenhuth, Databricks

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AI @ Scale: Industrialized ML with Databricks

Nathan Buesgens, Accenture • Atish Ray, Accenture

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Data Brew Vidcast: Data Ethics

Diana Pfeil, Honey

Keynote

Thursday Afternoon Keynotes

Dr. Moogega Cooper, NASA-JPL • Adam Steltzner, USC & NASA-JPL • Manuela Veloso, J.P. Morgan • DJ Patil

Join the afternoon keynotes, where we bring together the community to learn from thought leaders in science, data + AI.  We’ll hear from leading scientists, engineers, executives, and academics on the technical and human challenges of planetary exploration, delivering precision medicine, public health, criminal justice, and financial markets.

Dr. Moogega Cooper
NASA-JPL

Adam Steltzner
USC & NASA-JPL

Manuela Veloso
J.P. Morgan

DJ Patil

Apache Spark Internals and Best Practices

AMA about Spark/Koalas

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Open Source Data and ML Tools

Ask Me Anything about ML/MLflow

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Open Source Data and ML Tools

Data Discovery at Databricks with Amundsen

Tao Feng, Databricks • Tianru Zhou, Databricks

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Data Engineering and Data Architecture

Entity Resolution Using Patient Records at CMMI

Brett Luskin, NewWave

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Data Science, Deep Learning and ML

MLflow Model Serving

Andre Mesarovic, Databricks

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Data Science, Deep Learning and ML

Databricks: A Tool That Empowers You To Do More With Data

Francois Callewaert, Databricks

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SQL Analytics and BI

Building Lakehouses on Delta Lake with SQL Analytics Primer

Franco Patano, Databricks

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Data Engineering and Data Architecture

Architect’s Open-Source Guide for a Data Mesh Architecture

Lena Hall, Microsoft

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Data Science, Deep Learning and ML

Accelerate Your ML Pipeline with AutoML and MLflow

Elena Boiarskaia, H2O.ai • Eric Gudgion, H2O.ai

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Data Engineering and Data Architecture

Democratizing Data Quality Through a Centralized Platform

Yuliana Havryshchuk, Zillow • Smit Shah, Zillow

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Data Science, Deep Learning and ML

Video Analytics At Scale: DL, CV, ML On Databricks Platform

Claudiu Barbura, Blueprint Technologies

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Data Engineering and Data Architecture

Migrating ETL Workflow to Apache Spark at Scale in Pinterest

Daniel Dai, Pinterest • Zirui Li, Pinterest

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Apache Spark Internals and Best Practices

Project Zen: Making Data Science Easier in PySpark

Hyukjin Kwon, Databricks • Haejoon Lee, Databricks

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Data Science, Deep Learning and ML

How to use Apache TVM to optimize your ML models

Sameer Farooqui, OctoML

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Apache Spark Internals and Best Practices

Best Practices for Enabling Speculative Execution on Large Scale Platforms

Venkata Krishnan Sowrirajan, LinkedIn • Ron Hu, LinkedIn

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AI Use Cases

Jeeves Grows Up: An AI Chatbot for Performance and Quality

Shivnath Babu, Unravel Data Systems/Duke University

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Databricks Platform

Managing a Multi-Cloud Data and Analytics Platform

Sumit Mehrotra, Databricks • Carleton Miyamoto, Databricks

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Data Science, Deep Learning and ML

Follow-Along Training for Data Science and Machine Learning

Sean Owen, Databricks • Joseph Bradley, Databricks

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Data Science, Deep Learning and ML

Learn to Use Databricks for the Full ML Lifecycle

Rafi Kurlansik, Databricks

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Apache Spark Internals and Best Practices

Efficient Distributed Hyperparameter Tuning with Apache Spark

Viswesh Periyasamy, Databricks

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Data Science, Deep Learning and ML

Machine Learning CI/CD for Email Attack Detection

Jeshua Bratman, Abnormal Security • Justin Young, Abnormal Security

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Data Engineering and Data Architecture

Modularized ETL Writing with Apache Spark

Neelesh Salian, Stitch Fix

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Data Science, Deep Learning and ML

Build Large-Scale Data Analytics and AI Pipeline Using RayDP

Carson Wang, Intel • Xianyang Liu, Intel

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Open Source Data and ML Tools

Building an ML Platform with Ray and MLflow

Amog Kamsetty, Anyscale • Archit Kulkarni, Anyscale

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Apache Spark Internals and Best Practices

Stage Level Scheduling Improving Big Data and AI Integration

Thomas Graves, NVIDIA

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Data Engineering and Data Architecture

Degrading Performance? You Might be Suffering From the Small Files Syndrome

Adi Polak, Microsoft

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Databricks Production Use Cases

Misusing MLflow To Help Deduplicate Data At Scale

Robin Oliva-Kraft, Intuit • Maya Livshits, Intuit

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Industry and Business Use Cases

Scaling Privacy in a Spark Ecosystem

Aaron Colcord, Northwestern Mutual • Don Bosco Durai, Privacera

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AI Use Cases

Effective AIOps with Open Source Software in a Week

Keith Andre Kroculick, Wells Fargo • Murali Kaundinya, Wells Fargo

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Low-Latency & High-Concurrency Analytics Over Data Lakes

Boaz Farkash, Firebolt

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How to Extract Deeper Value from Data in Legacy Applications with Analytics in a Cloud Data Lake

Andy Kazmaier, HVR

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Data Engineering and Data Architecture

The Rise of Vector Data

Edo Liberty, Pinecone

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Apache Spark Internals and Best Practices

How We Optimize Spark SQL Jobs With parallel and sync IO

Jun Guo, Bytedance

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Data Engineering and Data Architecture

Manipulating Geospatial Data at Massive Scale

Charcey Petersen, John Deere • Christopher Hicks, John Deere

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Industry and Business Use Cases

From Vaccine Management to ICU Planning: How CRISP Unlocked the Power of Data During a Pandemic

Andy Hanks, Chesapeake Regional Information System • Steve Dowling, Slalom Consulting

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Data Science, Deep Learning and ML

Rethinking Feature Stores

Willem Pienaar, Tecton • Mike Del Balso

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Data Engineering and Data Architecture

Configuration Driven Reporting On Large Dataset Using Apache Spark

Arvind Das, american express • Zheng Gu, american express

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Data Science, Deep Learning and ML

Intuitive & Scalable Hyperparameter Tuning with Apache Spark + Fugue

Han Wang, Lyft Inc.

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Open Source Data and ML Tools

MLCommons: Better ML for Everyone

David Kanter, MLCommons

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Open Source Data and ML Tools

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Liang Zhang, Databricks

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SQL Analytics and BI

Advanced SQL For Data Scientists

Jean Joseph, Allied World

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Databricks Production Use Cases

Migrating Your Data Platform At a High Growth Startup

Carlos Gasperi, Abnormal Security

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Apache Spark Internals and Best Practices

Deep Dive into the New Features of Apache Spark 3.1

Wenchen Fan, Databricks • Xiao Li, Databricks

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Data Science, Deep Learning and ML

Real-world Strategies for Debugging Machine Learning Systems

Patrick Hall, bnh.ai | The George Washington University

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Industry and Business Use Cases, Databricks Production Use Cases

Introducing NEPHOS: Implement Lakehouse Without the Infrastructure Management

Aaron Davidson, Databricks • Vinay Wagh, Databricks

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Databricks Platform

Modernizing to a Cloud Data Architecture

Guido Oswald, Databricks • Matt Graves, GCI Communication Corp.

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Databricks Platform

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Ron Guerrero, Databricks

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A Modern Culture of Data Powered By Slalom + Databricks

Krishanu Nandy, Slalom Consulting

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Data Science, Deep Learning and ML

Re-imagine Data Monitoring with whylogs and Spark

Andy Dang, Whylabs

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Data Science, Deep Learning and ML

Why APM Is Not the Same As ML Monitoring

Cory Johannsen, Verta Inc

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Data Engineering and Data Architecture

Let’s Dumb-Proof Data Pipelines

Ganesh Chand, Databricks

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Open Source Data and ML Tools

Hyperspace for Delta Lake

Rahul Potharaju, Microsoft • Terry Kim, Microsoft, and more

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Data Engineering and Data Architecture

Architecting Agile Data Applications for Scale

Richard Garris, Databricks

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Industry and Business Use Cases

Weekday Demand Sensing at Walmart

Divya Hindupur, Walmart Labs • Jay Kakkar, Walmart Labs

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Open Source Data and ML Tools

Scaling AutoML-Driven Anomaly Detection With Luminaire

Sayan Chakraborty, Zillow, Inc. • Smit Shah, Zillow

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SQL Analytics and BI

Large Scale Lakehouse Implementation Using Structured Streaming

Tomasz Magdanski, Asurion

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Data Science, Deep Learning and ML

Infrastructure Agnostic Machine Learning Workload Deployment

Abi Akogun, MavenCode • Charles Adetiloye, MavenCode

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Data and ML Research

Efficient Large-Scale Language Model Training on GPU Clusters

Deepak Narayanan, Stanford University

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Why Trusted AI Starts With Self-Service Data Quality

Vadim Vaks, Collibra

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Ignite A Revolution With Enterprise Apache Spark On Multi-Cloud Kubernetes

Matt Maccaux, Hewlett Packard Enterprise

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Data Science, Deep Learning and ML

Data Brew Vidcast: Infrastructure for ML

Adam Oliner

Open Source Data and ML Tools

Machine Learning with PyCaret

Moez Ali, PyCaret

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Data Science, Deep Learning and ML

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Elijah Ben Izzy, Stitch Fix

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Data Science, Deep Learning and ML

Towards Personalization in Global Digital Health

Africa Perianez, benshi.ai

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Data Science, Deep Learning and ML

How to Build a ML Platform Efficiently Using Open-Source

Jean Machado, GetYourGuide • Theodore Meynard, GetYourGuide

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Data and ML Research

Raven: End-to-end Optimization of ML Prediction Queries

Konstantinos Karanasos, Microsoft • Kwanghyun Park, Microsoft

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Data Engineering and Data Architecture

Hybrid Apache Spark Architecture with YARN and Kubernetes

Catalin Toda, Lyft Inc. • Rohit Menon, Lyft Inc.

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Data and ML Research

Drug and Vaccine Discovery: Knowledge Graph + Apache Spark

John Hunter, GlaxoSmithKline

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Apache Spark Internals and Best Practices

Comprehensive View on Intervals in Apache Spark 3.2

Maxim Gekk, Databricks

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Data Science, Deep Learning and ML

Automated Background Removal Using PyTorch

Oleksander Miroshnychenko, GlobalLogic • Simona Stolnicu, Levi9

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Databricks Production Use Cases

Giving Away The Keys To The Kingdom: Using Terraform To Automate Databricks

Hamilton Hord, Scribd • Serge Smertin, Databricks

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Data Engineering and Data Architecture

Building the Foundations of an Intelligent, Event-Driven Data Platform at EFSA

Sebastiaan Leysen, delaware • David De Wilde, Delaware Consulting CVBA

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Industry and Business Use Cases

NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobil

Sumeet Trehan, ExxonMobil

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SQL Analytics and BI

SQL Analytics Powering Telemetry Analysis at Comcast

Suraj Nesamani, Comcast • Molly Nagamuthu, Databricks

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Data Engineering and Data Architecture

Phar Data Platform: From the Lakehouse Paradigm to the Reality

Marc Planagumà, SCRM - LIDL International Hub

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Sub-Second Analytics for User-Facing Applications with Apache Spark and Rockset

Venkat Venkataramani, Rockset

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Identify Sensitive Data and Mitigate Risk in Apache Spark and Databricks

Dan Rice, BigID

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Data Engineering and Data Architecture

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Yeshwanth Vijayakumar, Adobe, Inc.

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Open Source Data and ML Tools

A Fast Decision Rule Engine for Anomaly Detection

James Thomas, Stanford University

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Data and ML Research

Sawtooth Windows for Feature Aggregations

Nikhil Simha, Airbnb

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Data Science, Deep Learning and ML

Processing Large Datasets for ADAS Applications using Apache Spark

Arvind Hosagrahara, MathWorks, Inc.

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Apache Spark Internals and Best Practices

Improving Apache Spark for Dynamic Allocation and Spot Instances

Holden Karau, Apple

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Databricks Production Use Cases

Managing R&D Data on Parallel Compute Infrastructure

Andrew Brown, ZS Associates

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Databricks Production Use Cases

Building a Data Science as a Service Platform in Azure with Databricks

Terry McCann, Advancing Analytics

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AI Use Cases

Recommender-Based Transformers

Denis Rothman, Denis Rothman

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Data Engineering and Data Architecture

Change Data Feed in Delta

Rahul Mahadev, Databricks • Itai Weiss, Databricks, and more

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Industry and Business Use Cases

Gender Prediction with Databricks AutoML Pipeline

Sharon Xu, AARP

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Guaranteeing Data Quality SLAs with Deequ & Databand

Josh Benamram, Databand.ai • Michael Harper, Databand.ai

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Apache Spark Internals and Best Practices

Optimizing the Catalyst Optimizer for Complex Plans

Jianneng Li, Workday • Asif Shahid, Workday

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SQL Analytics and BI

Natural Language Query and Conversational Interface to Apache Spark

Anand Ranganathan, Unscrambl

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Databricks Production Use Cases

10 Things Learned Releasing Databricks Enterprise Wide

Jake Kulas, Western Governors University

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Data Engineering and Data Architecture

Scaling your Data Pipelines with Apache Spark on Kubernetes

Rajesh Thallam, Google • Sougata Biswas, Google

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Databricks Production Use Cases

Build Real-Time Applications with Databricks Streaming

Bennie Haelen, Insight Digital Innovation

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Data Science, Deep Learning and ML

Superworkflow of Graph Neural Networks with K8S and Fugue

Jintao Zhang, Square Inc.

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Data Science, Deep Learning and ML

Fully Utilizing Spark for Data Validation

Kevin Kho, Prefect

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Data and ML Research

Automatic ICD-10 Code Assignment to Consultations

Joinal Ahmed, Halodoc Technologies • Nirav Kumar, Halodoc Technologies

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Open Source Data and ML Tools

Data Quality With or Without Apache Spark and Its Ecosystem

Serge Smertin, Databricks

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Data Science, Deep Learning and ML

Observability for Data Pipelines With OpenLineage

Julien Le Dem, Datakin

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Apache Spark Internals and Best Practices

Delight: An Improved Apache Spark UI, Free, and Cross-Platform

Jean-Yves Stephan, Data Mechanics • Julien Dumazert, Data Mechanics

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Data Science, Deep Learning and ML

Brokering Data: Accelerating Data Evaluation with Databricks White Label

Vinoo Ganesh, Veraset • Nick Chmura, Veraset

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