Speaker: Tareef Kawaf, President, Posit Sofware, PBC

Legacy data warehouses are costly to maintain, unscalable and cannot deliver on data science, ML and real-time analytics use cases. Migrating from your enterprise data warehouse to Databricks lets you scale as your business needs grow and accelerate innovation by running all your data, analytics and AI workloads on a single unified data platform.

In the first part of this session we will guide you through the well-designed process and tools that will help you from the assessment phase to the actual implementation of an EDW migration project. Also, we will address ways to convert PL/SQL proprietary code to an open standard python code and take advantage of PySpark for ETL workloads and Databricks SQL’s data analytics workload power.

The second part of this session will be based on an EDW migration project of SNCF (French national railways); one of the major enterprise customers of Databricks in France. Databricks partnered with SNCF to migrate its real estate entity from Oracle DW and IBM DataStage to Databricks on AWS. We will walk you through the customer context, urgency to migration, challenges, target architecture, nitty-gritty details of implementation, best practices, recommendations, and learnings in order to execute a successful migration project in a very accelerated time frame.

Talk by: Himanshu Arora and Amine Benhamza

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

JetBlue has embarked over the past year on an AI and ML transformation. Databricks has been instrumental in this transformation due to the ability to integrate streaming pipelines, ML training using MLflow, ML API serving using ML registry and more in one cohesive platform. Using real-time streams of weather, aircraft sensors, FAA data feeds, JetBlue operations and more are used for the world's first AI and ML operating system orchestrating a digital-twin, known as BlueSky for efficient and safe operations. JetBlue has over 10 ML products (multiple models each product) in production across multiple verticals including dynamic pricing, customer recommendation engines, supply chain optimization, customer sentiment NLP and several more.

The core JetBlue data science and analytics team consists of Operations Data Science, Commercial Data Science, AI and ML engineering and Business Intelligence. To facilitate the rapid growth and faster go-to-market strategy, the team has built an internal Data Catalog + AutoML + AutoDeploy wrapper called BlueML using Databricks features to empower data scientists including advanced analysts with the ability to train and deploy ML models in less than five lines of code.

Talk by: Derrick Olson and Rob Bajra

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

Snowflake and Databricks both aim to provide data science toolkits for machine learning workflows, albeit with different approaches and resources. While developing ML models is technically possible using either platform, the Hitachi Solutions Empower team tested which solution will be easier, faster, and cheaper to work with in terms of both user experience and business outcomes for our customers. To do this, we designed and conducted a series of experiments with use cases from the TPCx-AI benchmark standard. We developed both single-node and multi-node versions of these experiments, which sometimes required us to set up separate compute infrastructure outside of the platform, in the case of Snowflake. We also built datasets of various sizes (1GB, 10GB, and 100GB), to assess how each platform/node setup handles scale.

Based on our findings, on the average, Databricks is faster, cheaper, and easier to use for developing machine learning models, and we use it exclusively for data science on the Empower platform. Snowflake’s reliance on third party resources for distributed training is a major drawback, and the need to use multiple compute environments to scale up training is complex and, in our view, an unnecessary complication to achieve best results.

Talk by: Michael Green and Don Scott

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

Every year, billions of dollars are lost due to water risks from storms, floods, and droughts. Water data scarcity and excess are issues that risk models cannot overcome, creating a world of uncertainty. Divirod is building a platform of water data by normalizing diverse data sources of varying velocity into one unified data asset. In addition to publicly available third-party datasets, we are rapidly deploying our own IoT sensors. These sensors ingest signals at a rate of about 100,000 messages per hour into preprocessing, signal-processing, analytics, and postprocessing workloads in one spark-streaming pipeline to enable critical real-time decision-making processes. By leveraging streaming architecture, we were able to reduce end-to-end latency from tens of minutes to just a few seconds.

We are leveraging Delta Lake to provide a single query interface across multiple tables of this continuously changing data. This enables data science and analytics workloads to always use the most current and comprehensive information available. In addition to the obvious schema transformations, we implement data quality metrics and datum conversions to provide a trustworthy unified dataset.

Talk by: Adam Wilson and Heiko Udluft

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV 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

With the proliferation of video content and flourishing consumer demand, there is an enormous opportunity for customer-centric video entertainment companies to use data and analytics to understand what their viewers want and deliver more of the content that that meets their needs.

At DIRECTV, our Data Science Center of Excellence is constantly looking to push the boundary of innovation in how we can better and more quickly understand the needs of our customers and leverage those actionable insights to deliver business impact. One way in which we do so is through the development of Viewership Personas with cluster analysis at scale to group our customers by the types of content they enjoy watching. This process is significantly accelerated by a unified feature store which contain a wide array of features that captures key information on viewing preferences.

This talk will focus on how the DIRECTV Data Science team utilizes Databricks to help develop a unified feature store, and learn how we leverage the feature store to accelerate the process of running machine learning algorithms to find meaningful viewership clusters.

Talk by: Malav Shah,Taylor Hosbach

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

Organizations are going through a critical phase of data infrastructure modernization, laying the foundation for the future, and adapting to support growing data and AI needs. Organizations that embraced cloud data warehouses (CDW) such as Snowflake have ended up trying to use a data warehousing tool for ETL pipelines and data science. This created unnecessary complexity and resulted in poor performance since data warehouses are optimized for SQL-based analytics only.

Realizing the limitation and pain with cloud data warehouses, organizations are turning to a lakehouse-first architecture. Though a cloud platform to cloud platform migration should be relatively easy, the breadth of the Databricks platform provides flexibility and hence requires careful planning and execution. In this session, we present the migration methodology, technical approaches, automation tools, product/feature mapping, a technical demo and best practices using real-world case studies for migrating data, ELT pipelines and warehouses from Snowflake to Databricks.

Talk by: Satish Garla and Ramachandran Venkat

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

WorldQuant Predictive’s customers rely on our predictions to understand how changing world and market conditions will impact decisions to be made. Speed is critical, and so are accuracy and resilience. To that end, our data team built a modern, automated MLOps data flow using Databricks as a key part of our data science tooling, and integrated with Immuta to provide automated data security and access control.

In this session, we will share details of how we used policy-as-code to support our globally distributed data science team with secure data sharing, testing, validation and other model quality requirements. We will also discuss our data science workflow that uses Databricks-hosted MLflow together with an Immuta-backed custom feature store to maximize speed and quality of model development through automation. Finally, we will discuss how we deploy the models into our customized serverless inference environment, and how that powers our industry solutions.

Talk by: Tyler Ditto

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 the spring and summer of 2022, monkeypox was detected in the United States and quickly spread throughout the country. To contain and mitigate the spread of the disease in Washington State, the Washington Department of Health data science team used the Databricks platform to develop a modeling pipeline that employed statistical and mathematical techniques to forecast the course of the monkeypox outbreak throughout the state. These models provided actionable information that helped inform decision making and guide the public health response to the outbreak.

We used contact-tracing data, standard line-lists, and published parameters to train a variety of time-series forecasting models, including an ARIMA model, a Poisson regression, and an SEIR compartmental model. We also calculated the daily R-effective rate as an additional output. The compartmental model best fit the reported cases when tested out of sample, but the statistical models were quicker and easier to deploy and helped inform initial decision-making. The R-effective rate was particularly useful throughout the effort.

Overall, these efforts highlighted the importance of rapidly deployable and scalable infectious disease modeling pipelines. Public health data science is still a nascent field, however, so common best practices in other industries are often-times novel approaches in public health. The need for stable, generalizable pipelines is crucial. Using the Databricks platform has allowed us to more quickly scale and iteratively improve our modeling pipelines to include other infectious diseases, such as influenza and RSV. Further development of scalable and standardized approaches to disease forecasting at the state and local level is vital to better informing future public health response efforts.

Talk by: Matthew Doxey

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

For video games, Data Science solutions can be applied throughout players' lifecycle, from Adtech, LTV forecasting, In-game economic system monitoring to experimentation. Databricks is used as a data and computation foundation to power these data science solutions, enabling data scientists to easily develop and deploy these solutions for different use cases.

In this session, we will share insights on how Databricks-powered data science solutions drive game growth and improve player experiences using different advanced analytics, modeling, experimentation, and causal inference methods. We will introduce the business use cases, data science techniques, as well as Databricks demos.

Talk by: Zhenyu Zhao and Shuo Chen

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

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

Ask questions from a panel of data science experts who have deployed LLMs and AI models into production.

Talk by: David Talby, Conor Murphy, Cheng Yin Eng, Sam Raymond, and Colton Peltier

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

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

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 the fast-paced world of data science and AI, we will explore how large language models (LLMs) can elevate the development process of Apache Spark applications.

We'll demonstrate how LLMs can simplify SQL query creation, data ingestion, and DataFrame transformations, leading to faster development and clearer code that's easier to review and understand. We'll also show how LLMs can assist in creating visualizations and clarifying data insights, making complex data easy to understand.

Furthermore, we'll discuss how LLMs can be used to create user-defined data sources and functions, offering a higher level of adaptability in Apache Spark applications.

Our session, filled with practical examples, highlights the innovative role of LLMs in the realm of Apache Spark development. We invite you to join us in this exploration of how these advanced language models can drive innovation and boost efficiency in the sphere of data science and AI.

Talk by: Gengliang Wang and Allison Wang

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

DoorDash was using a data warehouse but found that they needed more data transparency, lower costs, and the ability to handle streaming data as well as batch data. With an engineering team rooted in big data backgrounds at Uber and LinkedIn, they moved to a Lakehouse architecture intuitively, without knowing about the term. In this session, learn more about how they arrived at that architecture, the process of making the move, and the results they have seen. While addressing both data analysts and data scientists from their lakehouse, this session will focus on their machine learning operations, and how their efficiencies are enabling them to tackle more advanced use cases such as NLP and image classification.

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

The Department of Veterans Affairs (VA) is home to over 420,000 employees, provides health care for 9.16 million enrollees and manages the benefits of 5.75 million recipients. The VA also hosts an array of financial management, professional, and administrative services at their Financial Service Center (FSC), located in Austin, Texas. The FSC is divided into various service groups organized around revenue centers and product lines, including the Data Analytics Service (DAS). To support the VA mission, in 2021 FSC DAS continued to press forward with their cloud modernization efforts, successfully achieving four key accomplishments:

Office of Community Care (OCC) Financial Time Series Forecast - Financial forecasting enhancements to predict claims CFO Dashboard - Productivity and capability enhancements for financial and audit analytics Datasets Migrated to the Cloud - Migration of on-prem datasets to the cloud for down-stream analytics (includes a supply chain proof-of-concept) Data Science Hackathon - A hackathon to predict bad claims codes and demonstrate DAS abilities to accelerate a ML use case using Databricks AutoML

This talk discusses FSC DAS’ cloud and data science modernization accomplishments in 2021, lessons learned, and what’s ahead.

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

Many federal agencies use SAS software for critical operational data processes. While SAS has historically been a leader in analytics, it has often been used by data analysts for ETL purposes as well. However, modern data science demands on ever-increasing volumes and types of data require a shift to modern, cloud architectures and data management tools and paradigms for ETL/ELT. In this presentation, we will provide a case study at Centers for Medicare and Medicaid Services (CMS) detailing the approach and results of migrating a large, complex legacy SAS process to modern, open-source/open-standard technology - Spark SQL & Databricks – to produce results ~75% faster without reliance on proprietary constructs of the SAS language, with more scalability, and in a manner that can more easily ingest old rules and better govern the inclusion of new rules and data definitions. Significant technical and business benefits derived from this modernization effort are described in this session.

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

We all live in the exciting times and the hype of Distributed Data Mesh (or just mess). This talk will cover a couple architectural and organizational approaches on achieving Distributed Data Mesh, which is essentially a combination of mindset, fully automated infrastructure, continuous integration for data pipelines, dedicated team collaborative environments, and security enforcement. As a Data Leader, you’ll learn what kinds of things you’d need to pay attention to, when starting (or reviving) a modern Data Engineering and Data Science strategy and how Databricks Unity Catalog may help you automating that. As DevOps, you’ll learn about the best practices and pitfalls of Continuous Deployment on Databricks With Terraform and Continuous Integration with Databricks Repos. You’ll be excited how you can automate Data Security with Unity Catalog and Terraform. As a Data Scientist, you’ll learn how you can get relevant infrastructure into “production” relatively faster.

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

Are you considering converting some batch daily pipelines to a realtime system? Perhaps restating multiple days of batch data is becoming unscalable for your pipelines. Maybe a short SLA is music to your stakeholders' ears. If you're flink-curious or possibly just sick of pondering your late arriving data, this discussion is for you.

On the Streaming Data Science and Engineering team at Netflix we support business-critical daily batch, hourly batch, incremental, and realtime pipelines with a rotating on-call system. In this presentation I'll discuss tradeoffs we experience between these systems with an emphasis on operational support when things go sideways. I'll also share some learnings about "goodness of fit" per processing type amongst various workloads with an eye for keeping your data timely and your colleagues sane.

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