The understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes.

Graph analytics have found their way into every major industry, from marketing and financial services to transportation. Fraud detection, recommendation engines and process optimization are some of the use cases where real-time decisions are mission-critical, and the underlying domain can be easily modeled as a graph.

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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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Robinhood’s data lake is the bedrock foundation that powers business analytics, product experimentation, and other machine learning applications throughout our organization. Come join this session where we will share our journey of building a scalable streaming data lakehouse with Spark, Postgres and other leading open source technologies.

We will lay out our architecture in depth and describe how we perform CDC streaming ingestion and incremental processing of 1000’s of Postgres tables into our data lake.

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Los Angeles County’s Department of Human Resources (DHR) is responsible for attracting a diverse workforce for the 37 departments it supports. Each year, DHR processes upwards of 400,000 applications for job opportunities making it one of the largest employers in the nation. Managing a hiring process of this scale is complex with many complicated factors such as background checks and skills examination. These processes, if not managed properly, can create bottlenecks and a poor experience for both candidates and hiring managers.

In order to identify areas for improvement, DHR set out to build detailed operational metrics across each stage of the hiring process. DHR used to conduct high level analysis manually using excel and other disparate tools. The data itself was limited, difficult to obtain, and analyze. In addition, it was taking analysts weeks to manually pull data from half a dozen siloed systems into excel for cleansing and analysis. This process was labor-intensive, inefficient, and prone to human error.

To overcome these challenges, DHR in partnership with Internal Services Department (ISD) adopted a modern data architecture in the cloud. Powered by the Azure Databricks Lakehouse, DHR was able to bring together their diverse volumes of data into a single platform for data analytics. Manual ETL processes that took weeks could now be automated in 10 minutes or less. With this new architecture, DHR has built Business Intelligence dashboards to unpack the hiring process to get a clear picture of where the bottlenecks are and track the speed with which candidates move through the process The dashboards allow the County departments innovate and make changes to enhance and improve the experience of potential job seekers and improve the timeliness of securing highly qualified and diverse County personnel at all employment levels.

In this talk, we’ll discuss DHR’s journey towards building a data-driven hiring process, the architecture decisions that enabled this transformation and the types of analytics that we’ve deployed to improve hiring efforts.

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Interactive, Near Real Time analytics is usually a common requirement for many data teams across different fields.

In the field of web security, interactive analytics allows end users to get real time or historical insights about the state of their protected resource at any point of time and take actions accordingly.

One of the hardest aspects of enabling interactive, near-real-time analytics on a massive scale is a low response time. Scanning hundreds of Terabytes of data over a non-aggregated stream of events (a Delta Lake), and still returning an answer within just a few seconds can be a major challenge.

In this talk we will learn: • How did we build a 5PB Delta Lake of non-aggregated security events • What challenges did we see along the way - reducing delta log scan, improving cache affinity, reducing storage throttling errors etc. • How did we overcome them one by one

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We've come a long way from the way data analysis used to be done. The emergence of the analytics engineering workflow, with dbt at its center, has helped usher in a new era of productivity. Not quite data engineering or data analysis, analytics engineering has enabled new levels of collaboration between two key sets of practitioners.

But that's not the only coming together happening right now. Enabled by the open lakehouse, the worlds of data analysis and AI/ML are also converging under a single roof, hinting at a new future of intertwined workloads and silo-free collaboration. It's a future that's tantalizing, and entirely within reach. Let's talk about making it happen.

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Day 1 Afternoon Keynote | Data + AI Summit 2022 Supercharging our data architecture at Coinbase using Databricks Lakehouse | Eric Sun | Keynote Partner Connect & Ecosystem Strategy | Zaheera Valani What are ELT and CDC, and why are all the cool kids doing it? |George Fraser Analytics without Compromise | Francois Ajenstat Fireside Chat with Zhamak Dehghani and Arsalan Tavakoli

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Day 1 Morning Keynote | Data + AI Summit 2022 Welcome & "Destination Lakehouse" | Ali Ghodsi Apache Spark Community Update | Reynold Xin Streaming Lakehouse | Karthik Ramasamy Delta Lake | Michael Armbrust How Adobe migrated to a unified and open data Lakehouse to deliver personalization at unprecedented scale | Dave Weinstein Data Governance and Sharing on Lakehouse |Matei Zaharia Analytics Engineering and the Great Convergence | Tristan Handy Data Warehousing | Shant Hovespian Unlocking the power of data, AI & analytics: Amgen’s journey to the Lakehouse | Kerby Johnson

Get insights on how to launch a successful lakehouse architecture in Rise of the Data Lakehouse by Bill Inmon, the father of the data warehouse. Download the ebook: https://dbricks.co/3ER9Y0K

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The future of Financial Services is open with data and AI at its core. Welcome data teams and executives in Financial Services! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on how Financial Services leaders are harnessing the power of data and analytics to digitally transform, minimize risk, accelerate time to market and drive sustainable value creation To help you take full advantage of the Financial Services industry experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Financial Services Industry Forum: Our flagship event for Financial Services attendees at Summit featuring keynotes and panel discussions with ADP, Northwestern Mutual, Point72 Asset Management, S&P Global and EY, followed by networking. More details in the agenda below. Financial Services Lounge: Stop by our lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from our partners including Accenture, Avanade, Deloitte and others. Session Talks: Over 15 technical talks and demos on topics including hyper-personalization, AI-fueled forecasting, enterprise analytics in cloud, scaling privacy and cybersecurity, MLOps in cryptocurrency, ethical credit scoring and more.

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Welcome data teams and executives in the Healthcare and Life Sciences industry! This year’s Data + AI Summit is jam-packed with talks, demos and discussions on the biggest innovations in patient care and drug R&D. To help you take full advantage of the Healthcare and Life Sciences experience at Summit, we’ve curated all the programs in one place.

Highlights at this year’s Summit:

Healthcare and Life Sciences Industry Forum: Our capstone event for Healthcare and Life Sciences attendees at Summit featuring keynotes and panel discussions with Walgreens, Takeda, Optum, and Humana followed by networking. More details in the agenda below. Healthcare and Life Sciences Lounge: Stop by our industry lounge located outside the Expo floor to meet with Databricks’ industry experts and see solutions from our partners including ZS Associates, John Snow Labs and others. Session Talks: Over 10 technical talks on topics including healthcare NLP, knowledge graphs for R&D, commercial analytics, and predicting hospital readmissions.

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Data + AI Summit Keynotes from: Partner Connect & Ecosystem Strategy (Zaheera Valani) What are ELT and CDC, and why are all the cool kids doing it? (George Fraser) Analytics without Compromise (Francois Ajenstat)

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Presto is a widely adopted distributed SQL engine for data lake analytics. With Presto, you can perform ad hoc querying of data in place, which helps solve challenges around time to discover and the amount of time it takes to do ad hoc analysis. Additionally, new features like the disaggregated coordinator, Presto-on-Spark, scan optimizations, a reusable native engine, and a Pinot connector enable added benefits around performance, scale, and ecosystem.

In this session, Philip and Rohan will introduce the Presto technology and share why it’s becoming so popular – in fact, companies like Facebook, Uber, Twitter, Alibaba, and much more use Presto for interactive ad hoc queries, reporting & dashboarding data lake analytics, and much more. We’ll also show a quick demo on getting Presto running in AWS.

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In recent years, latest privacy laws & regulations bring a fundamental shift in the protection of data and privacy, placing new challenges to data applications. To resolve these privacy & security challenges in big data ecosystem without impacting existing applications, several hardware TEE (Trusted Execution Environment) solutions have been proposed for Apache Spark, e.g., PySpark with Scone and Opaque etc. However, to the best of our knowledge, none of them provide full protection to data pipelines in Spark applications. An adversary may still get sensitive information from unprotected components and stages. Furthermore, some of them greatly narrowed supported applications, e.g., only support SparkSQL. In this presentation, we will present a new PPMLA (privacy preserving machine learning and analytics) solution built on top of Apache Spark, BigDL, Occlum and Intel SGX. It ensures all spark components and pipelines are fully protected by Intel SGX, and existing Spark applications written in Scala, Java or Python can be migrated into our platform without any code change. We will demonstrate how to build distributed end-to-end SparkML/SparkSQL workloads with our solution on untrusted cloud environment and share real-world use cases for PPMLA.

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John Deere is leveraging big data and AI to deliver ‘smart’ industrial solutions that are revolutionizing agriculture and construction, driving sustainability and ultimately helping to feed the world. The John Deere Data Factory that is built upon the Databricks Lakehouse Platform is at the core of this innovation. It ingests petabytes of data and trillions of records to give data teams fast, reliable access to standardized data sets supporting 100s of ML and analytics use cases across the organization. From IoT sensor-enabled equipment driving proactive alerts that prevent failures, to precision agriculture that maximizes field output, to optimizing operations in the supply chain, finance and marketing, John Deere is providing advanced products, technology and services for customers who cultivate, harvest, transform, enrich, and build upon the land.

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Data is the key component of Analytics, AI or ML platform. Organizations may not be successful without having a Platform that can Source, Transform, Quality check and present data in a reportable format that can drive actionable insights.

This session will focus on how Capital One HR Team built a Low Cost Data movement Ecosystem that can source data, transform at scale and build the data storage (Redshift) at a level that can be easily consumed by AI/ML programs - by using AWS Services with combination of Open source software(Spark) and Enterprise Edition Hydrograph (UI Based ETL tool with Spark as backend) This presentation is mainly to demonstrate the flexibility that Apache Spark provides for various types ETL Data Pipelines when we code in Spark.

We have been running 3 types of pipelines over 6+ years , over 400+ nightly batch jobs for $1000/mo. (1) Spark on EC2 (2) UI Based ETL tool with Spark backend (on the same EC2) (3) Spark on EMR. We have a CI/CD pipeline that supports easy integration and code deployment in all non-prod and prod regions ( even supports automated unit testing). We will also demonstrate how this ecosystem can failover to a different region in less than 15 minutes , making our application highly resilient.

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Swedbank is the largest bank in Sweden & third largest in Nordics. They have about 7-8M customers across retail, mortgage , and investment (pensions). One of the key drivers for the bank was to look at data across all silos and build analytics to drive their ML models - they couldn’t. That’s when Swedbank made a strategic decision to go to the cloud and make bets on Databricks, Immuta, and Azure.

-Enterprise analytics in cloud is an initiative to move Swedbanks on-premise Hadoop based data lake into the cloud to provide improved analytical capabilities at scale. The strategic goals of the “Analytics Data Lake” are: -Advanced analytics: Improve analytical capabilities in terms of functionality, reduce analytics time to market and better predictive modelling -A Catalyst for Sharing Data: Make data Visible, Accessible, Understandable, Linked, and Trusted Technical advancements: Future proof with ability to add new tools/libraries, support for 3rd party solutions for Deep Learning/AI

To achieve these goals, Swedbank had to migrate existing capabilities and application services to Azure Databricks & implement Immuta as its unified access control plane. A “data discovery” space was created for data scientists to be able to come & scan (new) data, develop, train & operationalise ML models. To meet these goals Swedbank requires dynamic and granular data access controls to both mitigate data exposure (due to compromised accounts, attackers monitoring a network, and other threats) while empowering users via self-service data discovery & analytics. Protection of sensitive data is key to enable Swedbank to support key financial services use cases.

The presentation will focus on this journey, calling out key technical challenges, learning & benefits observed.

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In this keynote, you will learn more about Amgen's data platform journey from data warehouse to data lakehouse. They’’ll discuss our decision process and the challenges they faced with legacy architectures, and how they designed and implemented a sustaining platform strategy with Databricks Lakehouse, accelerating their ability to democratize data to thousands of users.
Today, Amgen has implemented 400+ data science and analytics projects covering use cases like clinical trial optimization, supply chain management and commercial sales reporting, with more to come as they complete their digital transformation and unlock the power of data across the company.

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The US Air Force VAULT platform is a cloud-native enterprise data platform designed to provide the Department of the Air Force (DAF) with a robust, interoperable, and secure data environment. The strategic goals of VAULT include:

• Leading Data Culture - Increase data use and literacy to improve efficiency and effectiveness of decisions, readiness, mission operations, and cybersecurity.
• A Catalyst for Sharing Data - Make data Visible, Accessible, Understandable, Linked, and Trusted (VAULT).
• Driving Data Capabilities - Increase access to the right combination of state-of-the-art technologies needed to best utilize data.

To achieve these goals, the VAULT team created a self-service platform to onboard and extract, transform and load data, perform data analytics, machine learning and visualization, and data governance. Supporting over 50 tenants across NIPR and SIPR, adds complexity to maintaining data security while ensuring data can be shared and utilized for analytics. To meet these goals VAULT requires dynamic and granular data access controls to both mitigate data exposure (due to compromised accounts, attackers monitoring a network, and other threats) while empowering users via self-service analytics. Protection of sensitive data is key to enable VAULT to support key use cases such as personal readiness to optimally place Airmen trainees to meet production goals, increase readiness, and match trainees to their preferences.

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In this presentation we will talk about how we will use available NER based sensitive data detection methods, automated record of activity processing on top of spark and feast for collaborative intelligent analytics & governed data sharing. Information sharing is the key to successful business outcomes but it's complicated by sensitive information both user centric and business centric.

Our presentation is motivated by the need to share key KPIs, outcomes for health screening data collected from various surveys to improve care and assistance. In particular, collaborative information sharing was needed to help with health data management, early detection and prevention of disease KPIs. We will present a framework or an approach we have used for these purposes.

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Hear Ganesh Jayaram, CIO of John Deere, talk about how the company is leveraging big data and AI to deliver ‘smart’ industrial solutions that are revolutionizing agriculture, driving sustainability and ultimately helping to feed the world. The John Deere Data Factory that is built upon the Databricks Lakehouse Platform is at the core of this innovation. It ingests 8 petabytes of data and trillions of records to give data teams fast, reliable access to standardized data sets to deliver over 3000 ML and analytics use cases that democratize data across John Deere, to deliver a culture of empowerment where data is everybody's responsibility.

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