As one of the world's largest fast-food chains, McDonald's manages massive amounts of data for customers, sales, inventory, marketing, and more. And at that scale, ensuring the accuracy, reliability, and quality of all that data comes with a new set of complex challenges. Developing manual data quality checks with legacy tools was too time consuming and resource-intensive, requiring developer support and data domain expertise. Ultimately, they struggled to scale their checks across their enterprise data pipelines.

Join our featured customer session, where you’ll hear from Matt Sandler, Senior Director of Data and Analytics at McDonald’s, about how they use the Lightup Deep Data Quality platform to deploy pushdown data quality checks in minutes, not months — without developer support. From reactive to proactive, the McDonald’s data team leverages Lightup to scale their data quality checks across petabytes of data, ensuring high-quality data and reliable analytics for their products and services. During the session, you’ll learn:

• The key challenges of scaling Data Quality checks with legacy tools
• Why fixing data quality (fast) was critical to launching their new loyalty program and personalized marketing initiatives
• How quickly McDonald’s ramped up with Lightup, transforming their data quality struggles into success

After the session, you’ll understand:

• Why McDonald’s phased out their legacy Data Quality tools
• The benefits of using pushdown data quality checks, AI-powered anomaly detection, and incident alerts
• Best practices for scaling data quality checks in your own organization

Talk by: Matt Sandler and Manu Bansal

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

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Composable analytics is the next progression of business intelligence. We will discuss how current analytics rely on two key principles: composability and agility. Through modularizing our analytics capabilities, we can rapidly “compose” new data applications. An organization uses these building blocks to deliver customized analytics experiences at a customer level.

This session will orientate business intelligence leaders to composable data and analytics.

• How data teams can use composable analytics to decrease application development time.
• How an organization can leverage existing and new tools to maximize value-based, data-driven insights.
  • Requirements for effectively deploying composable analytics.
  • Utilizing no, low-code and high-code analytics capabilities.
  • Extracting full value from your customer data and metadata.
  • Leveraging analytics building blocks to create new products and revenue streams.

Talk by: Scott Castle

Here’s more to explore: Why the Data Lakehouse Is Your next Data Warehouse: https://dbricks.co/3Pt5unq Lakehouse Fundamentals Training: https://dbricks.co/44ancQs

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

This session begins with data warehouse trivia and lessons learned from production implementations of multicloud data architecture. You will learn to design future-proof low latency data systems that focus on openness and interoperability. You will also gain a gentle introduction to Cloud FinOps principles that can help your organization reduce compute spend and increase efficiency. 

Most enterprises today are multicloud. While an assortment of low-code connectors boasts the ability to make data available for analytics in real time, they post long-lasting challenges:

• Inefficient EDW targets
• Inability to evolve schema
• Forbiddingly expensive data exports due to cloud and vendor lock-in

The alternative is an open data lake that unifies batch and streaming workloads. Bronze landing zones in open format eliminate the data extraction costs required by proprietary EDW. Apache Spark™ Structured Streaming provides a unified ingestion interface. Streaming triggers allow us to switch back and forth between batch and stream with one-line code changes. Streaming aggregation enables us to incrementally compute on data that arrives near each other.

Specific examples are given on how to use Autoloader to discover newly arrived data and ensure exactly once, incremental processing. How DLT can be configured effectively to further simplify streaming jobs and accelerate the development cycle. How to apply SWE best practices to Workflows and integrate with popular Git providers, either using the Databricks Project or Databricks Terraform provider. 

Talk by: Christina Taylor

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

Data Engineers love data and business users need outcomes. How do we cross the chasm? While there is no dearth of data in today’s world, managing and analyzing large datasets can be daunting. Additionally, data may lose its value over time. It needs to be analyzed and acted upon quickly, to accelerate decision-making, and help realize business outcomes faster. 

Take a deep dive into the future of the data economy and learn how to drive 50 times faster time to value. Hear from United Airlines how they leveraged Gathr to process massive volumes of complex digital interactions and operational data, to create breakthroughs in operations and customer experience, in real time.

The session will feature a live-demo, showcasing how enterprises from across domains leverage Gathr’s machine learning powered zero-code applications for ingestion, ETL, ML, XOps, Cloud Cost Control, Business Process Automation, and more – to accelerate their journey from data to outcomes, like never before.

Talk by: Sameer Bhide and Sarang Bapat

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/databricksin

A client recently asked to optimize their batch and streaming workloads. It happened to be aggregations using DataFrame.groupby operation with a custom Scala UDAF over a data stream from Kafka. Just a single simple-looking request that turned itself up into a a-few-month-long hunt to find a more performant query execution planning than ObjectHashAggregateExec that kept falling back to a sort-based aggregation (i.e., the worst possible aggregation runtime performance). It quickly taught us that an aggregation using a custom Scala UDAF cannot be planned other than ObjectHashAggregateExec but at least tasks don't always have to fall back. And that's just batch workloads. When you throw in streaming semantics and think of the different output modes, windowing and streaming watermark optimizing aggregation can take a long time to do right.

Talk by: Jacek Laskowski

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

An enterprise lakehouse contains many different datasets which are related to different sources and might belong to different business units. These datasets can span across hundreds of tables, and each table has a different schema, and those schemas evolve over time. The cyber security domain is a good example where datasets come from many different source systems and land in the lakehouse. With such a complex dataset ecosystem, answers to simple questions like “Have we ever detected this IP address?” or “Which columns contain IP addresses?” can become impractical and expensive.

DiscoverX can automate the discovery of all columns that might contain specific patterns, (e.g., IP addresses, MAC addresses, fully qualified domain names, etc.) and automatically generate search and indexing queries that span across multiple tables and columns.

Talk by: Erni Durdevic and David Tempelmann

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

Delta Lake is an open-source storage format that can be ideally used for storing large-scale datasets, which can be used for single-node and distributed training of deep learning models. Delta Lake storage format gives deep learning practitioners unique data management capabilities for working with their datasets. The challenge is that, as of now, it’s not possible to use Delta Lake to train PyTorch models directly.

PyTorch community has recently introduced a Torchdata library for efficient data loading. This library supports many formats out of the box, but not Delta Lake. This talk will demonstrate using the Delta Lake storage format for single-node and distributed PyTorch training using the torchdata framework and standalone delta-rs Delta Lake implementation.

Talk by: Michael Shtelma

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

Tune into DoorDash's journey to migrate from a flaky ETL system with 24-hour data delays, to standardizing a CDC streaming pattern across more than 150 databases to produce near real-time data in a scalable, configurable, and reliable manner.

During this journey, understand how we use Delta Lake to build a self-serve, read-optimized data lake with data latencies of 15, whilst reducing operational overhead. Furthermore, understand how certain tradeoffs like conceding to a non-real-time system allow for multiple optimizations but still permit for OLTP query use-cases, and the benefits it provides.

Talk by: Ivan Peng and Phani Nalluri

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

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

Geospatial data analysis is critical to understanding the impact of agricultural operations on environmental sustainability with respect to water quality, soil health, greenhouse gasses, and more. Outside of a few specialized software products, however, support for spatial data types is often limited or missing from analytics and visualization platforms. In this session, we show how Truterra is using Databricks, Apache Sedona, and R to analyze spatial data at scale. Additionally, learn how Truterra uses spatial insights to educate and promote practices that optimize profitability, sustainability, and stewardship outcomes at the farm.

In this session, you will see how Databricks and Apache Sedona are used to process large spatial datasets including field, watershed, and hydrologic boundaries. You will see dynamic widgets, SQL and R used in tandem to generate map visuals, display them, and enable download all from a Databricks notebook.

Talk by: Nara Khou and Cort Lunke

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 to apply DataSecOps patterns powered by Terraform to Unity Catalog to scale your governance efforts and support your organizational data usage.

Talk by: Zeashan Pappa and Deepak Sekar

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

Stepping into this brave new digital world we are certain that data will be a central product for many organizations. The way to convey their knowledge and their assets will be through data and analytics. Delta Sharing was the world's first open protocol for secure and scalable real-time data sharing. Through our customer conversations, there is a lot of anticipation of how Delta Sharing can be extended to non-tabular assets, such as machine learning experiments and models.

In this session, we will cover how we extended the Delta Sharing protocol to other sharing workflows, enabling sharing of ML models, arbitrary files and more. The development resulted in Arcuate, a Databricks Labs project with a data sharing flavor. The session will start with the high-level approach and how it can be extended to cover other similar use cases. It will then move to our implementation and how it integrates seamlessly with Databricks-managed Delta Sharing server and notebooks. We finally conclude with lessons learned, and our visions for a future of data sharing and beyond

Talk by: Vuong Nguyen and Milos Colic

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 session, we will discuss how real-time data streaming can be used to gain insights into user behavior and preferences, and how this data is being used to provide personalized content and recommendations on Discovery+. We will examine techniques that enables faster decision making and insights on accurate real time data including data masking and data validation. To enable a wide set of data consumers from data engineers to data scientists to data analysts, we will discuss how Unity Catalog is leveraged for secure data access and sharing while still allowing teams flexibility.

Operating at this scale requires examining the value being created by the data being processed and optimizing along the way and we will share some of our success in this area.

Talk by: Deepa Paranjpe

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

Amgen has invested in building modern, cloud-native enterprise data and analytics platforms over the past few years with a focus on tech rationalization, data democratization, overall user experience, increase reusability, and cost-effectiveness. One of these platforms is our Enterprise Data Fabric which focuses on pulling in data across functions and providing capabilities to integrate and connect the data and govern access. For a while, we have been trying to set up robust data governance capabilities which are simple, yet easy to manage through Databricks. There were a few tools in the market that solved a few immediate needs, but none solved the problem holistically. For use cases like maintaining governance on highly restricted data domains like Finance and HR, a long-term solution native to Databricks and addressing the below limitations was deemed important:

The way these tools were set up, allowed the overriding of a few security policies

• Tools were not UpToDate with the latest DBR runtime
• Complexity of implementing fine-grained security
• Policy management – AWS IAM + In tool policies

To address these challenges, and for large-scale enterprise adoption of our governance capability, we started working on UC integration with our governance processes. With an aim to realize the following tech benefits:

• Independent of Databricks runtime
• Easy fine-grained access control
• Eliminated management of IAM roles
• Dynamic access control using UC and dynamic views

Today, using UC, we have to implement fine-grained access control & governance for the restricted data of Amgen. We are in the process of devising a realistic migration & change management strategy across the enterprise.

Talk by: Lakhan Prajapati and Jaison Dominic

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

Lakehouse architecture has become a valuable solution for unifying data processing for AI, but faces limitations in maximizing data’s full potential. Additional data infrastructure is helpful for strengthening data consolidation and data connectivity with third-party sources, which are necessary for building full data sets for accurate audience modeling. 

In this session, LiveRamp will demonstrate to data and analytics decision-makers how to build on the Lakehouse architecture with extensions for collaborative identity graph construction, including how to simplify and improve data enrichment, data activation, and data collaboration. LiveRamp will also introduce a complete data marketplace, which enables easy, pseudonymized data enhancements that widen the attribute set for better behavioral model construction.

With these techniques and technologies, enterprises across financial services, retail, media, travel, and more can safely unlock partner insights and ultimately produce more accurate inputs for personalization engines, and more engaging offers and recommendations for customers.

Talk by: Erin Boelkens and Shawn Gilleran

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

Data with low latency is important for real-time incident analysis and metrics. Though we have up-to-date data in OLTP databases, they cannot support those scenarios. Data need to be replicated to a data warehouse to serve queries using GroupBy and Join across multiple tables from different systems. At Coinbase, we designed SOON (Spark cOntinuOus iNgestion) based on Kafka, Kafka Connect, and Apache Spark™ as an incremental table replication solution to replicate tables of any size from any database to Delta Lake in a timely manner. It also supports Kafka events ingestion naturally.

SOON incrementally ingests Kafka events as appends, updates, and deletes to an existing table on Delta Lake. The events are grouped into two categories: CDC (change data capture) events generated by Kafka Connect source connectors, and non-CDC events by the frontend or backend services. Both types can be appended or merged into the Delta Lake. Non-CDC events can be in any format, but CDC events must be in the standard SOON CDC schema. We implemented Kafka Connect SMTs to transform raw CDC events into this standardized format. SOON unifies all streaming ingestion scenarios such that users only need to learn one onboarding experience and the team only needs to maintain one framework.

We care about the ingestion performance. The biggest append-only table onboarded has ingress traffic at hundreds of thousands events per second; the biggest CDC-merge table onboarded has a snapshot size of a few TBs and CDC update traffic at hundreds of thousands events per second. A lot of innovative ideas are incorporated in SOON to improve its performance, such as min-max range merge optimization, KMeans merge optimization, no-update merge for deduplication, generated columns as partitions, etc.

Talk by: Chen Guo

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

Controlling permissions for accessing data assets can be messy, time consuming, and usually a combination of both. The teams responsible for creating the business rules that govern who should have access to what data are usually different from the teams responsible for administering the grants to achieve that access. On the other side of the equation, the end user who needs access to a data asset may be left waiting for grants to be made as the decision is passed between teams. That is, if they even know the correct path to getting access in the first place.

Separating the concerns of managing data governance at a business level and implementing data governance at an engineering level is the best way to clarify data access permissions. In practice, this involves building systems to enable data governance enforcement based on business rules, with little to no understanding of the individual system where the data lives.

In practice, with a concrete business rule, such as “only users from the finance team should have access to critical financial data,” we want a system that deals only with those constituent concepts. For example, “the data is marked as critical financial” and “the user is a part of the finance team.” By abstracting away any source system components, such as “the tables in the finance schema” and “someone who’s a member of the finance Databricks group,” the access policies applied will then model the business rules as closely as possible.

This session will focus on how to establish and align the processes, policies, and stakeholders involved in making this type of system work seamlessly. Sharing the experience and learnings of our team at Instacart, we will aim to help attendees streamline and simplify their data security and access strategies.

Talk by: Kieran Taylor and Andria Fuquen

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

Large language models provide a leap in capabilities on understanding medical language and context - from passing the US medical licensing exam to summarizing clinical notes. They also suffer from a wide range of issues - hallucinations, robustness, privacy, bias – blocking many use cases. This session shares currently deployed software, lessons learned, and best practices that John Snow Labs has learned while enabling academic medical centers, pharmaceuticals, and health IT companies to build LLM-based solutions.

First, we cover benchmarks for new healthcare-specific large language models, showing how tuning LLM’s specifically on medical data and tasks results in higher accuracy on use cases such as question answering, information extraction, and summarization, compared to general-purpose LLM’s like GPT-4. Second, we share an architecture for medical chatbots that tackles issues of hallucinations, outdated content, privacy, and building a longitudinal view of patients. Third, we present a comprehensive solution for testing LLM’s beyond accuracy – for bias, fairness, representation, robustness, and toxicity – using the open-source nlptest library.

Talk by: David Talby

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

Accurate, reliable, and timely data is critical for CPG companies to stay ahead in highly competitive retailer relationships, and for a company like the Hershey Company, the commercial relationship with Walmart is one of the most important. The team at Hershey found themselves with a looming deadline for their legacy analytics services and targeted a migration to the brand new Walmart Luminate API. Working in partnership with Advancing Analytics, the Hershey Company leveraged a metadata-driven Lakehouse Architecture to rapidly onboard the new Luminate API, helping the category management teams to overhaul how they measure, predict, and plan their business operations.

In this session, we will discuss the impact Luminate has had on Hershey's business covering key areas such as sales, supply chain, and retail field execution, and the technical building blocks that can be used to rapidly provision business users with the data they need, when they need it. We will discuss how key technologies enable this rapid approach, with Databricks Autoloader ingesting and shaping our data, Delta Streaming processing the data through the lakehouse and Databricks SQL providing a responsive serving layer. The session will include commentary as well as cover the technical journey.

Talk by: Simon Whiteley and Jordan Donmoyer

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