COVID has changed the way that we work and the way that we must do business. Supply Chain disruptions have impacted manufacturers’ ability to manufacture and distribute products. Logistics and the lack of labor have forced us to staff differently. The existential threat is real and we must change the way that we analyze data and solve problems real time in order to stay relevant.

In this session, you’ll learn about our journey, why the Data Lake and digital tech is essential to survival in this new world, some practical examples of how machine learning and data pipelines enable faster decision making, and why businesses cannot survive without these capabilities.

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Accelerate the path from data to decisions with the the Tellius AI-driven Decision Intelligence platform powered by Databricks Delta Lake. Empower business users and data teams to analyze data residing in the Delta Lake to understand what is happening in their business, uncover the reasons why metrics change, and get recommendations on how to impact outcomes. Learn how organizations derive value from Delta Lakehouse with a modern analytics experience that unifies guided insights, natural language search, and automated machine learning to speed up data-driven decision making at cloud scale.

In this session, we will showcase how customers: - Discover changes in KPIs and investigate the reasons why metrics change with AI-powered automated analysis - Empower business users and data analysts to iteratively explore data to identify trend drivers, uncover new customer segments, and surface hidden patterns in data - Simplify and speed-up analysis from massive datasets on Databrick Delta lake

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Delta Lake is the open source storage layer that makes the Databricks Lakehouse Platform possible by adding reliability, performance, and scalability to your data, wherever it is located. Join this session for an inside look at what is under the hood of Databricks - see how Delta Lake, by adding ACID transactions and versioning to Parquet files together with the Photon engine, provides customers with huge performance gains and the ability to address new challenges. This session will include a demo and overview of customer use cases unlocked by Delta Lake, and the benefits of running Delta Lake workloads on Databricks.

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Data sharing and data collaboration have become important in today's hyper connected digital economy. But to date, a lack of standards-based data sharing protocol has resulted in data sharing solutions tied to a single vendor or commercial product introducing vendor lock-in risks. What the industry deserves is an open approach to data sharing. Additionally, with stringent privacy regulations, data collaboration on sensitive data has become a challenge for organizations, resulting in fragmented, siloed, and incomplete insights. Join this session to learn how Databricks Lakehouse Platform simplifies secure data sharing and enables data collaboration across organizations in a privacy centric way.

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The data lakehouse is the future for modern data teams seeking to innovate with a data architecture that simplifies data workloads, eases collaboration, and maintains the flexibility and openness to stay agile as a company scales. The Databricks Lakehouse Platform realizes this idea by unifying analytics, data engineering, machine learning, and streaming workloads across clouds on one simple, open data platform. In this session, learn how the Databricks Lakehouse Platform can meet your needs for every data and analytics workload, with examples of real-customer applications, reference architectures, and demos to showcase how you can create modern data solutions of your own.

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To derive value from data, engineers need to collect, transform, and orchestrate data from various data types and source systems. However, today’s data engineering solutions support only a limited number of delivery styles, involve a significant amount of hand-coding, and have become resource-intensive. Modern data engineering requires more advanced data lifecycle for data ingestion, transformation, and processing. In this session, learn how the Databricks Lakehouse Platform provides an end-to-end data engineering solution — ingestion, processing and scheduling — that automates the complexity of building and maintaining pipelines and running ETL workloads directly on a data lake, so your team can focus on quality and reliability to drive valuable insights.

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Traditional data warehouses typically struggle when it comes to handling large volumes of data and traffic, particularly when it comes to unstructured data. In contrast, data lakes overcome such issues and have become the central hub for storing data. We outline how we can enable BI Kimball data modelling in a Lakehouse environment.

We present how we built a Spark-based framework to modernize DWH development with data lake as central storage, assuring high data quality and scalability. The framework was implemented at over 15 enterprise data warehouses across Europe.

We present how one can tackle in Spark & with Delta Lake the data warehouse principles like surrogate, foreign and business keys, SCD type 1 and 2 etc. Additionally, we share our experiences on how such a unified data modelling framework can bridge BI with modern day use cases, such as machine learning and real time analytics. The session outlines the original challenges, the steps taken and the technical hurdles we faced.

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Auto loader, the most popular tool for incremental data ingestion from cloud storage to Databricks’ Lakehouse, is used in our biggest customers’ ingestion workflows. Auto Loader is our all-in-one solution for exactly-once processing offering efficient file discovery, schema inference and evolution, and fault tolerance.

In this talk, we want to delve into key features in Auto Loader, including: • Avro schema inference • Rescued column • Semi-structured data support • Incremental listing • Asynchronous backfilling • Native listing • File-level tracking and observability

Auto Loader is also used in other Databricks features such as Delta Live Tables. We will discuss the architecture, provide a demo, and feature an Auto Loader customer speaking about their experience migrating to Auto Loader.

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Analytics has long been the end goal for data teams— standing up dashboards and exporting reports for business teams. But what if data teams could extend their work directly into the tools business teams use?

The next evolution for data teams is Activation. Smart organizations use reverse ETL to extend the value of Databricks by syncing data directly into business platforms, making their lakehouse a Customer Data Platform (CDP). By making Databricks the single source of truth for your data, you can create business models in your lakehouse and serve them directly to your marketing tools, ad networks, CRMs, and more. This saves time and money, unlocks new use cases for your data and turns data team efforts into revenue generating activities.

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Azure Databricks is an easy, open, and collaborative service for data, analytics & AI use cases, enabled by Lakehouse architecture. Join this session to discover how you can get the most out of your Azure investments by combining the best of Azure Synapse Analytics, Azure Databricks and Power BI for building a complete analytics & AI solution based on Lakehouse architecture.

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Most organizations routinely operate their business with complex cloud data architectures that silo applications, users and data. As a result, there is no single source of truth of data for analytics, and most analysis is performed with stale data. To solve these challenges, the lakehouse has emerged as the new standard for data architecture, with the promise to unify data, AI and analytic workloads in one place. In this session, we will cover why the data lakehouse is the next best data warehouse. You will hear from the experts success stories, use cases, and best practices learned from the field and discover how the data lakehouse ingests, stores and governs business-critical data at scale to build a curated data lake for data warehousing, SQL and BI workloads. You will also learn how Databricks SQL can help you lower costs and get started in seconds with instant, elastic SQL serverless compute, and how to empower every analytics engineers and analysts to quickly find and share new insights using their favorite BI and SQL tools, like Fivetran, dbt, Tableau or PowerBI.

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As a DBA for your Organization’s Lakehouse, it’s your job to stay on top of performance & cost optimization techniques. We will discuss how to use the available Delta Lake tools to tune your jobs and optimize your tables.

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dbt's analytics engineering workflow has been adopted by 11,000+ teams, and quickly become an industry standard for data transformation. This is a great chance to see why.

dbt allows anyone who knows SQL to develop, document, test, and deploy models. With the native, SQL-first integration between Databricks and dbt Cloud, analytics teams can collaborate in the same workspace as data engineers and data scientists to build production-grade data transformation pipelines on the lakehouse.

In this live session, Aaron Steichen, Solutions Architect at dbt Labs will walk you through dbt's workflow, how it works with Databricks, and what it makes possible.

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Data Lineage is key for managing change, ensuring data quality and implementing Data Governance in an organization. There are a few use cases for Data Lineage: Data Governance: For compliance and regulatory purposes our customers are required to prove the data/reports they are submitting came from a trusted and verified source.

This typically means identifying the tables and data sets used in a report or dashboard and tracing the source of these tables and fields. Another use case for the Governance scenario is to understand the spread of sensitive data within the lakehouse. Data Discovery: Data analysts looking to self-serve and build their own analytics and models typically spend time exploring and understanding the data in their lakehouse.

Lineage is a key piece of information which enhances the understanding and trustworthiness of the data the analyst plans to use. Problem Identification: Data teams are often called to solve errors in analysts dashboards and reports (“Why is the total number of widgets different in this report than the one I have built?”). This usually leads to an expensive forensic exercise by the DE team to understand the sources of data and the transformations applied to it before it hits the report. Change Management : It is not uncommon for data sources to change, a new source may stop delivering data or a field in the source system changes its semantics.

In this scenario the DE team would like to understand the downstream impact of this change - to get a sense of how many datasets and users will be affected by this change. This will help them determine the impact of the change, manage user expectations and address issues ahead of time In this talk, we will talk about how we capture table and column lineage for spark / delta and unity catalog for our customers in details and how users could leverage data lineage to serve various use cases mentioned above.

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Productionalizing ML models is hard. In fact, very few ML projects make it to production, and one of the hardest problems is data! Most AI platforms are disconnected from the data platform, making it challenging to keep features constantly updated and available in real-time. Offline/online skew prevents models from being used in real-time or, worse, introduces bugs and biases in production. Building systems to enable real-time inference requires valuable production engineering resources. As a result of these challenges, most ML models do not see the light of day.

Learn how you can simplify production ML using Databricks Feature Store, the first feature store built on the data lakehouse. Data sources for features are drawn from a central data lakehouse, and the feature tables themselves are tables in the lakehouse, accessible in Spark and SQL for both machine learning and analytics use cases. Features, data pipelines, source data, and models can all be co-governed in a central platform. Feature Store is seamlessly integrated with Apache Spark™, enabling automatic lineage tracking, and with MLflow, enabling models to look up feature values at inference time automatically. See these capabilities in action and how you can use it for your ML projects.

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Data architectures are the key and part of a larger picture to building robust analytical and AI applications. One must take a holistic view of the entire data analytics realm when it comes to planning for data science initiatives.

Through this talk, learn about the evolution of the data landscape and why Lakehouses are becoming a de facto for organizations building scalable data architectures. A lakehouse architecture combines data management capability including reliability, integrity, and quality from the data warehouse and supports all data workloads including BI and AI with the low cost and open approach of data lakes.

Data Practitioners will also learn some core concepts of building an efficient Lakehouse with Delta Lake.

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The logic in your data is the heartbeat of your organization’s reports, analytics, dashboards and applications. But that logic is often trapped in antiquated technologies that can’t take advantage of the massive scalability in the Databricks Lakehouse.

In this session BladeBridge will show how to automate the conversion of this metadata and code into Databricks PySpark and DBSQL. BladeBridge will demonstrate the flexibility of configuring for N legacy technologies to facilitate an automated path for not just a single modernization project but a factory approach for corporate wide modernization.

BladeBridge will also present how you can empirically size your migration project to determine the level of effort required.

In this session you will learn: What BladeBridge Converter is What BladeBridge Analyzer is How BladeBridge configures Readers and Writers How to size a conversion effort How to accelerate adoption of Databricks

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Join the Day 1 keynote to hear from Databricks co-founders - and original creators of Apache Spark and Delta Lake - Ali Ghodsi, Matei Zaharia, and Reynold Xin on how Databricks and the open source community is taking on the biggest challenges in data. The talks will address the latest updates on the Apache Spark and Delta Lake projects, the evolution of data lakehouse architecture, and how companies like Adobe and Amgen are using lakehouse architecture to advance their data goals.

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Data + AI Summit Keynote talks from Reynold Xin and Karthik Ramasamy

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Ingesting data from hundreds of different data sources is critical before organizations can execute advanced analytics, data science, and machine learning. Unfortunately, ingesting and unifying this data to create a reliable single source of truth is usually extremely time-consuming and costly. In this session, discover how Databricks simplifies data ingestion, at low latency, with SQL-only ingestion capabilities. We will discuss and demonstrate how you can easily and quickly ingest any data into the lakehouse. The session will also cover newly-released features and tools that make data ingestion even simpler on the Databricks Lakehouse Platform.

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