Databricks Experience
Introducing the Databricks Experience (DBX)
A curated set of content and sessions to help you learn more about the amazing innovation happening at Databricks. Designed to help you make the most of your time at Summit, DBX offers quick access to the subjects most relevant to your needs. Some key features of DBX include:
- Product deep dives concentrated on Delta Lake, managing multi-cloud platforms, modernizing to a cloud data architecture and more
- Training in SQL Analytics, data science, machine learning and more
- News on upcoming products and features
- Networking and engagement with other attendees and Databricks experts
Speaker Highlights
- Claudiu Barbura, Director of Engineering, Blueprint Technologies
- Max Cantor, Software Engineer, Condé Nast
- Fred Kimball, Software Engineer, Northwestern Mutual
- Molly Nagamuthu, Senior Solutions Architect, Databricks
- Suraj Nesamani, Principal Engineer, Comcast
- Franco Patano, Senior Solutions Architect, Databricks
- Josh Reilly, Lead Software Engineer, Northwestern Mutual
- Harin Sanghirun, Machine Learning Engineer, Condé Nast
- Yeshwanth Vijayakumar, Senior Engineering Manager/Architect, Adobe
Featured Sessions
Intro to Delta Lake
Delta Lake delivers reliability, security, and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to an insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable Lakehouse architecture.
Make Reliable ETL Easy on Delta Lake
As the size and complexity of data grows, building reliable data pipelines is increasingly important, but also complex and challenging. Learn how Databricks simplifies the ETL lifecycle on Delta Lake, helping data engineering teams greatly simplify ETL development and ongoing management to improve data quality, scale operations and reduce cost.
What’s New With Databricks Machine Learning
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
Learn to Use Databricks for the Full ML Lifecycle
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
Databricks SQL Analytics Deep Dive for the Data Analyst
In this session, we will cover best practices for analysts, data scientists, and SQL developers exploring Databricks SQL Analytics as a solution for their companies. This guided technical tour of the product walks through:
- Creating and working with queries, dashboards, query refresh and alerts
- Constructing queries for semi-structured data, such as json, structs, and arrays
- Navigating the improved Spark SQL Documentation to find and leverage powerful built-in functions to solve common problems
- Creating connections to 3rd party BI and database tools (PowerBI, Tableau, dbVisualizer etc.)
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Databricks Lakehouse Platform Governance and Security Fundamentals
Attacks on enterprise data can come from employees with access to company systems or external private or state-sponsored malicious actors. Some of the larger well-known data breaches were planned and executed over months and years of preparation, and in all cases, the victims were unaware until it was too late — the damage was done. Any comprehensive solution an enterprise adopts to mitigate the risk have to address all four areas of people, process, policy and platform. Most may spend a lot of time managing people, policies and processes. But what happens when you start with your data platform, the core of your entire data architecture, and work your way out? In this session, learn the fundamentals of governance and security for your cloud data and analytics platform, including extending cloud identity management, setting up private links, monitoring access and costs, and ensuring the right policies are enforced for every workspace.
Managing a Multicloud Data and Analytics Platform
Multicloud adoption is gaining momentum. Gartner predicts that by 2022, 75% of enterprise customers using cloud infrastructure as a service (IaaS) will adopt a deliberate multicloud strategy. Enterprises adopt multicloud strategies for various reasons, such as preventing vendor lock-in, enabling access to best-of-breed cloud services, regional requirements, and so on. However, there are challenges with this model. Every cloud has its way of doing things, and they don’t play nice together. What if you had a data and analytics platform that extended across the major cloud providers? How would you manage it? In this session, you’ll learn how simple it is to manage the Databricks Lakehouse Platform — one platform for data, analytics, and AI across AWS, Microsoft Azure and Google Cloud.
Speaker Highlights
- Claudiu Barbura, Director of Engineering, Blueprint Technologies
- Max Cantor, Software Engineer, Condé Nast
- Fred Kimball, Software Engineer, Northwestern Mutual
- Molly Nagamuthu, Senior Solutions Architect, Databricks
- Suraj Nesamani, Principal Engineer, Comcast
- Franco Patano, Senior Solutions Architect, Databricks
- Josh Reilly, Lead Software Engineer, Northwestern Mutual
- Harin Sanghirun, Machine Learning Engineer, Condé Nast
- Yeshwanth Vijayakumar, Senior Engineering Manager/Architect, Adobe
Featured Sessions
Intro to Delta Lake
Delta Lake delivers reliability, security, and performance to data lakes. Join this session to learn how customers have achieved 48x faster data processing, leading to 50% faster time to an insight after implementing Delta Lake. You’ll also learn how Delta Lake provides the perfect foundation for a cost-effective, highly scalable Lakehouse architecture.
Make Reliable ETL Easy on Delta Lake
As the size and complexity of data grows, building reliable data pipelines is increasingly important, but also complex and challenging. Learn how Databricks simplifies the ETL lifecycle on Delta Lake, helping data engineering teams greatly simplify ETL development and ongoing management to improve data quality, scale operations and reduce cost.
What’s New With Databricks Machine Learning
In this session, the Databricks product team provides a deeper dive into the machine learning announcements. Join us for a detailed demo that gives you insights into the latest innovations that simplify the ML lifecycle — from preparing data, discovering features, and training and managing models in production.
Learn to Use Databricks for the Full ML Lifecycle
Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. In this talk, learn how to operationalize ML across the full lifecycle with Databricks Machine Learning.
Databricks SQL Analytics Deep Dive for the Data Analyst
In this session, we will cover best practices for analysts, data scientists, and SQL developers exploring Databricks SQL Analytics as a solution for their companies. This guided technical tour of the product walks through:
- Creating and working with queries, dashboards, query refresh and alerts
- Constructing queries for semi-structured data, such as json, structs, and arrays
- Navigating the improved Spark SQL Documentation to find and leverage powerful built-in functions to solve common problems
- Creating connections to 3rd party BI and database tools (PowerBI, Tableau, dbVisualizer etc.)
Learn to Use Databricks for Data Science
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Databricks Lakehouse Platform Governance and Security Fundamentals
Attacks on enterprise data can come from employees with access to company systems or external private or state-sponsored malicious actors. Some of the larger well-known data breaches were planned and executed over months and years of preparation, and in all cases, the victims were unaware until it was too late — the damage was done. Any comprehensive solution an enterprise adopts to mitigate the risk have to address all four areas of people, process, policy and platform. Most may spend a lot of time managing people, policies and processes. But what happens when you start with your data platform, the core of your entire data architecture, and work your way out? In this session, learn the fundamentals of governance and security for your cloud data and analytics platform, including extending cloud identity management, setting up private links, monitoring access and costs, and ensuring the right policies are enforced for every workspace.
Managing a Multicloud Data and Analytics Platform
Multicloud adoption is gaining momentum. Gartner predicts that by 2022, 75% of enterprise customers using cloud infrastructure as a service (IaaS) will adopt a deliberate multicloud strategy. Enterprises adopt multicloud strategies for various reasons, such as preventing vendor lock-in, enabling access to best-of-breed cloud services, regional requirements, and so on. However, there are challenges with this model. Every cloud has its way of doing things, and they don’t play nice together. What if you had a data and analytics platform that extended across the major cloud providers? How would you manage it? In this session, you’ll learn how simple it is to manage the Databricks Lakehouse Platform — one platform for data, analytics, and AI across AWS, Microsoft Azure and Google Cloud.