Data + AI Summit 2022: June 27–30, Moscone Center + Virtual  Image

Data + AI Summit 2022: June 27–30, Moscone Center + Virtual

Join us for unique community-driven content, deep technical training and outstanding speakers like Andrew Ng and Ali Ghodsi.

Demo Hub

Get a firsthand look at Databricks from the practitioner’s perspective with these simple on-demand videos. Each of the demos below is paired with related materials — including notebooks, videos and eBooks — so that you can try it out for yourself on Databricks.

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Product demos

Demo Hub

In this demo, we walk through a high-level overview of the Databricks Lakehouse Platform, including discussion of how open source projects, such as Apache Spark™, Delta Lake, MLflow and Koalas, fit into the Databricks ecosystem. Learn more →

In this demo, we walk through some of the features of the new Databricks SQL that are important to data analysts, including the integrated data browser, SQL query editor with live autocomplete, built-in data visualization tools, and flexible dashboarding and alerting capabilities. We also cover how Databricks SQL endpoints provide a high-performance, low latency, SQL-optimized compute resource that can power your existing BI tools like Power BI and Tableau. Learn more →

Databricks Workflows

Databricks Workflows is the fully managed orchestration service for all your data, analytics, and AI. Deep integration with the underlying lakehouse platform ensures you will create and run reliable production workloads on any cloud while providing deep and centralized monitoring with simplicity for end-users.
Learn more →

In this demo, we walk through a real-world data science and machine learning use case on Databricks, showing how different members of the data team can interact and collaborate on the Databricks platform. We also show how MLflow on Databricks simplifies and streamlines the end-to-end machine learning lifecycle. Learn more →

Delta Lake on Databricks enables you to build a lakehouse architecture that combines the best of data lakes and data warehouses. This simple, open platform stores and manages your data while supporting your analytics and AI use cases. In this demo, we cover Delta Lake features, including unified batch and streaming data processing, schema enforcement and evolution, time travel, and support for UPDATE, MERGE and DELETE commands. The demo also highlights a few performance enhancements available with Delta Lake on Databricks. Learn more →

In this demo, we give you a first look at Delta Live Tables, a cloud service that makes reliable ETL – extract, transform and load capabilities – easy on Delta Lake. It helps data engineering teams simplify ETL development with a simple UI and declarative tooling, improve data reliability through defined data quality rules and bad data monitoring, and scale operations with deep visibility through an event log. Learn more →

With Databricks Auto Loader, you can incrementally and efficiently ingest new batch and real-time streaming data files into your Delta Lake tables as soon as they arrive — so that they always contain the most complete and up-to-date data available. SQL users can use the simple “COPY INTO” command to pull new data into their Delta Lake tables automatically, without the need to keep track of which files have already been processed. Learn more →








Demo Hub

In this demo, we walk through a high-level overview of the Databricks Lakehouse Platform, including discussion of how open source projects, such as Apache Spark™, Delta Lake, MLflow and Koalas, fit into the Databricks ecosystem. Learn more →

In this demo, we walk through some of the features of the new Databricks SQL that are important to data analysts, including the integrated data browser, SQL query editor with live autocomplete, built-in data visualization tools, and flexible dashboarding and alerting capabilities. We also cover how Databricks SQL endpoints provide a high-performance, low latency, SQL-optimized compute resource that can power your existing BI tools like Power BI and Tableau. Learn more →

Databricks Workflows

Databricks Workflows is the fully managed orchestration service for all your data, analytics, and AI. Deep integration with the underlying lakehouse platform ensures you will create and run reliable production workloads on any cloud while providing deep and centralized monitoring with simplicity for end-users.
Learn more →

In this demo, we walk through a real-world data science and machine learning use case on Databricks, showing how different members of the data team can interact and collaborate on the Databricks platform. We also show how MLflow on Databricks simplifies and streamlines the end-to-end machine learning lifecycle. Learn more →

Delta Lake on Databricks enables you to build a lakehouse architecture that combines the best of data lakes and data warehouses. This simple, open platform stores and manages your data while supporting your analytics and AI use cases. In this demo, we cover Delta Lake features, including unified batch and streaming data processing, schema enforcement and evolution, time travel, and support for UPDATE, MERGE and DELETE commands. The demo also highlights a few performance enhancements available with Delta Lake on Databricks. Learn more →

In this demo, we give you a first look at Delta Live Tables, a cloud service that makes reliable ETL – extract, transform and load capabilities – easy on Delta Lake. It helps data engineering teams simplify ETL development with a simple UI and declarative tooling, improve data reliability through defined data quality rules and bad data monitoring, and scale operations with deep visibility through an event log. Learn more →

With Databricks Auto Loader, you can incrementally and efficiently ingest new batch and real-time streaming data files into your Delta Lake tables as soon as they arrive — so that they always contain the most complete and up-to-date data available. SQL users can use the simple “COPY INTO” command to pull new data into their Delta Lake tables automatically, without the need to keep track of which files have already been processed. Learn more →

Partner demos

The Azure Databricks Lakehouse Platform gives you the best of data lakes and data warehouses, on a simple, open and collaborative platform that securely integrates with your existing Azure services. In this demo, we cover several of the most common Azure Databricks integrations, including Azure Data Lake Storage (ADLS), Azure Data Factory (ADF), Azure IoT Hub, Azure Synapse Analytics, Power BI and more. Learn more →

Databricks runs on AWS and integrates with all of the major services you use like S3, EC2, Redshift, and more. We provide the platform that enables you to combine all of these services to build a lakehouse architecture. In this demo, we’ll show you how Databricks integrates with each of these services simply and seamlessly. Learn more →

Databricks on Google Cloud Integration Promo

Databricks on Google Cloud is a jointly developed service that allows you to store all your data on a simple, open lakehouse platform that combines the best of data warehouses and data lakes. Unify all your analytics and AI workloads on a single platform. Tight integration with Google Cloud Storage, BigQuery and the Google Cloud AI Platform enables Databricks to work seamlessly across data and AI services on Google Cloud.

Easily discover validated data, analytics and AI tools directly within the Databricks platform — and quickly integrate the tools you already use today. With Partner Connect, you can simplify tool integration to just a few clicks and rapidly expand the capabilities of your lakehouse.
Learn more →





The Azure Databricks Lakehouse Platform gives you the best of data lakes and data warehouses, on a simple, open and collaborative platform that securely integrates with your existing Azure services. In this demo, we cover several of the most common Azure Databricks integrations, including Azure Data Lake Storage (ADLS), Azure Data Factory (ADF), Azure IoT Hub, Azure Synapse Analytics, Power BI and more. Learn more →

Databricks runs on AWS and integrates with all of the major services you use like S3, EC2, Redshift, and more. We provide the platform that enables you to combine all of these services to build a lakehouse architecture. In this demo, we’ll show you how Databricks integrates with each of these services simply and seamlessly. Learn more →

Databricks on Google Cloud Integration Promo

Databricks on Google Cloud is a jointly developed service that allows you to store all your data on a simple, open lakehouse platform that combines the best of data warehouses and data lakes. Unify all your analytics and AI workloads on a single platform. Tight integration with Google Cloud Storage, BigQuery and the Google Cloud AI Platform enables Databricks to work seamlessly across data and AI services on Google Cloud.

Easily discover validated data, analytics and AI tools directly within the Databricks platform — and quickly integrate the tools you already use today. With Partner Connect, you can simplify tool integration to just a few clicks and rapidly expand the capabilities of your lakehouse.
Learn more →

Solution Accelerator demos

In this Solution Accelerator, we demonstrate how to use the Databricks Lakehouse Platform to better understand and quantify the holistic ESG impact of any investment in a company or business in order to generate alpha, mitigate reputation risk and maintain the trust of both clients and shareholders. Learn more →

In this Solution Accelerator, we demonstrate how to use Apache Spark™ and Facebook Prophet™ to build dozens of time series forecasting models in parallel on the Databricks Lakehouse Platform. Learn more →

Identify your most valuable and longest lifetime customers. Find out where to prioritize resources and where to limit spending on unprofitable customers — to help improve the ROI of marketing programs. Learn more →




In this Solution Accelerator, we demonstrate how to use the Databricks Lakehouse Platform to better understand and quantify the holistic ESG impact of any investment in a company or business in order to generate alpha, mitigate reputation risk and maintain the trust of both clients and shareholders. Learn more →

In this Solution Accelerator, we demonstrate how to use Apache Spark™ and Facebook Prophet™ to build dozens of time series forecasting models in parallel on the Databricks Lakehouse Platform. Learn more →

Identify your most valuable and longest lifetime customers. Find out where to prioritize resources and where to limit spending on unprofitable customers — to help improve the ROI of marketing programs. Learn more →

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