To unlock all the competitive advantage Databricks offers your organization, you might need to update your strategy and methodology for the platform. With over 1,000+ Databricks projects completed globally in the last 18 months, we are going to share our insights on the best building blocks to target as you search for efficiency and competitive advantage.

These building blocks supporting this include enterprise metadata and data management services, data management foundation, and data services and products that enable business units to fully use their data and analytics at scale.

In this session, Avanade data leaders will highlight how Databricks’ modern data stack fits Azure PaaS and SaaS (such as Microsoft Fabric) ecosystem, how Unity catalog metadata supports automated data operations scenarios, and how we are helping clients measure modern analytics and governance business impact and value.

Talk by: Alan Grogan and Timur Mehmedbasic

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp Databricks named a Leader in 2022 Gartner® Magic QuadrantTM CDBMS: https://dbricks.co/3phw20d

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

ABN AMRO Bank is one of the top leading banks in the Netherlands. It is the third largest bank in the Netherlands by revenue and number of mortgages held within the Netherlands, and has top management support of the objective to become a fully data-driven bank. ABN AMRO started its data journey almost seven years ago and has built a data platform off-premises with Hadoop technologies. This data platform has been used by more than 200 data providers, 150 data consumers, and more than 3000 datasets.

To become a fully digital bank and address the limitation of the on-premises platform requires a future-proof data platform DIAL (digital integration and access layer). ABN AMRO decided to build an Azure cloud-native data platform with the help of Microsoft and Databricks. Last year this cloud-native platform was ready for our data providers and data consumers. Six months ago we started the journey of migrating all the content from the on-premises data platform to the Azure data platform, this was a very large-scale migration and was achieved in six months.

In this session, we will focus on three things: 1. The migration strategy going from on-premises to a cloud-native platform 2. Which Databricks solutions were used in the data platform 3. How the Databricks team assisted in the overall migration

Talk by: Rakesh Singh and Marcel Kramer

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

We designed a scalable Spark Streaming job to manage 100s of millions of prescription-related operations per day at an end-to-end SLA of a few minutes and a lookup time of one second using CosmosDB.

In this session, we will share not only the architecture, but the challenges and solutions to using the Spark Cosmos connector at scale. We will discuss usages of the Aggregator API, custom implementations of the CosmosDB connector, and the major roadblocks we encountered with the solutions we engineered. In addition, we collaborated closely with Cosmos development team at Microsoft and will share the new features which resulted. If you ever plan to use Spark with Cosmos, you won't want to miss these gotchas!

Talk by: Daniel Zafar

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Every year, organizations lose millions of dollars due to equipment failure, unscheduled downtime, or unoptimized supply chains because business and operational data is not integrated. During this session you will hear from experts at Qlik and Databricks on how global luxury automotive manufacturers are accelerating the discovery and availability of complex data sets like SAP. Learn how Qlik, Microsoft, and Databricks together are delivering an integrated solution for global luxury automotive manufacturers that combines the automated data delivery capabilities of Qlik Data Integration with the agility and openness of the Databricks Lakehouse platform and AI on Azure Synpase.

We'll explore how to leverage the IT and OT data convergence to extract the full potential of business-critical SAP data, lower IT costs and deliver real-time prescriptive insights, at scale, for more resilient, predictable, and sustainable supply-chains. Learn how organizations can track and manage inventory levels, predict demand, optimize production and help their organizations identify opportunities for improvements.

Talk by: Matthew Hayes and Bala Amavasai

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksi

The widely-adopted combination of Power BI and Databricks has been a game-changer in providing a comprehensive solution for modern data analytics. In this session, you’ll learn how self-service analytics combined with the Databricks Lakehouse Platform can allow users to make better-informed decisions by unlocking insights hidden in complex data. We’ll provide practical examples of how organizations have leveraged these technologies together to drive digital transformation, lower total cost of ownership (TCO), and increase revenue. By the end of the presentation and demo, you’ll understand how Power BI and Databricks can help drive real-time insights at scale for organizations in any industry.

Talk by: Bob Zhang and Mahesh Prakriya

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

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

With the world’s most distributed compute platform — from cloud to edge — Akamai makes it easy for businesses to develop and run applications, while keeping experiences closer to users and threats farther away. ​So when it was time to scale it’s legacy Hadoop-like infrastructure reaching its capacity limits, while keeping their global operations running uninterrupted, Akamai partnered with Microsoft and Databricks to migrate to Azure Databricks.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/

Data + AI Summit 2022 was a great opportunity to check-in on the partnership between Microsoft Azure and Databricks!

Business Intelligence (BI) tools such as Tableau and Microsoft Power BI are notoriously slow at extracting large query results from traditional data warehouses because they typically fetch the data in a single thread through a SQL endpoint that becomes a data transfer bottleneck. Data analysts can connect their BI tools to Databricks SQL endpoints to query data in tables through an ODBC/JDBC protocol integrated in our Simba drivers. With Cloud Fetch, which we released in Databricks Runtime 8.3 and Simba ODBC 2.6.17 driver, we introduce a new mechanism for fetching data in parallel via cloud storage such as AWS S3 and Azure Data Lake Storage to bring the data faster to BI tools. In our experiments using Cloud Fetch, we observed a 10x speed-up in extract performance due to parallelism.

Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/data... Instagram: https://www.instagram.com/databricksinc/