Data + AI Professional Workshop Series

Join this session as we introduce the benefits of using the Databricks Data Intelligence Platform, that allows you and your team to take advantage of data and AI. We will cover the foundational components of Databricks, introduce key features in a platform demo, and share techniques used in Databricks to help democratize data by simplifying the end-end analytics workflow. In just an hour, you will get a quick overview:

• Data Governance with Unity Catalog
• Reducing effort with Serverless jobs, notebooks and SQL Warehouses
• AI/ML capabilities including Gen AI

The Well Architected Framework consists of 7 pillars which describe different areas of concern for the implementation of a data Lakehouse in the cloud. In this session we will provide a systematic way for assessment and recommendation of gold principles/best practices for the implementation and operation of Lakehouse using Databricks.

You will learn:

• What is the Well Architected Framework and Why?
• How do we help customers implement Well Architected Lakehouse
• Playbook, templates and practical examples of Well Architected Lakehouse

This session will explore how to optimize your Databricks environment, showcasing both recent AI-driven optimizations and a variety of other best practices to ensure you fully leverage the platform’s capabilities for optimal performance and cost-efficiency.

In this session, you will learn:

• Leverage new features (Serverless compute, Predictive Optimisation, Liquid Clustering etc) to improve your platform performance
• Learn to identify and diagnose areas for improvement within your Databricks environment
• Discover optimization tips and tricks for compute, data, and code, along with specific strategies to solve common performance issues
• Implement monitoring practices to ensure stable performance over time

Part 1: Build your first LLM App
Part 2: Accelerate LLM Apps to Production

Join us in this engaging 2-parts virtual workshop to learn how to build, iterate, and polish your LLM ideas into shiny, production-ready solutions. This virtual workshop is suited for data practitioners looking to use LLMs for a variety of use cases. Whether you’re new to Gen AI or looking to refine your skills, this event is perfect for you.

Part 1: Build your first LLM App

• Introduction to LLM Technology: Gain a quick overview of how LLMs work and their potential applications
• How to work with LLMs
• Building your first RAG (Retrieval Augmented Generation) App

Part 2: Accelerate LLM Apps to Production

• Building evaluations
• Productionizing your App

Join us to learn how Databricks ensures comprehensive security for your data and AI workloads by providing tools at different levels. Our data governance solution, Unity Catalog, offers fine-grained data governance, centralized metadata and user management, centralized data access controls and various other features to seamlessly govern data and AI on any cloud or platform. It also enables secure sharing with Delta Sharing and collaboration through Databricks Clean Rooms and Databricks Marketplace. Discover how Unity Catalog can strengthen your Data Intelligence Platform and support GenAI features.

In this session, you will learn about:

• Data and AI security overview
• Key considerations for end-to-end security and governance
• Introduction to Unity Catalog
• Data and AI governance with Unity Catalog
• Sharing and Collaboration
• Upgrading to Unity Catalog

Join us for an exclusive webinar to learn more about Databricks LakeFlow, the next-generation solution for simplifying Data Engineering. Discover how LakeFlow integrates data ingestion, transformation, and orchestration into a unified platform, enabling both batch and streaming workloads using standard SQL and Python.

This session will cover the below topics:

1. Lakeflow Connect: A point-and-click data ingestion tool that integrates with Databases like SQL Server and Enterprise applications such as Salesforce, Workday, Google Analytics, and ServiceNow.
2. Lakeflow DLT: A declarative framework that allows you to write business logic in SQL and Python while Databricks automates data orchestration, incremental processing and compute infrastructure autoscaling on your behalf.
3. Lakeflow Jobs: Reliably orchestrates and monitors production workloads. Built on the advanced capabilities of Databricks Workflows, it orchestrates any workload, including ingestion, pipelines, notebooks, SQL queries, machine learning training, model deployment and inference. Data teams can also leverage triggers, branching and looping to meet complex data delivery use cases.