Event Registration: Data + AI World Tour

Thursday, November 23, 2023 | 8:30 AM-6:30 PM

Explore the latest advancements, hear real-world case studies and discover best practices that deliver data and AI transformation. From the Databricks Lakehouse Platform to open source technologies including LLMs, Apache Spark™, Delta Lake, MLflow and more — World Tour has the info you need to accelerate and enhance your work.

Generation AI
Organizations across nearly every industry are striving to stay ahead by embracing automation, expediting innovation and enhancing operational efficiency. We believe that generative AI plays a significant role in helping organizations achieve these goals.

Join Generation AI at the Data + AI World Tour to learn more about the transformative potential of AI. Hear from top Fortune 500 companies and Databricks customers on how they have transformed their business through the Lakehouse. From the Databricks Lakehouse Platform to open source technologies including LLMs, Apache Spark™, Delta Lake, MLflow and more — the World Tour will provide the essential insights needed to enhance and accelerate your business initiatives.

Agenda

Grow your network by connecting with hundreds of local data and AI professionals.
Connect with sponsors and discover new solutions and trends on the Expo floor.
Learn from customer use cases, community leaders and Databricks experts.
Uplevel your skills with hands-on training and certifications.

From inspiring keynotes to practical demos and insightful sessions, the Data + AI World Tour Amsterdam has something for you.

Start Time	End Time	Session
08:30	10:00	Registration
10:00	12:15	Keynote
12:15	13:30	Lunch
13:30	16:40	Breakouts, Lightning Talks and Industry Tracks
16:40	18:30	Drinks and Networking

Start Time	End Time	Session
10:00	10:20	Welcome Generation AI
10:20	10:35	How does realtime data processing help to reduce foodwaste?
10:35	11:05	Generation AI
11:05	11:25	From grass to glass, from data to dairy, from numbers to nutrition
11:25	11:40	Fireside Chat
11:40	12:15	Data Leaders Panel

Databricks: Welcome to Generation AI

Visionary leaders will share insights on:

Data, analytics and AI landscape: Learn why there is so much interest in Generative AI and why it's reshaping conversations around data, analytics and AI
Evolution of the lakehouse: More workloads live at the lakehouse than ever, opening up new opportunities to improve your data and AI initiatives
Open source technologies: Discover what’s new and what’s to come for Apache Spark™, Delta Lake and MLflow and learn how this affects the lakehouse and the overall market at large

Albert Heijn: How does realtime data processing help to reduce foodwaste?

Albert Heijn is committed to reduce our food waste with 50% by 2030 through various sustainability initiatives. One of these initiatives is the dynamic markdown project in which we dynamically reduce the prices of products as they get closer to the expiry date. This way we minimize unsold items, thus reducing product waste. Calculating the most effective markdown requires a high freshness of data. We will explain how we process data in realtime with a distributed lakehouse architecture to enable real time use cases like dynamic markdown.

Data Drives FrieslandCampina's Strategy: From grass to glass, from data to dairy, from numbers to nutrition

Fireside Chat: With Ahold Delhaize

Data Leaders Panel: Generation AI

Join some of the biggest data powerhouses in the industry as we discuss the evolution of data culture resulting from the GenAI movement. The panellists will discuss the implications of generative AI on various aspects of organisations, including workforce dynamics, decision-making processes, and the role of human creativity. They will explore how generative AI can disrupt traditional organisational structures and challenge existing power hierarchies. Additionally, the panel aims to provide insights and share strategies for leading organisations through this transformative period, including the importance of fostering a culture of innovation, up-skilling employees to work alongside AI systems, and establishing ethical guidelines for AI usage.

Databricks: Introduction to Data Engineering on the Lakehouse

Data engineering is a requirement for any data, analytics or AI workload. With the increased complexity of data pipelines, the need to handle real-time streaming data and the challenges of orchestrating reliable pipelines, data engineers require the best tools to help them achieve their goals. The Databricks Lakehouse Platform offers a unified platform to ingest, transform and orchestrate data and simplifies the task of building reliable ETL pipelines. This session will provide an introductory overview of the end-to-end data engineering capabilities of the platform, including Delta Live Tables and Databricks Workflows. We’ll see how these capabilities come together to provide a complete data engineering solution and how they are used in the real world by organizations leveraging the lakehouse turning raw data into insights.

Databricks: What's your IT & Data worth? How to communicate Business Value to secure investment

Databricks: More Responsible Generative AI

Microsoft: Modern Analytics with Azure Databricks and Power BI

Enterprises are trying to become agile by accelerating data-driven decision making often get slowed down due to data estate and tool-set fragmentation. Lakehouse provides an architectural pattern that dramatically reduces time to value by enabling rapid capability build-out, and helping democratize analytics across all levels. Join this demo-rich session to discover how Microsoft cloud provides the best destination for building Lakehouse and analytics on top, powered by Azure Databricks and Power BI.

Databricks: Databricks SQL: Why the Best Serverless Data Warehouse is a Lakehouse

Many organizations rely on complex cloud data architectures that create silos between applications, users and data. This fragmentation makes it difficult to access accurate, up-to-date information for analytics, often resulting in the use of outdated data. Enter the lakehouse, a modern data architecture that unifies data, AI, and analytics in a single location. This session explores why the lakehouse is the best data warehouse, featuring success stories, use cases and best practices from industry experts. You'll discover how to unify and govern business-critical data at scale to build a curated data lake for data warehousing, SQL and BI. Additionally, you'll learn how Databricks SQL can help lower costs and get started in seconds with on-demand, elastic SQL serverless warehouses, and how to empower analytics engineers and analysts to quickly find and share new insights using their preferred BI and SQL tools such as Fivetran, dbt, Tableau, or Power BI.

Databricks: What’s New in Unity Catalog - With Live Demos

Join the Unity Catalog product team and dive into the cutting-edge world of data, analytics and AI governance. With Unity Catalog’s unified governance solution for data, analytics, and AI on any cloud, you’ll discover the latest and greatest enhancements we’re shipping, including fine-grained governance with row/column filtering, new enhancements with automated data lineage and governance for ML assets. In this demo-packed session, You’ll learn how new capabilities in Unity Catalog can further simplify your data governance and accelerated analytics and AI initiatives. Plus, get an exclusive sneak peek at our upcoming roadmap. And don’t forget, you’ll have the chance to ask the product teams themselves any burning questions you have about the best governance solution for the lakehouse. Don’t miss out on this exciting opportunity to level up your data game with Unity Catalog.

Albert Heijn: Albert Heijn's Journey in Continuous Data Processing

Join us for a presentation on the journey in Continuous Data Processing of the Netherlands' leading food retailer. Discover how a microbatching data architecture fuels Albert Heijn's data science success, improving customer satisfaction, operational efficiency, and supporting waste reduction.

One of our key enablers is the meta-driven Delta Lake, a pivotal component of our data infrastructure that empowers us to effectively manage and process data from our vast range of sources.

In this presentation, we will walk you through our journey, sharing our insights, challenges, and the success story of dynamic markdown in the realm of continuous data processing.

Databricks: Deep Dive into the Latest Lakehouse AI Capabilities

With a unified data & ML platform approach, Databricks has a unique approach to machine learning. By breaking down the silos between the data stack, ML stack and DevOps stack, Databricks offers a simplified, faster, and better-governed way to do ML, including integrated feature engineering and governance tooling, end-to-end tracking and lineage of models and data, automatic monitoring, and root cause analysis. All these capabilities, which typically require six different tools and vendors, are all unified on one platform: Databricks. This saves significant time and resources while accelerating innovation. In this session you will learn about: Built-in governance, lineage and monitoring across model, feature and data Unified feature engineering Monitoring across your data and ML assets, with automatic reporting and root cause analysis Deep integration with the data in lakehouse to accelerate the development.

Databricks: Earth Observation and Geospatial Data Processing with the Databricks Lakehouse

Avanade: Road to adopting Unity Catalog

Databricks: Delta Live Tables A to Z: Best Practices for Modern Data Pipelines

Databricks: Introduction to Data Streaming on the Lakehouse

Streaming is the future of all data pipelines and applications. It enables businesses to make data-driven decisions sooner and react faster, develop data-driven applications considered previously impossible, and deliver new and differentiated experiences to customers. However, many organizations have not realized the promise of streaming to its full potential because it requires them to completely redevelop their data pipelines and applications on new, complex, proprietary, and disjointed technology stacks. The Databricks Lakehouse Platform is a simple, unified, and open platform that supports all streaming workloads ranging from ingestion, ETL to event processing, event-driven application, and ML inference. In this session, we will discuss the streaming capabilities of the Databricks Lakehouse Platform and demonstrate how easy it is to build end-to-end, scalable streaming pipelines and applications, to fulfill the promise of streaming for your business.

Databricks: Observability of your Data Platform (Cost and Performance Management)

Heineken: Scaling data platforms at HEINEKEN

Databricks: Maximizing Value From Your Data with Lakehouse AI

Ready to get started with lakehouses, especially around DS/ML? This session is for you. The journey from training models to taking them to production can be quite challenging. Teams face a large variety of issues: siloed data, inconsistent tools, complex infrastructure, and a lack of visibility into their model performance. Learn how the Databricks Lakehouse Platform provides a unified, data-centric ML environment that accelerates and simplifies machine learning lifecycle, with a standardized set of tools, frameworks and governance used across your lakehouse data. In this session, you’ll learn how about lakehouses, and how to: Ingest, prepare and process data on a platform designed to handle production-scale ML training, including LLMs Leverage data science notebooks and MLflow to manually train and track your ML experiments or let AutoML do the experimentation for you Use real-time serving with fast autoscaling to save cost and maintain SLAs Monitor your deployed models for drift and accuracy Manage and govern all data and ML assets with Unity Catalog.

Databricks: Simplifying Lakehouse Observability: Databricks Key Design Goals and Strategies

In this session, we'll explore Databricks vision for simplifying lakehouse observability, a critical component of any successful data, analytics, and machine learning initiatives. By directly integrating observability solutions within the lakehouse, Databricks aims to provide users with the tools and insights needed to run a successful business on top of lakehouse. Our approach is designed to leverage existing expertise and simplify the process of monitoring and optimizing data and ML workflows, enabling teams to deliver sustainable and scalable data and AI applications. Join us to learn more about our key design goals and how Databricks is streamlining lakehouse observability to support the next generation of data-driven applications.

Nederlandse Loterij: A Data-Driven Commitment:
Innovating for Responsible Gaming

Databricks: 5 Guiding Principles to Choose the right Data & AI Technology

AWS: Building Your Lakehouse on AWS to Expand Your Business Capabilities

Celebal: SAP Modernization: The AI Journey with Databricks

Databricks: What is the EU AI Act and how does Databricks help compliancy?

Databricks: Next Gen CX

In today’s data-driven world, teams need to better understand their audience so they can more effectively find and activate them online. If you’re stitching together a bunch of platforms, you might find that fragmented views, legacy technologies and the shift from descriptive to predictive insights are holding your team back. With the Databricks unified data and AI platform, we give you a single copy of data with unified governance and an ever-expanding ecosystem of partners to build the next generation of your customer experience.

Mapiq: Creating a workplace experience platform that scales - Lessons learned from Mapiq’s transition to Databricks

Neo4j: The Art of the Possible - Databricks and Graph Technology at Rabobank

Are you drowning in data but lacking in insight? 80% of business leaders say data is critical in decision-making, yet 41% cite a lack of understanding of data because it is too complex or not accessible enough. You’ll learn how companies are using graph technology to leverage the relationships in their connected data to reveal new ways of solving their most pressing business problems and creating new business value for their enterprises. During this talk Rabobank will explain how Databricks and Neo4j go hand in hand together to create improved insights on their connected data.

Capgemini: How to optimize a milk factory using a Digital Twin

Astellas: How Databricks is Helping Astellas to Build the Future of Healthcare

LTIMindtree: Echo Delta Charlie, Data and AI Strategy for Amplifying Business Insights

Kenvue: Cutting AI Product costs and run time by up to 90%

RTL: Building a local streaming hero with Data & AI

Start Time	End Time	Session
12:45	13:30	Financial Services and Insurance Networking Lunch
13:30	14:10	Financial Risk Avoidance with Bridgefund & Rabobank
14:45	15:25	Advantage Lakehouse: CZ & VGZ
16:00	16:40	Data Leaders Panel: The Future of Financial Services and Insurance

Rabobank: Building a team from scratch that implements and maintains a Transaction Monitoring solution processing billions of records every day

For Rabobank, transaction monitoring is a vital part of our responsibilities as a bank. 3 years ago we started a project to improve our monitoring solution. Today, almost 100 colleagues are working together on a Transaction Monitoring solution developed on Databricks. With an ever-expanding scope, we have a continuous drive and need to improve our way-of-working. I'll talk you through how we started with a small group of people and were able to expand to our current size in such a small time, with billions of records passing our solution every day.

Bridgefund: How Bridgefund builds AI-driven lending and credit risk management solutions on the Lakehouse

In 2018, together with a diverse group of passionate entrepreneurs, we launched the FinTech startup BridgeFund. BridgeFund uses its own technology and smart credit models to evaluate the financial health of businesses. With this, we are able to efficiently offer capital to a market that is not respected by banks.

We have automated the entire process. As a result, entrepreneurs can secure a loan more quickly and efficiently than with traditional lenders. The outcome: a perfect digital experience that truly makes entrepreneurs happy.

CZ: Data Lakehouse at CZ Health Insurances: a transition towards a federated Data & Analytics model

As a leading health insurer in the Netherlands, CZ holds a fundamental belief that every insured individual in need of medical care should have access to it, all while maintaining an affordable premium. Yet, todays’ healthcare landscape is facing challenges around diminishing accessibility to healthcare services and escalating cost of medical care. Data, in all its facets, has become an indispensable asset in addressing these access and affordability concerns that continue to shape the healthcare sector.

VGZ: The journey within VGZ to the value-based data platform

Data Leaders Panel: The Future of Financial Services and Insurance

Start Time	End Time	Session
13:30	14:10	Accelerating Manufacturing on the Data Intelligence Platform
14:15	14:35	IoT on the Lakehouse for Manufacturing
14:45	15:25	Shell: Data Democratization
15:30	15:50	Xebia: Applying Databricks for your Datamesh
16:00	16:40	Data Leaders Panel: The Future of Supply Chain

Start Time	End Time	Session
13:30	14:10	From Raw Data to Data Product in Under 30 Minutes
14:15	14:35	Empowering Renewable Electricity Grids with Flexibility Using Data & AI
14:45	15:25	Deploy your LLM Bot - Part 1
16:00	16:40	Deploy your LLM Bot - Part 2