This session focuses on one of the first Unity Catalog implementations for a large-scale enterprise. In this scenario, a cloud scale analytics platform with 7500 active users based on the lakehouse approach is used. In addition, there is potential for 1500 further users who are subject to special governance rules. They are consuming more than 600 TB of data stored in Delta Lake - continuously growing at more than 1TB per day. This might grow due to local country data. Therefore, the existing data platform must be extended to enable users to combine global and local data from their countries. A new data management was required, which reflects the strict information security rules at a need to know base. Core requirements are: read only from global data, write into local and share the results.

Due to a very pronounced information security awareness and a lack of the technological possibilities it was not possible to interdisciplinary analyze and exchange data so easy or at all so far. Therefore, a lot of business potential and gains could not be identified and realized.

With the new developments in the technology used and the basis of the lakehouse approach, thanks to Unity Catalog, we were able to develop a solution that could meet high requirements for security and process. And enables globally secured interdisciplinary data exchange and analysis at scale. This solution enables the democratization of the data. This results not only in the ability to gain better insights for business management, but also to generate entirely new business cases or products that require a higher degree of data integration and encourage the culture to change. We highlight technical challenges and solutions, present best practices and point out benefits of implementing Unity catalog for enterprises.

Talk by: Artem Meshcheryakov and Pascal van Bellen

Here’s more to explore: Data, Analytics, and AI Governance: https://dbricks.co/44gu3YU

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

To unlock all the competitive advantage Databricks offers your organization, you might need to update your strategy and methodology for the platform. With over 1,000+ Databricks projects completed globally in the last 18 months, we are going to share our insights on the best building blocks to target as you search for efficiency and competitive advantage.

These building blocks supporting this include enterprise metadata and data management services, data management foundation, and data services and products that enable business units to fully use their data and analytics at scale.

In this session, Avanade data leaders will highlight how Databricks’ modern data stack fits Azure PaaS and SaaS (such as Microsoft Fabric) ecosystem, how Unity catalog metadata supports automated data operations scenarios, and how we are helping clients measure modern analytics and governance business impact and value.

Talk by: Alan Grogan and Timur Mehmedbasic

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp Databricks named a Leader in 2022 Gartner® Magic QuadrantTM CDBMS: https://dbricks.co/3phw20d

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

When it comes to building stickier apps and products to grow your business, there's no greater opportunity than embedded analytics. Data apps that deliver superior user engagement and business value do analytics differently. They take a user-first approach and know how to deliver real-time, AI-powered insights - not just to internal employees - but to an organization’s customers and partners, as well.

Learn how ThoughtSpot Everywhere is helping companies like Emerald natively integrate analytics with other tools in their modern data stack to deliver a blazing-fast and instantly available analytics experience across all the data their users love. Join this session to learn how you can leverage embedded analytics to: Drive higher app engagement Get your app to market faster And create new revenue streams

Talk by: Krishti Bikal and Vika Smilansky

Here’s more to explore: Why the Data Lakehouse Is Your next Data Warehouse: https://dbricks.co/3Pt5unq Lakehouse Fundamentals Training: https://dbricks.co/44ancQs

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

When creating streaming workloads with Databricks, it can sometimes be difficult to capture and understand the current structure of your source data. For example, what happens if you are ingesting JSON events from a vendor, and the keys are very sparsely populated, or contain dynamic content? Ideally, data engineers want to "lock in" a target schema in order to minimize complexity and maximize performance for known access patterns. What do you do when your data sources just don't cooperate with that vision? The first step is to quantify how far your current source data is drifting from your established Delta table. But how?

This session will demonstrate a way to capture and visual drift across all your streaming tables. The next question is, "Now that I see all of the data I'm missing, how do I selectively promote some of these keys into DataFrame columns?" The second half of this session will demonstrate precisely how to do a schema migration with minimal job downtime.

Talk by: Alexander Vanadio

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Honeywell manages the control of equipment for hundreds of thousands of buildings worldwide. Many of our outcomes relating to energy and comfort rely on knowing where people are in the building at any one time. This is so we can target health and comfort conditions more suitably to areas where are more densely populated. Many of these buildings have Cisco IT infrastructure in them. Using their WIFI points and the RSSI signal strength from people’s laptops and phones, Cisco can calculate the number of people in each area of the building. Cisco Spaces offer this data up as a real-time streaming source. Honeywell HBT has utilized this stream of data by writing delta live table pipelines to consume this data source.

Honeywell buildings can now receive this firehose data from hundreds of concurrent customers and provide this occupancy data as a service to our vertical offerings in commercial, health, real estate and education. We will discuss the benefits of using DLT to handle this sort of incoming stream data, and illustrate the pain points we had and the resolutions we undertook in successfully receiving the stream of Cisco data. We will illustrate how our DLT pipeline was designed, and how it scaled to deal with huge quantities of real-time streaming data.

Talk by: Paul Mracek and Chris Inkpen

Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

In this session, we will explain the visualizations that serve to shorten the time to insight for our prospects and encourage potential buyers to take the next step and request more information from our commercial team. The S&P Global Marketplace is a discovery and exploration platform that enables prospective buyers and clients to easily search fundamental and alternative datasets from across S&P Global and curated third-party providers. It serves as a digital storefront that provides transparency into data coverage and use cases, reducing the time and effort for clients to find data for their needs. A key feature of Marketplace is our interactive data visualizations that provide insight into the coverage of a dataset and demonstrate how the dataset can be used to make more informed decisions.

The S&P Global Marketplace’s interactive visualizations are displayed in Tableau and are powered by Databricks. The Databricks platform allows for easy integration of S&P Global data and provides a collaborative environment where our team of product managers and data engineers can develop the code to generate each visualization. The team utilizes the web interface to develop the queries that perform the heavy lifting of data transformation instead of performing these tasks in Tableau. The final notebook output is saved into a custom data mart (“golden table”) which is the source for Tableau. We also developed an automated process that refreshes the whole process to ensure Marketplace has up to date visualizations.

Talk by: Onik Kurktchian

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

In this session, NTT DATA presents a case study involving of one of the largest and most prominent financial institutions in Japan. The project involved migration from the largest data analysis platform to Databricks, a project that required careful navigation of very strict security requirements while accommodating the needs of evolving technical solutions so they could support a wide variety of company structures. This session is for those who want to accelerate their business by effectively utilizing AI as well as BI.

NTT DATA is one of the largest system integrators in Japan, providing data analytics infrastructure to leading companies to help them effectively drive the democratization of data and AI as many in the Japanese market are now adding AI into their BI offering.

Talk by: Yuki Saito

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Lamini, the most powerful LLM engine, is the platform for any and every software engineer to ship an LLM into production as rapidly and as easily as possible. In this session, learn how to train your LLM on your own data and infrastructure with a few lines of code using the Lamini library. Get early access to a playground to train any open-source LLM. With Lamini, your own LLM comes with better performance, better data privacy, lower cost, lower latency, and more.

Talk by: Sharon Zhou

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

ABN AMRO Bank is one of the top leading banks in the Netherlands. It is the third largest bank in the Netherlands by revenue and number of mortgages held within the Netherlands, and has top management support of the objective to become a fully data-driven bank. ABN AMRO started its data journey almost seven years ago and has built a data platform off-premises with Hadoop technologies. This data platform has been used by more than 200 data providers, 150 data consumers, and more than 3000 datasets.

To become a fully digital bank and address the limitation of the on-premises platform requires a future-proof data platform DIAL (digital integration and access layer). ABN AMRO decided to build an Azure cloud-native data platform with the help of Microsoft and Databricks. Last year this cloud-native platform was ready for our data providers and data consumers. Six months ago we started the journey of migrating all the content from the on-premises data platform to the Azure data platform, this was a very large-scale migration and was achieved in six months.

In this session, we will focus on three things: 1. The migration strategy going from on-premises to a cloud-native platform 2. Which Databricks solutions were used in the data platform 3. How the Databricks team assisted in the overall migration

Talk by: Rakesh Singh and Marcel Kramer

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Healthcare datasets contain both personally identifiable information (PII) and personal health information (PHI) that needs to be de-identified in order to protect patient confidentiality and ensure HIPAA compliance. This privacy data is easily detected when it’s provided in columns labeled with names such as “SSN,” First Name,” “Full Name,” and “DOB;” however, it is much harder to detect when it is hidden within columns labeled “Doctor Notes,” “Diagnoses,” or “Comments.” HealthVerity, a leader in the HIPAA-compliant exchange of real-world data (RWD) to uncover patient, payer and genomic insights and power innovation for the healthcare industry, ensures healthcare datasets are de-identified from PII and PHI using elaborate privacy procedures.

During this session, we will demonstrate how to use a low-code/no-code platform to simplify and automate data pipelines that leverage prebuilt ML models to scan data for PHI/PII leakage and quarantine those rows in Unity Catalog when leakage is identified and move them to a Databricks clean room for analysis.

Talk by: Pouya Barrach-Yousefi and Simon King

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Akamai is a leading content delivery network (CDN) and cybersecurity company operating hundreds of thousands of servers in more than 135 countries worldwide. In this session, we will share our experiences and lessons learned from building and maintaining the Web Security Analytics (WSA) product, an interactive analytics platform powered by Databricks and Delta Lake that enables customers to efficiently analyze and take informed action on a high volume of streaming security events.

The WSA platform must be able to serve hundreds of queries per minute, scanning hundreds of terabytes of data from a six petabyte data lake, with most queries returning results within ten seconds; for both aggregation queries and needle in a haystack queries. This session will cover how to use Databricks SQL warehouses and job clusters cost-effectively, and how to improve query performance using tools and techniques such as Delta Lake, Databricks Photon, and partitioning. This talk will be valuable for anyone looking to build and operate a high-performance analytics platform.

Talk by: Tomer Patel and Itai Yaffe

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Banco Bradesco represents one of the largest companies in the financial sector in Latin America. They have more than 99 million customers, 79 years of history, and a legacy of data distributed in hundreds of on-premises systems. With the spread of data-driven approaches and the growth of cloud computing adoption, we needed to innovate and adapt to new trends and enable an analytical environment with democratized data.

We will show how more than eight business departments have already engaged in using the Lakehouse exploratory environment, with more than 190 use cases mapped and a multi-bank financial manager. Unlike with on-premises, the cost of each process can be isolated and managed in near real-time, allowing quick responses to cost and budget deviations, while increasing the deployment speed of new features 36 times compared to on-premises.

The data is now used and shared safely and easily between different areas and companies of the group. Also, the view of dashboards within Databricks allows panels to be efficiently "prototyped" with real data, allowing an easy interaction of the business area with its real needs and then creating a definitive view with all relevant points duly stressed.

Talk by: Pedro Boareto and Fabio Luis Correia da Silva

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

In recent years, many businesses have adopted real-time streaming applications to enable faster decision making, quicker predictions, and improved customer experiences. Few of these applications are driving critical business use cases like financial fraud detection, loan application processing, personalized offers, etc. These business critical applications need robust disaster recovery strategies to recover from the catastrophic events to reduce the lost uptime. However, most organizations find it hard to set up disaster recovery for streaming applications as it involves continuous data flow. Streaming state and temporal behavior of data brings add complexities to the DR strategy. A reliable disaster recovery strategy includes backup, failover and failback approaches for the streaming application. Unlike the batch applications, these steps include many moving elements and need a very sophisticated approach to ensure that the services are failing over the DR region and meet the set RTO and RPO requirements.

In this session, we will cover following topics with a FINSERV use case demo: - Backup strategy: backup of delta tables, message bus services and checkpoint including offsets - Failover strategy: failover strategy to disable services in the primary region and start the services in the secondary region with minimum data loss - Failback strategy: failback strategy to restart the services in the primary region once all the services are restored - Common challenges and best practices for backup

Talk by: Shasidhar Eranti and Sachin Balgonda Patil

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

DLite is a new instruction-following model developed by AI Squared by fine-tuning the smallest GPT-2 model on the Alpaca dataset. Despite having only 124 million parameters, DLite exhibits impressive ChatGPT-like interactivity and can be fine-tuned on a single T4 GPU for less than $15.00. Due to its small relative size, DLite can be run locally on a wide variety of compute environments, including laptop CPUs, and can be used without sending data to any third-party API. This lightweight property of DLite makes it highly accessible for personal use, empowering users to integrate machine learning models and advanced analytics into their workflows quickly, securely, and cost-effectively.

Leveraging DLite within AI Squared's platform can empower organizations to orchestrate the integration of Dolly/DLite into business workflows, creating personalized versions of Dolly/DLite, chaining models or analytics to contextualize Dolly/Dlite responses/prompts, and curating new datasets leveraging real-time feedback.

Talk by: Jacob Renn and Ian Sotnek

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

As the backbone of Australia’s supply chain, the Australia Rail Track Corporation (ARTC) plays a vital role in the management and monitoring of goods transportation across 8,500km of its rail network throughout Australia. ARTC provides weighbridges along their track which read train weights as they pass at speeds of up to 60 kilometers an hour. This information is highly valuable and is required both by ARTC and their customers to provide accurate haulage weight details, analyze technical equipment, and help ensure wagons have been loaded correctly.

A total of 750 trains run across a network of 8500 km in a day and generate real-time data at approximately 50 sensor platforms. With the help of structured streaming and Delta Lake, ARTC was able to analyze and store:

• Precise train location
• Weight of the train in real-time
• Train crossing time to the second level
• Train speed, temperature, sound frequency, and friction
• Train schedule lookups

Once all the IoT data has been pulled together from an IoT event hub, it is processed in real-time using structured streaming and stored in Delta Lake. To understand the train GPS location, API calls are then made per minute per train from the Lakehouse. API calls are made in real-time to another scheduling system to lookup customer info. Once the processed/enriched data is stored in Delta Lake, an API layer was also created on top of it to expose this data to all consumers.

The outcome: increased transparency on weight data as it is now made available to customers; we built a digital data ecosystem that now ARTC’s customers use to meet their KPIs/ planning; the ability to determine temporary speed restrictions across the network to improve train scheduling accuracy and also schedule network maintenance based on train schedules and speed.

Talk by: Deepak Sekar and Harsh Mishra

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

International Finance Corporation (IFC) is using data and AI to build machine learning solutions that create analytical capacity to support the review of ESG issues at scale. This includes natural language processing and requires entity recognition and other applications to support the work of IFC’s experts and other investors working in emerging markets. These algorithms are available via IFC’s Machine Learning ESG Analyst (MALENA) platform to enable rapid analysis, increase productivity, and build investor confidence. In this manner, IFC, a development finance institution with the mandate to address poverty in emerging markets, is making use of its historical datasets and open source AI solutions to build custom-AI applications that democratize access to ESG capacity to read and classify text.

In this session, you will learn the unique flexibility of the Apache Spark™ ecosystem from Databricks and how that has allowed IFC’s MALENA project to connect to scalable data lake storage, use different natural language processing models and seamlessly adopt MLOps.

Talk by: Atiyah Curmally and Blaise Sandwidi

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

As part of Comcast Effectv’s transformation into a completely digital advertising agency, it was key to develop an approach to manage and remediate data quality issues related to customer data so that the sales organization is using reliable data to enable data-driven decision making. Like many organizations, Effectv's customer lifecycle processes are spread across many systems utilizing various integrations between them. This results in key challenges like duplicate and redundant customer data that requires rationalization and remediation. Data is at the core of Effectv’s modernization journey with the intended result of winning more business, accelerating order fulfillment, reducing make-goods and identifying revenue.

In partnership with Slalom Consulting, Comcast Effectv built a traditional lakehouse on Databricks to ingest data from all of these systems but with a twist; they anchored every engineering decision in how it will enable their data governance program.

In this session, we will touch upon the data transformation journey at Effectv and dive deeper into the implementation of data governance leveraging Databricks solutions such as Delta Lake, Unity Catalog and DB SQL. Key focus areas include how we baked master data management into our pipelines by automating the matching and survivorship process, and bringing it all together for the data consumer via DBSQL to use our certified assets in bronze, silver and gold layers.

By making thoughtful decisions about structuring data in Unity Catalog and baking MDM into ETL pipelines, you can greatly increase the quality, reliability, and adoption of single-source-of-truth data so your business users can stop spending cycles on wrangling data and spend more time developing actionable insights for your business.

Talk by: Maggie Davis and Risha Ravindranath

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Procore is a construction project management software that helps construction professionals efficiently manage their projects and collaborate with their teams. Our mission is to connect everyone in construction on a global platform.

Procore is the system of record for all construction projects. Our customers need to access the data in near real-time for construction insights. Enhanced reporting is a self-service operational reporting module that allows quick data access with consistency to thousands of tables and reports.

Procore data platform rebuilt the module (originally built on the relational database) using Databricks and Delta lake. We used Apache Spark™ streaming to maintain the consistent state on the ingestion side from Kafka and plan to leverage the fully capable functionalities of DBSQL using the serverless SQL warehouse to read the medallion models (built via DBT) in Delta Lake. In addition, the Unity Catalog and the Delta share features helped us share the data across regions seamlessly. This design enabled us to improve the p95 and p99 read time by xx% (which were initially timing out).

Attend this session to hear about the learnings and experience of building a Data Lakehouse architecture.

Talk by: Jay Yang and Hari Rajaram

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Akamai's content delivery network (CDN) processes about 30% of the internet's daily traffic, resulting in a massive amount of data that presents engineering challenges, both internally and with cloud vendors. In this session, we will discuss the barriers faced while building a data infrastructure on Azure, Databricks, and Kafka to meet strict SLAs, hitting the limits of some of our cloud vendors’ services. We will describe the iterative process of re-architecting a massive scale data platform using the aforementioned technologies.

We will also delve into how today, Akamai is able to quickly ingest and make available to customers terabytes of data, as well as efficiently query Petabytes of data and return results within 10 seconds for most queries. This discussion will provide valuable insights for attendees and organizations seeking to effectively process and analyze large amounts of data.

Evaluating LLM-based applications can feel like more of an art than a science. In this workshop, we'll give a hands-on introduction to evaluating language models. You'll come away with knowledge and tools you can use to evaluate your own applications, and answers to questions like:

• Where do I get evaluation data from, anyway?
• Is it possible to evaluate generative models in an automated way?
• What metrics can I use?
• What's the role of human evaluation?

Talk by: Josh Tobin

Here’s more to explore: LLM Compact Guide: https://dbricks.co/43WuQyb Big Book of MLOps: https://dbricks.co/3r0Pqiz

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc