One of the largest State based health exchanges in the country was looking to modernize their data warehouse (DWH) environment to support the vision that every decision to design, implement and evaluate their state-based health exchange portal is informed by timely and rigorous evidence about its consumers’ experiences. The scope of the project was to replace existing Oracle-based DWH with an analytics platform that could support a much broader range of requirements with an ability to provide unified analytics capabilities including machine learning. The modernized analytics platform comprises a cloud native data lake and DWH solution using Databricks. The solution provides significantly higher performance and elastic scalability to better handle larger and varying data volumes with a much lower cost of ownership compared to the existing solution. In this session, we will walk through the rationale behind tool selection, solution architecture, project timeline and benefits expected.

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Over the last few years two new approaches to data management have been developed in the data community: Data Mesh and Data Lakehouse. The latter is an open architecture that pushes the technological advancements of a Data Lake by adding data management capabilities proven by a long history of Data Warehousing practices. Data Mesh on the other hand is addressing data management challenges from an organizational angle, by advocating decentralized ownership of domain data while applying product thinking and domain-driven design to analytics data. At first one might think that those two architectural approaches are competing with each other, however in this talk you will learn that the two are rather orthogonal and can go very well together.

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Traditional data warehouses typically struggle when it comes to handling large volumes of data and traffic, particularly when it comes to unstructured data. In contrast, data lakes overcome such issues and have become the central hub for storing data. We outline how we can enable BI Kimball data modelling in a Lakehouse environment.

We present how we built a Spark-based framework to modernize DWH development with data lake as central storage, assuring high data quality and scalability. The framework was implemented at over 15 enterprise data warehouses across Europe.

We present how one can tackle in Spark & with Delta Lake the data warehouse principles like surrogate, foreign and business keys, SCD type 1 and 2 etc. Additionally, we share our experiences on how such a unified data modelling framework can bridge BI with modern day use cases, such as machine learning and real time analytics. The session outlines the original challenges, the steps taken and the technical hurdles we faced.

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Most organizations routinely operate their business with complex cloud data architectures that silo applications, users and data. As a result, there is no single source of truth of data for analytics, and most analysis is performed with stale data. To solve these challenges, the lakehouse has emerged as the new standard for data architecture, with the promise to unify data, AI and analytic workloads in one place. In this session, we will cover why the data lakehouse is the next best data warehouse. You will hear from the experts success stories, use cases, and best practices learned from the field and discover how the data lakehouse ingests, stores and governs business-critical data at scale to build a curated data lake for data warehousing, SQL and BI workloads. You will also learn how Databricks SQL can help you lower costs and get started in seconds with instant, elastic SQL serverless compute, and how to empower every analytics engineers and analysts to quickly find and share new insights using their favorite BI and SQL tools, like Fivetran, dbt, Tableau or PowerBI.

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Data architectures are the key and part of a larger picture to building robust analytical and AI applications. One must take a holistic view of the entire data analytics realm when it comes to planning for data science initiatives.

Through this talk, learn about the evolution of the data landscape and why Lakehouses are becoming a de facto for organizations building scalable data architectures. A lakehouse architecture combines data management capability including reliability, integrity, and quality from the data warehouse and supports all data workloads including BI and AI with the low cost and open approach of data lakes.

Data Practitioners will also learn some core concepts of building an efficient Lakehouse with Delta Lake.

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While SAS has been a standard in analytics and data science use cases, it is not cloud-native and does not scale well. Join us to learn how AARP automated the conversion of hundreds of complex data processing, model scoring, and campaign workloads to Databricks using LeapLogic, an intelligent code transformation accelerator that can transform any and all legacy ETL, analytics, data warehouse and Hadoop to modern data platforms.

In this session experts from AARP and Impetus will share about collaborating with Databricks and how they were able to: • Automate modernization of SAS marketing analytics based on coding best practices • Establish a rich library of Spark and Python equivalent functions on Databricks with the same capabilities as SAS procedures, DATA step operations, macros, and functions • Leverage Databricks-native services like Delta Live Tables to implement waterfall techniques for campaign execution and simplify pipeline monitoring

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The logic in your data is the heartbeat of your organization’s reports, analytics, dashboards and applications. But that logic is often trapped in antiquated technologies that can’t take advantage of the massive scalability in the Databricks Lakehouse.

In this session BladeBridge will show how to automate the conversion of this metadata and code into Databricks PySpark and DBSQL. BladeBridge will demonstrate the flexibility of configuring for N legacy technologies to facilitate an automated path for not just a single modernization project but a factory approach for corporate wide modernization.

BladeBridge will also present how you can empirically size your migration project to determine the level of effort required.

In this session you will learn: What BladeBridge Converter is What BladeBridge Analyzer is How BladeBridge configures Readers and Writers How to size a conversion effort How to accelerate adoption of Databricks

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Day 1 Morning Keynote | Data + AI Summit 2022 Welcome & "Destination Lakehouse" | Ali Ghodsi Apache Spark Community Update | Reynold Xin Streaming Lakehouse | Karthik Ramasamy Delta Lake | Michael Armbrust How Adobe migrated to a unified and open data Lakehouse to deliver personalization at unprecedented scale | Dave Weinstein Data Governance and Sharing on Lakehouse |Matei Zaharia Analytics Engineering and the Great Convergence | Tristan Handy Data Warehousing | Shant Hovespian Unlocking the power of data, AI & analytics: Amgen’s journey to the Lakehouse | Kerby Johnson

Get insights on how to launch a successful lakehouse architecture in Rise of the Data Lakehouse by Bill Inmon, the father of the data warehouse. Download the ebook: https://dbricks.co/3ER9Y0K

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Data + AI Summit Keynote talk from Shant Hovespian

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In this keynote, you will learn more about Amgen's data platform journey from data warehouse to data lakehouse. They’’ll discuss our decision process and the challenges they faced with legacy architectures, and how they designed and implemented a sustaining platform strategy with Databricks Lakehouse, accelerating their ability to democratize data to thousands of users.
Today, Amgen has implemented 400+ data science and analytics projects covering use cases like clinical trial optimization, supply chain management and commercial sales reporting, with more to come as they complete their digital transformation and unlock the power of data across the company.

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