Join Bruno Aziza, Head of Data and Analytics, Google Cloud, for an in-depth look at what he is seeing in the future of data and emerging trends. He will also cover Google Cloud’s data analytics practice, including insights into the Data Cloud Alliance, Big Lake, and our strategic partnership with Databricks.

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Trusted analytics and predictive data models require accurate, consistent, and contextual data. The more attributes used to fuel models, the more accurate their results. However, building comprehensive models with trusted data is not easy. Accessing data from multiple disparate sources, making spatial data consumable, and enriching models with reliable third-party data is challenging.

In response to these challenges, Precisely has developed tools to facilitate a location-enabled lakehouse on the Databricks platform, helping users get more out of their data. Come see live demos and learn how to build your own location-enabled lakehouse by:

• Organizing and managing address data and assigning a unique and persistent identifier • Enriching addresses with standard and dynamic attributes from our curated data portfolio • Analyzing enriched data to uncover relationships and create dashboard visualizations • Understanding high-level solution architecture

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Why is it so hard to integrate Machine Learning into real business applications? In 2019 Gartner predicted that AI augmentation would solve this problem and would create will create $2.9 trillion of business value and 6.2 billion hours of worker productivity in 2021. A new realm of business science methods that encompass AI-powered analytics that allows people with domain expertise to make smarter decisions faster and with more confidence have also emerged as a solution to this problem. Dr. Harvey will demystify why integration challenges still account for $30.2 billion in annual global losses and discuss what it takes to integrate AI/ML code or algorithms into real business applications and the effort that goes into making each component, including data collection, preparation, training, and serving production-ready, enabling organizations to use the results of integrated models repeatedly with minimal user intervention. Finally, Dr. Harvey will discuss AISquared’s integration with Databricks and MLFlow to accelerate the integration of AI by unifying data science with business. By adding five lines of code to your model, users can now leverage AISquared’s model integration API framework which provides a quick and easy way to integrate models directly into live business applications.

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Modern data assets take many forms: not just files or tables, but dashboards, ML models, and unstructured data like video and images, all of which cannot be governed and managed by legacy data governance solutions. Join this session to learn how data teams can use Unity Catalog to centrally manage all data and AI assets with a common governance model based on familiar ANSI SQL, ensuring much better native performance and security. Built-in automated data lineage provides end-to-end visibility into how data flows from source to consumption, so that organizations can identify and diagnose the impact of data changes. Unity Catalog delivers the flexibility to leverage existing data catalogs and solutions and establish a future-proof, centralized governance without expensive migration costs. It also creates detailed audit reports for data compliance and security, while ensuring data teams can quickly discover and reference data for BI, analytics, and ML workloads, accelerating time to value.

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With serverless SQL compute and built-in governance, Databricks SQL lets every analyst and analytics engineer easily ingest, transform, and query the freshest data directly on your data lake, using their tools of choice like Fivetran, dbt, PowerBI or Tableau, and standard SQL. There is no need to move data to another system. All this takes place at virtually any scale, at a fraction of the cost of traditional cloud data warehouses. Join this session for a deep dive into how Databricks SQL works under the hood, and see a live end-to-end demo of the data and analytics on Databricks from data ingestion, transformation, and consumption, using the modern data stack along with Databricks SQL.

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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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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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Change data capture (CDC) is an increasingly common technology used in real-time machine learning and AI data pipelines. When paired with Databricks Delta Lake, it provides organizations with a number of benefits including lower data processing costs and highly responsive analytics applications. This session will provide a detailed overview of Matillion’s new CDC capabilities and how the integration of these capabilities with Delta Lake on Databricks can help you manage dataset changes, making it easy to automate the capture, transformation, and enrichment of data in near real time. Attend this session and see the advantages of a Matillion’s CDC capabilities to simplify real time data capture and analytics in your Delta Lake.

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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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Multiple organizations often wish to aggregate their confidential data and learn from it, but they cannot do so because they cannot share their data with each other. For example, banks wish to train models jointly over their aggregate transaction data to detect money launderers more efficiently because criminals hide their traces across different banks.

To address such problems, we developed MC^2 at UC Berkeley, an open-source framework for multi-party confidential computation, on top of Apache Spark. MC^2 enables organizations to share encrypted data and perform analytics and machine learning on the encrypted data without any organization or the cloud seeing the data. Our company Opaque brings the MC^2 technology in an easy-to-use form to organizations in the financial, medical, ad tech, and other sectors.

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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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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 engineers have the difficult task of cleansing complex, diverse data, and transforming it into a usable source to drive data analytics, data science, and machine learning. They need to know the data infrastructure platform in depth, build complex queries in various languages and stitch them together for production. Join this talk to learn how Delta Live Tables (DLT) simplifies the complexity of data transformation and ETL. DLT is the first ETL framework to use modern software engineering practices to deliver reliable and trusted data pipelines at any scale. Discover how analysts and data engineers can innovate rapidly with simple pipeline development and maintenance, how to remove operational complexity by automating administrative tasks and gaining visibility into pipeline operations, how built-in quality controls and monitoring ensure accurate BI, data science, and ML, and how simplified batch and streaming can be implemented with self-optimizing and auto-scaling data pipelines.

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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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In this talk, we'll first introduce Sigma Workbooks along with its technical design motivations and architectural details. Sigma Workbooks is an interactive visual data analytics system that enables business users to easily perform complex ad-hoc analysis over data in cloud data warehouses (CDWs). We'll then demonstrate the expressivity, scalability, and ease-of-use of Sigma Workbooks through real-life use cases over datasets stored in Delta Lake. We’ll conclude the talk by sharing the lessons that we have learned throughout the design and implementation iterations of Sigma Workbooks.

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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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While SAS has been a standard in analytics and data science use cases, it is not cloud-native and does not scale well. Join us to learn how AARP automated the conversion of hundreds of complex data processing, model scoring, and campaign workloads to Databricks using LeapLogic, an intelligent code transformation accelerator that can transform any and all legacy ETL, analytics, data warehouse and Hadoop to modern data platforms.

In this session experts from AARP and Impetus will share about collaborating with Databricks and how they were able to: • Automate modernization of SAS marketing analytics based on coding best practices • Establish a rich library of Spark and Python equivalent functions on Databricks with the same capabilities as SAS procedures, DATA step operations, macros, and functions • Leverage Databricks-native services like Delta Live Tables to implement waterfall techniques for campaign execution and simplify pipeline monitoring

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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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In an age of data abundance and digital disruption CFOs are adopting next generation planning capabilities to drive strategic decision making in real time. The future of forecasting is AI driven. PrecisionViewTM, a Deloitte’s proprietary forecasting solution leverages data aggregation technologies with predictive analytics and machine-learning capabilities to allow businesses to achieve improved forecasting accuracy.

Attend this webinar to hear about: • AI-powered financial planning that helps generate high-impact insights by incorporating the organization’s internal data and a myriad of external macroeconomic factors • Examples of how companies have achieved success using scenario modelling • Databricks’ compute capabilities that allow for parallel processing which helps generate near real time forecasts at the most granular levels

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Data and predictions have permeated sports and our conversations around it since the beginning. Who will win the big game this weekend? How many points will your favorite player score? How much money will be guaranteed in the next free agent contract? Once could argue that data-driven decisions in sports started with Moneyball in baseball, in 2003. In the two decades since, data and technology have exploded on the scene. The Texas Rangers are using modern cloud software, such as Databricks, to help make sense of this data, and provide actionable information to create a World Series team on the field. From computer vision, pose analytics, and player tracking, to pitch design, base stealing likelihood, and more, come see how the Texas Rangers are using innovative cloud technologies to create action-driven reports from the current sea of Big Data. Finally, this talk will demonstrate how the Texas Rangers use MLFlow and the MLRegistry inside Databricks to organize their predictive models.

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As gatekeepers of the financial system, banks play a crucial role in reporting possible instances of financial crime. At the same time, criminals continuously reinvent their approaches to hide their activities among dense transaction data. In this talk, we describe the challenges of detecting money laundering and outline why employing machine learning via MLOps is critically important to identify complex and ever-changing patterns.

In anti-money-laundering, machine learning answers to a dire need for vigilance and efficiency where previous-generation systems fall short. We will demonstrate how our open platform facilitates a gradual migration towards a model-driven landscape, using the example of transaction-monitoring to showcase applications of supervised and unsupervised learning, human explainability, and model monitoring. This environment enables us to drive change from the ground up in how the bank understands its clients to detect financial crime.

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In this talk, we'll review the major milestones that have defined Spatial SQL as the powerful tool for geospatial analytics that it is today.

From the early foundations of the JTS Topology Suite and GEOS and its application on the PostGIS extension for PostgreSQL, to the latest implementation in Spark SQL using libraries such as the CARTO Analytics Toolbox for Databricks, Spatial SQL has been a key component of many geospatial analytics products and solutions, leveraging the computing power of different databases with SQL as lingua franca, allowing easy adoption by data scientists, analysts and engineers.

The latest innovation in this area is the CARTO Spatial Extension for Databricks, which makes the most of the near-unlimited scalability provided by Spark and the cutting-edge geospatial capabilities that CARTO offers.

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