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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Data is no longer considered an asset to be protected within teams, but as an asset to be democratized and made available to everyone in the organization in a secure and governed manner. The Data Mesh is an evolving data architecture pattern that helps organizations in breaking down data silos and providing agility to respond to market changes quickly with decentralized data ownership and centralized governance and security.

This talk will provide details and demonstrate how to use Databricks Delta Lake with Unity Catalog to implement and operationalize the Data Mesh Architecture pattern. The demo includes LTI Canvas Alcazar solution which helps accelerate the data mesh implementation with Databricks.

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Apache Spark has become the most widely-used engine for executing data engineering, data science and machine learning on single-node machines or clusters. The number of monthly maven downloads of Spark has rapidly increased to 20 million.

We will talk about the higher-level features and improvements in Spark 3.2 and 3.3. The talk also dives deeper into the following features + Introducing pandas API on Apache Spark to unify small data API and big data API. + Completing the ANSI SQL compatibility mode to simplify migration of SQL workloads. + Productionizing adaptive query execution to speed up Spark SQL at runtime. + Introducing RocksDB state store to make state processing more scalable

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The application of AI algorithms in domains such as self-driving cars, facial recognition, and hiring holds great promise. At the same time, it raises legitimate concerns about AI algorithms robustness against adversarial attacks. Widespread adoption of AI algorithms where the predictions are hidden or obscured from the trained eye of the subject expert, opportunities for a malicious actor to take advantage of the AI algorithms grow considerably, necessitating the addition of adversarial robustness training and checking. To protect against and mitigate the damages caused by these malicious actors, this talk will examine how to build a pipeline that’s robust against adversarial attacks by leveraging Kubeflow Pipelines and integration with LFAI Adversarial Robustness Toolbox (ART). Additionally we will show how to test a machine learning model's adversarial robustness in production on Kubeflow Serving, by virtue of Payload logging (KNative eventing) and ART. This presentation focuses on adversarial robustness instead of fairness and bias.

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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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Real-world data problems are becoming increasingly daunting to solve, as data volume grows and computing tools proliferate. Since 2018, Gartner has predicted that 85% of ML projects will fail and this trend will likely continue through 2022 as well. Nevertheless, in most cases, ML practitioners have the opportunity to avoid their projects from failing in the early phases.

In this talk, the speaker will borrow from her consultancy and hands-on implementation experience with cross-functional clients to share her takeaways in designing better ML systems. The talk will walk through common pitfalls to watch out for, relevant best practices in software engineering for ML, and technical anchors that make a robust system. This talk aims to empower the audience – beginner and experienced practitioners alike – with confidence in their ML project designs and help provide the big-picture design thinking framework for successful projects.

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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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Data collection, preprocessing, feature engineering are the fundamental steps in any Machine Learning Pipeline. After feature engineering, being able to parallelize training on multiple low cost machines helps to reduce cost and time both. And, then being able to train models in a distributed manner speeds up Hyperparameter Tuning. How can we unify these stages of ML Pipeline in one unified distributed training platform together? And that too on Kubernetes?

Our ML platform is completely based on Kubernetes because of its scalability and rapid bootstrapping time of resources. In this talk we will demonstrate how Lyft uses Spark on Kubernetes, Fugue (our home grown unifying compute abstraction layer) to design a holistic end to end ML Pipeline system for distributed feature engineering, training & prediction experience for our customers on our ML Platform on top of Spark on K8s. We will also do a deep dive to show how we are abstracting and hiding infrastructure complexities so that our Data Scientists and Research Scientist can focus only on the business logic for their models through simple pythonic APIs and SQL. We let the users focus on ''what to do'' and the platform takes care of ''how to do''. We will share our challenges, learning and the fun we had while implementing. Using Spark on K8s have helped us achieve large scale data processing with 90% less cost and at times bringing down processing time from 2 hours to less than 20 mins.

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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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The Delta ecosystem rapidly expanded with the release of Delta Lake 1.2 which included integrations with Apache Spark™, Apache Flink, Presto, Trino, features such as OPTIMIZE, data skipping using column statistics, restore APIs, S3 multi-cluster writes, and more.

Join this session to learn about how the wider Delta community collaborated together to bring these features and integrations together; as well as the current roadmap. This will be an interactive session so come prepared with your questions—we should have answers!

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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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Many ML and big data teams in the open source community are looking to run their workloads in the cloud and they invariably face a common set of challenges such as multi-tenant cluster management, resource fairness and sharing, gang scheduling and cost-effective infrastructure operations. Kubernetes is the de-facto standard platform for running containerized applications in the cloud. However, the default resource scheduler in Kubernetes leaves more to be desired for AI scenarios when running ML/DL training workloads or large-scale data processing jobs for feature engineering.

In this talk, we will share how the community leverage and build upon Apache YuniKorn to address the unique resource scheduling needs for ML and big data teams.

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As organizations adopt AI they inherent AI risk. AI risk often manifests itself in AI models that produce erroneous predictions that go undetected and result in serious consequences for the organization and individuals affected by the decisions.

In this talk we will discuss root causes for AI models going haywire, and present a rigorous framework for eliminating risk from AI. We will show how this methodology can be used as building blocks for building an AI firewall that can prevent and model AI model failures.

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As more applications harness the power of real-time data, it’s important to architect and implement a data stack to meet the broad requirements of operational ML and be able to seamlessly integrate neural embeddings into applications.

Real-time ML requires more than just deploying ML models to production using MLOps tooling; it requires a fast and scalable operational database that easily integrates into the MLOps workflow. Milliseconds matter and can make the difference in delivering fast online predictions whether it’s personalized recommendations, detecting fraud, or figuring out the most optimal food delivery route.

Attend this session to explore how a modern data stack can be used for real-time operational ML and building AI-infused applications. The session will over the following topics:

Emerging architectural components for operational ML such as the online feature store for real-time serving.

Operational excellence in managing globally distributed ML data and feature pipelines

Foundational data types of Redis including the representation of data using vector embeddings.

Using Redis as a vector database to build vector similarity search applications.

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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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Rust guarantees zero memory access bug once a program compiles. However, one can still introduce logical bugs in the implementation.

In this talk, I will first give a high level overview on common formal verification methods used in distributed system designs and implementations. Then I will talk about our experiences with using TLA+ and Stateright to formally model delta-rs' multi-writer S3 backend implementation. The end result of combining both Rust and formal verification is we end up with an efficient native Delta Lake implementation that is both memory safe and logical bug free!

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SQL users working with Pandas and Spark quickly realize SQL is a second-class interface, invoked between predominantly Python code.

We will introduce FugueSQL, an enhanced SQL interface that allows SQL lovers to express end-to-end workflows predominantly in SQL. With a Jupyter notebook extension, SQL commands can be used in Databricks notebooks for interactive handling of in-memory datasets. This allows heavy SQL users to fully leverage Spark in their preferred grammar.

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