Technologies like Databricks Delta Lake and Databricks SQL enable enterprises to store and query their data. But existing rules and metrics approaches to monitoring the quality of this data are tedious to set up and maintain, fail to catch unexpected issues, and generate false positive alerts that lead to alert fatigue.

In this talk, Jeremy will describe a set of fully unsupervised machine learning algorithms for monitoring data quality at scale in Databricks. He will cover how the algorithms work, their strengths and weaknesses, and how they are tested and calibrated.

Participants will leave this talk with an understanding of unsupervised data quality monitoring, its strengths and weaknesses, and how to begin monitoring data using it in Databricks.

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Serving patients in over 100 countries, Amgen is a leading global biotech company focused on developing therapies that have the power to save lives. Delivering on this mission requires our commercial teams to regularly meet with healthcare providers to discuss new treatments that can help patients in need. With the onset of the pandemic, where face-to-face interactions with doctors and other Healthcare Providers (HCPs) were severely impacted, Amgen had to rethink these interactions. With that in mind, the Amgen Commercial Data and Analytics team leveraged a modern data and AI architecture built on the Databricks Lakehouse to help accelerate its digital and data insights capabilities. This foundation enabled Amgen’s teams to develop a comprehensive, customer-centric view to support flexible go-to-market models and provide personalized experiences to our customers. In this presentation, we will share our recent journey of how we took an agile approach to bringing together over 2.2 petabytes of internally generated and externally sourced vendor data , and onboard into our AWS Cloud and Databricks environments to enable a standardized, scalable and robust capabilities to meet the business requirements in our fast-changing life sciences environment. We will share use cases of how we harmonized and managed our diverse sets of data to deliver efficiency, simplification, and performance outcomes for the business. We will cover the following aspects of our journey along with best practices we learned over time: • Our architecture to support Amgen’s Commercial Data & Analytics constant processing around the globe • Engineering best practices for building large scale Data Lakes and Analytics platforms such as Team organization, Data Ingestion and Data Quality Frameworks, DevOps Toolkit and Maturity Frameworks, and more • Databricks capabilities adopted such as Delta Lake, Workspace policies, SQL workspace endpoints, and MLflow for model registry and deployment. Also, various tools were built for Databricks workspace administration • Databricks capabilities being explored for future, such as Multi-task Orchestration, Container-based Apache Spark Processing, Feature Store, Repos for Git integration, etc. • The types of commercial analytics use cases we are building on the Databricks Lakehouse platform Attendees building global and Enterprise scale data engineering solutions to meet diverse sets of business requirements will benefit from learning about our journey. Technologists will learn how we addressed specific Business problems via reusable capabilities built to maximize value.

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• There are unique challenges associated with working with big data for finance (volume of data, disparate storage, variable sharing protocols etc...)
• Leveraging open source technologies, like Databricks' Delta Sharing, in combination with a flexible data management stack, can allow organizations to be more nimble in testing and deploying more strategies
• Live demonstration of Delta Sharing in combination with Nasdaq Data Fabric

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In retail, the right quantity at the right time is crucial for success. In this session we share how a demand forecasting solution helped some of our retailers to improve efficiencies and sharpen fresh product production and delivery planning.

With the setup in place we train hundreds of models in parallel, training on various levels including store level, product level and the combination of the two. By leveraging the distributed computation of Spark, we can do all of this in a scalable and fast way. Powered by Delta Lake, feature store and MLFlow this session clarifies how we built a highly reliable ML factory.

We show how this setup runs at various retailers and feeds accurate demand forecasts back to the ERP system, supporting the clients in their production planning and delivery. Through this session we want to inspire retailers & conference attendants to use data & AI to not only gain efficiency but also decrease food waste.

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Learn how Wipro helped a world leader in medical technology to modernize its data used the PySpark interface on Azure Databricks to create reusable generic frameworks, including slowly changing dimensions (SCDs), data validation/reconciliation tools, and delta lake tables created from metadata.

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Microservices is an increasingly popular architecture much loved by application teams, for it allows services to be developed and scaled independently. Data teams, though, often need a centralized repository where all data from different services come together to join and aggregate. The data platform can serve as a single source of company facts, enable near real time analytics, and secure sharing of massive data sets across clouds.

A viable microservices ingestion pattern is Change Data Capture, using AWS Database Migration Services or Debezium. CDC proves to be a scalable solution ideal for stable platforms, but it has several challenges for evolving services: Frequent schema changes, complex, unsupported DDL during migration, and automated deployments are but a few. An event streaming architecture can address these challenges.

Confluent, for example, provides a schema registry service where all services can register their event schemas. Schema registration helps with verifying that the events are being published based on the agreed contracts between data producers and consumers. It also provides a separation between internal service logic and the data consumed downstream. The services write their events to Kafka using the registered schemas with a specific topic based on the type of the event.

Data teams can leverage Spark jobs to ingest Kafka topics into Bronze tables in the Delta Lake. On ingestion, the registered schema from schema registry is used to validate the schema based on the provided version. A merge operation is sometimes called to translate events into final states of the records per business requirements.

Data teams can take advantage of Delta Live Tables on streaming datasets to produce Silver and Gold tables in near real time. Each input data source also has a set of expectations to ensure data quality and business rules. The pipeline allows Engineering and Analytics to collaborate by mixing Python and SQL. The refined data sets are then fed into Auto ML for discovery and baseline modeling.

To expose Gold tables to more consumers, especially non spark users across clouds, data teams can implement Delta Sharing. Recipients can accesses Silver tables from a different cloud and build their own analytics data sets. Analytics teams can also access Gold tables via pandas Delta Sharing client and BI tools.

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Due to the unique cybersecurity challenges that HSBC faces daily - from high data volumes to untrustworthy sources to the privacy and security restrictions of a highly regulated industry - the resulting architecture was an unwieldy set of disparate data silos. So, how do we build a cybersecurity advanced analytics environment to enrich and transform these myriad data sources into a unified, well-documented, robust, resilient, repeatable, scalable, maintainable platform that will empower the cyber analysts of the future? That at the same time remains cost-effective and enables everyone from the less-technical junior reporting user to the senior machine learning engineers?

In this session, Ryan Harris, Principal Cybersecurity Engineer at HSBC, dives into the infrastructure and architecture employed, ranging from the landing zone concepts, secure access workstations, data lake structure, and isolated data ingestion, to the enterprise integration layer. In the process of building the data pipelines and lakehouses, we ended up building a hybrid data mesh leveraging Delta Lake. The result is a flexible, secure, self-service environment that is unlocking the capabilities of our humans.

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Delta Lake and Lakehouse architectures have been instrumental technologies in providing a better foundation for dealing with streaming and data deltas via an open-industry standard. The rapid growth of the ecosystem is a testament to the success of this approach. However, challenges still remain in building a data platform that allows teams to process all data via streams, regardless of the age of data, while also being able to view all streams as tables without exporting data out of the streaming system. In this talk, we will take a hands-on look at how Apache Pulsar is building it’s core storage engine on the concepts of Lakehouse architectures, allowing teams to build data platforms that can manage data over its entire lifecycle and enabling data to be consumed as either a stream or a table. With these capabilities, we will show how Pulsar + Delta Lake empowers teams, regardless of toolset, to better focus on driving value from data, not just managing it.

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Delta Lake has quickly grown in usage across data lakes everywhere due to the growing use cases that require DML capabilities that Delta Lake brings. Outside of support for ACID transactions, users want the ability to interactively query the data in their data lake. This is where a query engine like Trino (formerly PrestoSQL) comes in. Starburst provides an enterprise version of the popular Trino MPP SQL query engine and has recently open sourced their Delta Lake connector.

In this talk, Tom and Claudius will talk about the connector, its features, and how their users are taking advantage of expanding the functionality of their data lakes with improved performance and the ability to handle colliding modifications. Get started with this feature-rich and open stack without the need of a multi-million dollar budget.

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Amgen has developed a suite of enterprise data & analytics platforms powered by modern, cloud native and open source technologies, that have played a vital role in building game changing analytics capabilities within the organization. Our platforms include a mature Data Lake with extensive self service capabilities, a Data Fabric with semantically connected data, a Data Marketplace for advanced cataloging, an intelligent Enterprise search among others to solve for a range of high value business problems. In this talk, we - Amgen and our partner ZS Associates - will share learning from our journey so far, best practices for building enterprise scale data & analytics platforms, and describe several business use cases and how we leverage modern technologies such as Databricks to enable our business teams. We will cover use cases related to Delta Lake, microservices, platform monitoring, fine grained security, and more.

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Fairness, Ethics, Accountability and Transparency (FEAT) are must-haves for high-stakes machine learning models. In particular, models within the Financial Services industry such as those that assign credit scores can impact people’s access to housing and utilities and even influence their social standing. Hence, model developers have a moral responsibility to ensure that models do not systematically disadvantage any one group. Nevertheless, implementing such models in industrial settings remains challenging. A lack of concrete guidelines, common standards and technical templates make evaluating models from a FEAT perspective unfeasible. To address these implementation challenges, the Monetary Authority of Singapore (MAS) set up the Veritas Initiative to create a framework for operationalising the FEAT principles, so as to guide the responsible development of AIDA (Artificial Intelligence and Data Analytics) systems.

In January 2021, MAS announced the successful conclusion of Phase 1 of the Veritas Initiative. Deliverables included an assessment methodology for the Fairness principle and open source code for applying Fairness metrics to two use cases - customer marketing and credit scoring. In this talk, we demonstrate how these open-source examples, and their fairness metrics, might be put into production using open source tools such as Delta Lake and MLFlow. Although the Veritas Framework was developed in Singapore, the ethical framework is applicable across geographies.

By doing this, we illustrate how ethical principles can be operationalised, monitored and maintained in production, thus moving beyond only accuracy-based metrics of model performance and towards a more holistic and principled way of developing and productionizing machine learning systems.

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Apache Kafka in conjunction with Apache Spark became the de facto standard for processing and analyzing data. Both frameworks are open, flexible, and scalable. Unfortunately, the latter makes operations a challenge for many teams. Ideally, teams can use serverless SaaS offerings to focus on business logic. However, hybrid and multi-cloud scenarios require a cloud-native platform that provides automated and elastic tooling to reduce the operations burden.

This post explores different architecture to build serverless Kafka and Spark multi-cloud architectures across regions and continents. We start from the analytics perspective of a data lake and explore its relation to a fully integrated data streaming layer with Kafka to build a modern data lakehouse. Real-world use cases show the joint value and explore the benefit of the "delta lake" integration.

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Simon and Denny Build A Thing is a live webshow, where Simon Whiteley (Advancing Analytics) and Denny Lee (Databricks) are building out a TV Ratings Analytics tool, working through the various challenges of building out a Data Lakehouse using Databricks. In this session, they'll be talking through their Lakehouse Platform, revisiting various pieces of functionality, and answering your questions, Live!

This is your chance to ask questions around structuring a lake for enterprise data analytics, the various ways we can use Delta Live Tables to simplify ETL or how to get started serving out data using Databricks SQL. We have a whole load of things to talk through, but we want to hear YOUR questions, which we can field from industry experience, community engagement and internal Databricks direction. There's also a chance we'll get distracted and talk about the Expanse for far too long.

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Scribd's data architecture was originally batch-oriented, but in the last couple years, we introduced streaming data ingestion to provide near-real-time ad hoc query capability, mitigate the need for more batch processing tasks, and set the foundation for building real-time data applications.

Kafka and Delta Lake are the two key components of our streaming ingestion pipeline. Various applications and services write messages to Kafka as events are happening. We were tasked with getting these messages into Delta Lake quickly and efficiently.

Our first solution was to deploy Spark Structured Streaming jobs. This got us off the ground quickly, but had some downsides.

Since Delta Lake and the Delta transaction protocol are open source, we kicked off a project to implement our own Rust ingestion daemon. We were confident we could deliver a Rust implementation since our ingestion jobs are append only. Rust offers high performance with a focus on code safety and modern syntax.

In this talk I will describe Scribd's unique approach to ingesting messages from Kafka topics into Delta Lake tables. I will describe the architecture, deployment model, and performance of our solution, which leverages the kafka-delta-ingest Rust daemon and the delta-rs crate hosted in auto-scaling ECS services. I will discuss foundational design aspects for achieving data integrity such as distributed locking with DynamoDb to overcome S3's lack of "PutIfAbsent" semantics, and avoiding duplicates or data loss when multiple concurrent tasks are handling the same stream. I'll highlight the reliability and performance characteristics we've observed so far. I'll also describe the Terraform deployment model we use to deliver our 70-and-growing production ingestion streams into AWS.

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In the past, stream processing over data lakes required a lot of development efforts from data engineering teams, as Itai has shown in his talk at Spark+AI Summit 2019 (https://tinyurl.com/2s3az5td). Today, with Delta Lake and Databricks Auto Loader, this becomes a few minutes' work! Not only that, it unlocks a new set of ways to efficiently leverage your data.

Nexar, a leading provider of dynamic mapping solutions, utilizes Delta Lake and advanced features such as Auto Loader to map 150 million miles of roads a month and provide meaningful insights to cities, mobility companies, driving apps, and insurers. Nexar’s growing dataset contains trillions of images that are used to build and maintain a digital twin of the world. Nexar uses state-of-the-art technologies to detect road furniture (like road signs and traffic lights), surface markings, and road works.

In this talk, we will describe how you can efficiently ingest, process, and maintain a robust Data Lake, whether you’re a mapping solutions provider, a media measurement company, or a social media network. Topics include: * Incremental & efficient streaming over cloud storage such as S3 * Storage optimizations using Delta Lake * Supporting mutable data use-cases with Delta Lake

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Talk about a challenge of building a scalable framework for data scientists and ML engineers, that could accommodate hundreds of generic or customer specific ML models, running both in streaming and batch, capable of processing 100+ million records per day from social media networks.

The goal has been archived using Spark and Delta. Our framework is built on clever usage of delta features such as change data feed, selective merge and spark structure streaming from and into delta tables. Saving the data in multiple delta tables, where the structure of these tables are reflecting the particular step in the whole flow. This brings great efficiency, as the downstream processing does very little transformations and thus even people without extensive experience of writing ML pipelines and jobs can use the framework easily. At the heart of the framework there is a series of Spark structure streaming jobs continuously evaluating rules and looking for what social media content should be processed by which model. These rules could be updated by the users anytime and the framework needs to automatically adjust the processing. In an environment like this, the ability to track the records throughout the whole process and the atomicity of operations is of utmost importance and delta tables are providing all of this out of the box.

In the talk we are going to focus on the ideas behind the framework and efficient combining of structured streaming and delta tables. Key takeaways would be exploring some of the lesser known delta table features and real-life experiences from building a ML framework solution based on scalable big data technologies, showing how capable and fast such a solution can be, even with minimal hardware resources.

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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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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 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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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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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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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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