Insights into Infosys' Topaz AI First Innovations including AI-enabled Analytics and AI-enabled Automation to help clients in significant cost savings, improved efficiency and customer experience across industry segments.

Talk by: Neeraj Dixit

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Cloud technologies have revolutionized global data access across a number of industries. However, many enterprise organizations face challenges in adopting these technologies effectively, as comprehensive cloud data governance strategies and solutions are complex and evolving – particularly in hybrid or multicloud scenarios involving multiple third parties. KPMG and S&P Global have harnessed the power of Databricks Lakehouse to create a novel approach.

By integrating Unity Catalogue, Delta Sharing, and the KPMG Modern Data Platform, S&P Global has enabled scalable, transformative cross-enterprise data sharing and governance. This demonstration highlights a collaboration between S&P Global Sustainable1 (S1) ESG program and the KPMG ESG Analytics Accelerators to enable large-scale SFDR ESG portfolio analytics. Join us to discover our solution that drives transformative change, fosters data-driven decision-making, and bolsters sustainability efforts in a wide range of industries.

Talk by: Niels Hanson,Dennis Tally

Here’s more to explore: A New Approach to Data Sharing: https://dbricks.co/44eUnT1

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

In this presentation, Matillion’s Sarah Pollitt, Group Product Manager for ETL, will discuss how you can use Matillion to load data from popular data sources such as Salesforce, SAP, and over a hundred out-of-the-box connectors into your data lakehouse. You can quickly transform this data using powerful tools like Matillion or dbt, or your own custom notebooks, to derive valuable insights. She will also explore how you can run streaming pipelines to ensure real-time data processing, and how you can extract and manage this data using popular governance tools such as Alation or Collibra, ensuring compliance and data quality. Finally, Sarah will showcase how you can seamlessly integrate this data into your analytics tools of choice, such as Thoughtspot, PowerBI, or any other analytics tool that fits your organization's needs.

Talk by: Rick Wear

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

In this talk, we will dive into the powerful analytics combination of Databricks and Sigma for data science and machine learning use cases. Databricks offers a scalable and flexible platform for building and deploying machine learning models at scale, while Sigma enhances this framework with real-time data insights, analysis, and visualization capabilities. Going a step further, we will demonstrate how input tables can be utilized from Sigma to create seamless workflows in Databricks for data science and machine learning. From this workflow, business users can leverage data science and ML models to do ad-hoc analysis and make data-driven decisions.

This talk is perfect for data scientists, data analysts, business users, and anyone interested in harnessing the power of Databricks and Sigma to drive business value. Join us and discover how these two platforms can revolutionize the way you analyze and leverage your data.

Talk by: Mitch Ertle and Greg Owen

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

With the rapid proliferation of consumer data privacy laws across the world, it is becoming a strict requirement for data organizations to be mindful of data privacy risks. Privacy violation fines are reaching record highs and will only get higher as governments continue to crack down on the runaway abuse of user data. To continue producing value without becoming a liability, data systems must include privacy protections at a foundational level.

The most practical way to do this is to enable privacy as code, shifting privacy left and including it as a foundational part of the organization's software development life cycle. The promise of privacy as code is that data organizations can be liberated from inefficient, manual workflows for producing the compliance deliverables their legal teams need, and instead ship at speed with pre-defined privacy guardrails built into the structure of their preferred workflows.

Despite being an emerging and complex problem, there are already powerful open source tools available designed to help organizations of all sizes achieve this outcome. Fides is an open source privacy as code tool, written in Python and Typescript, that is engineered to tackle a variety of privacy problems throughout the application lifecycle. The most relevant feature for data organizations is the ability to annotate systems and their datasets with data privacy metadata, thus enabling automatic rejection of dangerous or illegal uses. Fides empowers data organizations to be proactive, not reactive, in terms of protecting user privacy and reducing organizational risk. Moving forward data privacy will need to be top of mind for data teams.

Talk by: Thomas La Piana

Here’s more to explore: Data, Analytics, and AI Governance: https://dbricks.co/44gu3YU

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Vector databases such as ElasticSearch and Pinecone offer fast ingestion and querying on vector embeddings with ANNs. However, they typically do not decouple compute and storage, making them hard to integrate in production data stacks. Because data storage in these databases is expensive and not easily accessible, data teams typically maintain ETL pipelines to offload historical embedding data to blob stores. When that data needs to be queried, they get loaded back into the vector database in another ETL process. This is reminiscent of loading data from OLTP database to cloud storage, then loading said data into an OLAP warehouse for offline analytics.

Recently, “lakehouse” offerings allow direct OLAP querying on cloud storage, removing the need for the second ETL step. The same could be done for embedding data. While embedding storage in blob stores cannot satisfy the high TPS requirements in online settings, we argue it’s sufficient for offline analytics use cases like slicing and dicing data based on embedding clusters. Instead of loading the embedding data back into the vector database for offline analytics, we propose direct processing on embeddings stored in Parquet files in Delta Lake. You will see that offline embedding workloads typically touch a large portion of the stored embeddings without the need for random access.

As a result, the workload is entirely bound by network throughput instead of latency, making it quite suitable for blob storage backends. On a test one billion vector dataset, ETL into cloud storage takes around one hour on a dedicated GPU instance, while batched nearest neighbor search can be done in under one minute with four CPU instances. We believe future “lakehouses” will ship with native support for these embedding workloads.

Talk by: Tony Wang and Chang She

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp Databricks named a Leader in 2022 Gartner® Magic QuadrantTM CDBMS: https://dbricks.co/3phw20d

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Data science and analytics teams are unique. Large and small corporations want to build and manage analytics teams to convert their data and analytic assets into revenue and competitive advantage, but many are failing before they make their first hire. In this session, the audience will learn how to structure, hire, manage and grow an analytics team. Organizational structure, project and program portfolios, neurodiversity, developing talent, and more will be discussed.

Questions and discussion will be encouraged and engaged in. The audience will leave with a deeper understanding of how to succeed in turning data and analytics into tangible results.

Talk by: John Thompson

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Using Databricks, we built a “Unified Talent Solution” backed by a robust data and AI engine for analyzing skills of a combined pool of permanent employees, contractors, part-time employees and vendors, inferring skill gaps, future trends and recommended priority areas to bridge talent gaps, which ultimately greatly improved operational efficiency, transparency, commercial model, and talent experience of our client. We leveraged a variety of ML algorithms such as boosting, neural networks and NLP transformers to provide better AI-driven insights.

One inevitable part of developing these models within a typical DS workflow is iteration. Databricks' end-to-end ML/DS workflow service, MLflow, helped streamline this process by organizing them into experiments that tracked the data used for training/testing, model artifacts, lineage and the corresponding results/metrics. For checking the health of our models using drift detection, bias and explainability techniques, MLflow's deploying, and monitoring services were leveraged extensively.

Our solution built on Databricks platform, simplified ML by defining a data-centric workflow that unified best practices from DevOps, DataOps, and ModelOps. Databricks Feature Store allowed us to productionize our models and features jointly. Insights were done with visually appealing charts and graphs using PowerBI, plotly, matplotlib, that answer business questions most relevant to clients. We built our own advanced custom analytics platform on top of delta lake as Delta’s ACID guarantees allows us to build a real-time reporting app that displays consistent and reliable data - React (for front-end), Structured Streaming for ingesting data from Delta table with live query analytics on real time data ML predictions based on analytics data.

Talk by: Nitu Nivedita

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

A global franchise retailer was struggling to understand the value of its employees and had not fostered a data-driven enterprise. During the journey to use facts as the basis for decision making, Databricks became the facilitator of DataMesh and created the pipelines, analytics and source engine for a three-layer — bronze, silver, gold — lakehouse that supports the HR domain and drives the integration of multiple additional domains: sales, customer satisfaction, product quality and more. In this talk, we will walk through:

• The business rationale and drivers
• The core data sources
• The data products, analytics and pipelines
• The adoption of Unity Catalog for data privacy compliance /adherence and data management
• Data quality metrics

Join us to see the analytic product and the design behind this innovative view of employees and their business outcomes.

Talk by: Rebecca Bucnis

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp Databricks named a Leader in 2022 Gartner® Magic QuadrantTM CDBMS: https://dbricks.co/3phw20d

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Join the panel to hear how Avanade is helping clients enable real-time analytics and tackle the people and process problems that accompany technology, powered by Azure Databricks.

Talk by: Thomas Kim, Dael Williamson, Zoé Durand

Here’s more to explore: Data, Analytics, and AI Governance: https://dbricks.co/44gu3YU

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Fivetran helps Enterprise and Commercial companies improve the efficiency of their data movement, infrastructure, and analysis by providing a secure, scalable platform for high-volume data movement. In this fireside chat, we will dive into the pain points that drove Catalyst, a cloud-based platform that helps software companies grow revenue with advanced insights and workflows that strengthen customer adoption, retention, expansion and advocacy, to begin their search for a partnership that would automate and simplify data management along with the pivotal success driven by the implementation of Fivetran and Databricks. 

Discover how together Fivetran and Databricks:

• Deliver scalable, real-time analytics to customers with minimal configuration and centralize customer data into customer success tools.
• Improve Catalyst’s visibility into customer health, opportunities, and risks across all teams.
• Turn data into revenue-driving insights around digital customer behavior with improved targeting and Ai/ Machine learning.
• Provide a robust and scalable data infrastructure that supports Catalyst’s growing data needs, with improvements in data availability, data quality, and overall efficiency in data operations.

Talk by: Edward Chiu and Lauren Schwartz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Learn how Rec Room, a fast-growing augmented and virtual reality software startup, is saving 50% of their engineering team's time by using Databricks and RudderStack to power real-time analytics and insights for their 85 million gaming customers.

In this session, you will walk through a step-by-step explanation of how Rec Room set up efficient processes for ingestion into their data lakehouse, transformation, reverse-ETL and product analytics. You will also see how Rec Room is using incremental materialization of tables to save costs and establish an uptime of close to 100%.

Talk by: Albert Hu and Lewis Mbae

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Get ready to accelerate your data and AI collaboration game with the Databricks product team. Join us as we build the next generation of secure data collaboration capabilities on the lakehouse. Whether you're just starting your data sharing journey or exploring advanced data collaboration features like data cleanrooms, this session is tailor-made for you.

In this demo-packed session, you'll discover what’s new in Delta Sharing including dynamic and materialized views for sharing, sharing other assets such as notebooks, ML models, new Delta Sharing open source connectors for the tools of your choice, and updates to Databricks cleanroom. Learn how lakehouse is the perfect solution for your data and AI collaboration requirements, across clouds, regions and platforms and without any vendor lock-in. Plus, you'll get a peek into our upcoming roadmap. Ask any burning questions you have for our expert product team as they build a collaborative lakehouse for data, analytics and AI.

Talk by: Erika Ehrli, Kelly Albano, and Xiaotong Sun

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

The widely-adopted combination of Power BI and Databricks has been a game-changer in providing a comprehensive solution for modern data analytics. In this session, you’ll learn how self-service analytics combined with the Databricks Lakehouse Platform can allow users to make better-informed decisions by unlocking insights hidden in complex data. We’ll provide practical examples of how organizations have leveraged these technologies together to drive digital transformation, lower total cost of ownership (TCO), and increase revenue. By the end of the presentation and demo, you’ll understand how Power BI and Databricks can help drive real-time insights at scale for organizations in any industry.

Talk by: Bob Zhang and Mahesh Prakriya

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Chesapeake Regional Information System for our Patients (CRISP), a nonprofit healthcare information exchange (HIE), initially partnered with Slalom to build a Databricks data lakehouse architecture in response to the analytics demands of the COVID-19 pandemic, since then they have expanded the platform to additional use cases. Recently they have worked together to engineer streaming data pipelines to process healthcare messages, such as HL7, to help CRISP become vendor independent.

This session will focus on the improvements CRISP has made to their data lakehouse platform to support streaming use cases and the impact these changes have had for the organization. We will touch on using Databricks Auto Loader to efficiently ingest incoming files, ensuring data quality with Delta Live Tables, and sharing data internally with a SQL warehouse, as well as some of the work CRISP has done to parse and standardize HL7 messages from hundreds of sources. These efforts have allowed CRISP to stream over 4 million messages daily in near real-time with the scalability it needs to continue to onboard new healthcare providers so it can continue to facilitate care and improve health outcomes.

Talk by: Andy Hanks and Chris Mantz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Get ready to take a deep dive into Unity Catalog and explore how it can simplify data, analytics and AI governance across multiple clouds. In this session, take a deep dive into Unity Catalog and the expert Databricks team will guide you through a hands-on demo, showcasing the latest features and best practices for data governance. You'll learn how to master Unity Catalog and gain a practical understanding of how it can streamline your analytics and AI initiatives. Whether you're migrating from Hive Metastore or just looking to expand your knowledge of Unity Catalog, this session is for you. Join us for a practical, hands-on deep dive into Unity Catalog and learn how to achieve seamless data governance while following best practices for data, analytics and AI governance.

Talk by: Zeashan Pappa and Ifigeneia Derekli

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksin

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.

Talk by: Jibreal Hamenoo and Ori Zohar

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Join Databricks' Distinguished Principal Engineer Michael Armbrust for a technical deep dive into how Delta Live Tables (DLT) reduces the complexity of data transformation and ETL. Learn what’s new; what’s coming; and how to easily master the ins-and-outs of DLT.

Michael will describe and demonstrate:

• What’s new in Delta Live Tables (DLT) - Enzyme, Enhanced Autoscaling, and more
• How to easily create and maintain your DLT pipelines
• How to monitor pipeline operations
• How to optimize data for analytics and ML
• Sneak Peek into the DLT roadmap

Talk by: Michael Armbrust

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

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.

Talk by: Paul Roome

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Hear from two guests. First, Simon Whiteley (co-owner, Advancing Analytics) on his reaction to industry announcements, where he sees the industry heading, and an introduction to his community at Advancing Analytics. Second guest, Matteo Quattrocchi (Director - Policy, EMEA at BSA | The Software Alliance) on the current state of AI policies - by international governments, global committees, and individual companies.. Hosted by Ari Kaplan (Head of Evangelism, Databricks) and Pearl Ubaru (Sr Technical Marketing Engineer, Databricks)