Are you looking for an introduction to the Lakehouse and what the related technology is all about? This session is for you. This session explains the value that lakehouses bring to the table using examples of companies that are actually modernizing their data, showing demos throughout. The data lakehouse is the future for modern data teams that want to simplify data workloads, ease collaboration, and maintain the flexibility and openness to stay agile as a company scales.

Come to this session and learn about the full stack, including data engineering, data warehousing in a lakehouse, data streaming, governance, and data science and AI. Learn how you can create modern data solutions of your own.

Talk by: Ari Kaplan and Pearl Ubaru

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With businesses competing to deliver value while growing rapidly and adapting to changing markets, it is more important than ever for data teams to support faster and reliable insights. We need to fail fast, learn, adapt, release and repeat. For us, Trusted and unified data infrastructure with standardized practices is at the crux of it all

In this talk: we'll go over Atlassian's data engineering team organization, infrastructure and development practices

• Team organization and roles
• Overview of our data engineering technical stack
• Code repositories and CICD setup
• Testing framework
• Development walkthrough
• Production data quality & integrity
• Alerting & Monitoring
• Tracking operational metrics (SLI/SLO, Cost)

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Serving patients in over 100 countries, Amgen is a leading global biotech company focused on developing therapies that have the power to save lives. Delivering on this mission requires our commercial teams to regularly meet with healthcare providers to discuss new treatments that can help patients in need. With the onset of the pandemic, where face-to-face interactions with doctors and other Healthcare Providers (HCPs) were severely impacted, Amgen had to rethink these interactions. With that in mind, the Amgen Commercial Data and Analytics team leveraged a modern data and AI architecture built on the Databricks Lakehouse to help accelerate its digital and data insights capabilities. This foundation enabled Amgen’s teams to develop a comprehensive, customer-centric view to support flexible go-to-market models and provide personalized experiences to our customers. In this presentation, we will share our recent journey of how we took an agile approach to bringing together over 2.2 petabytes of internally generated and externally sourced vendor data , and onboard into our AWS Cloud and Databricks environments to enable a standardized, scalable and robust capabilities to meet the business requirements in our fast-changing life sciences environment. We will share use cases of how we harmonized and managed our diverse sets of data to deliver efficiency, simplification, and performance outcomes for the business. We will cover the following aspects of our journey along with best practices we learned over time: • Our architecture to support Amgen’s Commercial Data & Analytics constant processing around the globe • Engineering best practices for building large scale Data Lakes and Analytics platforms such as Team organization, Data Ingestion and Data Quality Frameworks, DevOps Toolkit and Maturity Frameworks, and more • Databricks capabilities adopted such as Delta Lake, Workspace policies, SQL workspace endpoints, and MLflow for model registry and deployment. Also, various tools were built for Databricks workspace administration • Databricks capabilities being explored for future, such as Multi-task Orchestration, Container-based Apache Spark Processing, Feature Store, Repos for Git integration, etc. • The types of commercial analytics use cases we are building on the Databricks Lakehouse platform Attendees building global and Enterprise scale data engineering solutions to meet diverse sets of business requirements will benefit from learning about our journey. Technologists will learn how we addressed specific Business problems via reusable capabilities built to maximize value.

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In this session, I cover our lessons in rethinking data governance by approaching data governance as an enablement function through implementing over 200+ data projects. I’ll go into the nuts and bolts of tooling and cultural practices governing our team and data helped our team accomplish projects twice as fast with teams that were one-third our normal size.

The session concludes with why organizations should start believing in and investing in true data governance and implementing governance tools and processes that are agile and collaborative, rather than top-down.

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In today’s modern IT organization whether it is the delivery of a sophisticated analytical model or a product advancement decision or understanding the behavior of a customer, the fact remains that in every instance we rely on data to make good, informed decisions. Given this backdrop, having an architecture which supports the ability to efficiently collect data from a wide range of sources within the company is still an important goal of all data organizations.

In this session we will explain how Bayer has deployed a hybrid data platform which strives to integrate key existing legacy data systems of the past while taking full advantage of what a modern cloud data platform has to offer in terms of scalability and flexibility. It will elaborate the use of its most significant component, Databricks, which serves to provide not only a very sophisticated data pipelining solution but also a complete ecosystem for teams to create data and analytical solutions in a flexible and agile way.

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Learn from two governance and data practitioners what it takes to do data governance at enterprise scale. This is critical, since the power of Data Science is the ability to tap into any type of data source and turn it into pure value. It is at odds with its key enablers of Scale and Governance and we often must tackle new ways to bring our focus back to unlocking the insights inside the data. In this session, We will share new agile practices to roll out governance policies that balance Governance and Scale. We will untap how to deliver centralized fine-grained governance for ML and data transformation workloads that actually empowers data scientists in an enterprise Databricks environment that ensures privacy and compliance across hundreds of datasets. With automation being key to scale, we will also explore how we successfully automated security and governance

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The data lakehouse is the future for modern data teams seeking to innovate with a data architecture that simplifies data workloads, eases collaboration, and maintains the flexibility and openness to stay agile as a company scales. The Databricks Lakehouse Platform realizes this idea by unifying analytics, data engineering, machine learning, and streaming workloads across clouds on one simple, open data platform. In this session, learn how the Databricks Lakehouse Platform can meet your needs for every data and analytics workload, with examples of real-customer applications, reference architectures, and demos to showcase how you can create modern data solutions of your own.

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Welcome data teams and executives in the Manufacturing industry! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on the biggest innovations around improving manufacturing operations, building agile supply chains and enabling an AI-driven business. To help you take full advantage of the Manufacturing experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Manufacturing Industry Forum: Our capstone event for Manufacturing attendees at Summit featuring keynotes and panel discussions with John Deere, Honeywell and Collins Aerospace followed by networking. More details in the agenda below. Manufacturing Lounge:Stop by our lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from The Global Solution Integrator and Tredence. Session Talks: Insightful talks on predicting and preventing machine downtime, real-time process optimization and leveraging informational and operational technology data to make enterprise decisions.

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