The insurance industry is rapidly evolving as advances in data and artificial intelligence (AI) drive innovation, enabling more personalized customer experiences, streamlined operations, and improved efficiencies. With powerful data analytics and AI-driven solutions, insurers can automate claims processing, enhance risk management, and make real-time decisions. Leveraging insights from large and complex datasets, organizations are delivering more customer-centric products and services than ever before. Key takeaways: Real-world applications of data and AI in claims automation, underwriting, and customer engagementHow predictive analytics and advanced data modeling help anticipate risks and meet customer needs. Personalization of policies, optimized pricing, and more efficient workflows for greater ROI. Discover how data and AI are fueling growth, improving protection, and shaping the future of the insurance industry!

Seismic shift Large Language Models are unleashing on data engineering, challenging traditional workflows. LLMs obliterate inefficiencies and redefine productivity. AI powerhouses automate complex tasks like documentation, code translation, and data model development with unprecedented speed and precision. Integrating LLMs into tools promises to reduce offshore dependency, fostering agile onshore innovation. Harnessing LLMs' full potential involves challenges, requiring deep dives into domain-specific data and strategic business alignment. Session will addresses deploying LLMs effectively, overcoming data management hurdles, and fostering collaboration between engineers and stakeholders. Join us to explore a future where LLMs redefine possibilities, inviting you to embrace AI-driven innovation and position your organization as a leader in data engineering.

Eli Lilly and Company, a leading bio-pharma company, is revolutionizing manufacturing with next-gen fully digital sites. Lilly and Tredence have partnered to establish a Databricks-powered Global Manufacturing Data Fabric (GMDF), laying the groundwork for transformative data products used by various personas at sites and globally. By integrating data from various manufacturing systems into a unified data model, GMDF has delivered actionable insights across several use cases such as batch release by exception, predictive maintenance, anomaly detection, process optimization and more. Our serverless architecture leverages Databricks Auto Loader for real-time data streaming, PySpark for automation and Unity Catalog for governance, ensuring seamless data processing and optimization. This platform is the foundation for data driven processes, self-service analytics, AI and more. This session will provide details on the data architecture and strategy and share a few use cases delivered.

Unlock the truth behind data modeling in Databricks. This session will tackle the top 10 myths surrounding relational and dimensional data modeling. Attendees will gain a clear understanding of what Databricks Lakehouse truly supports today, including how to leverage primary and foreign keys, identity columns for surrogate keys, column-level data quality constraints and much more. This session will talk through the lens of medallion architecture, explaining how to implement data models across bronze, silver, and gold tables. Whether you’re migrating from a legacy warehouse or building new analytics solutions, you’ll leave equipped to fully leverage Databricks’ capabilities, and design scalable, high-performance data models for enterprise analytics.

This session is repeated. In today’s data-driven world, the Data Lakehouse has emerged as a powerful architectural paradigm that unifies the flexibility of data lakes with the reliability and structure of traditional data warehouses. However, organizations must adopt the right data modeling techniques to unlock its full potential to ensure scalability, maintainability and efficiency. This session is designed for beginners looking to demystify the complexities of data modeling for the lakehouse and make informed design decisions. We’ll break down Medallion Architecture, explore key data modeling techniques and walk through the maturity stages of a successful data platform — transitioning from raw, unstructured data to well-organized, query-efficient models.

This course offers a deep dive into designing data models within the Databricks Lakehouse environment, and understanding the data products lifecycle. Participants will learn to align business requirements with data organization and model design leveraging Delta Lake and Unity Catalog for defining data architectures, and techniques for data integration and sharing. Prerequisites: Foundational knowledge equivalent to Databricks Certified Data Engineer Associate and familiarity with many topics covered in Databricks Certified Data Engineer Professional. Experience with: Basic SQL queries and table creation on Databricks Lakehouse architecture fundamentals (medallion layers) Unity Catalog concepts (high-level) [Optional] Familiarity with data warehousing concepts (dimensional modeling, 3NF, etc.) is beneficial but not mandatory. Labs: Yes