Send us a text Datatopics is a podcast presented by Kevin Missoorten to talk about the fuzzy and misunderstood concepts in the world of data, analytics, and AI and get to the bottom of things.

In this episode, together with expert guests Jeroen, Guillaume and our own Murilo, we dive deep into the fascinating world of Edge AI. Where “traditional” AI models can be accessed remotely they often run centrally on a cloud instance, but what do you do when you need an immediate response, or when you don’t want the data to be sent or don’t always have connection? In that case you can use ‘smaller’ models deployed on a device, this is called Edge AI. Edge AI can bring many benefits but there are still some challenges as well. Tune in to DataTopics to hear our take on Edge AI, what business value it can bring, where it is today and where we see it evolve to next! Datatopics is brought to you by Dataroots Music: The Gentlemen - DivKidThe thumbnail is generated by Midjourney

Summary

Cloud data warehouses and the introduction of the ELT paradigm has led to the creation of multiple options for flexible data integration, with a roughly equal distribution of commercial and open source options. The challenge is that most of those options are complex to operate and exist in their own silo. The dlt project was created to eliminate overhead and bring data integration into your full control as a library component of your overall data system. In this episode Adrian Brudaru explains how it works, the benefits that it provides over other data integration solutions, and how you can start building pipelines today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Adrian Brudaru about dlt, an open source python library for data loading

Interview

Introduction How did you get involved in the area of data management? Can you describe what dlt is and the story behind it?

What is the problem you want to solve with dlt? Who is the target audience?

The obvious comparison is with systems like Singer/Meltano/Airbyte in the open source space, or Fivetran/Matillion/etc. in the commercial space. What are the complexities or limitations of those tools that leave an opening for dlt? Can you describe how dlt is implemented? What are the benefits of building it in Python? How have the design and goals of the project changed since you first started working on it? How does that language choice influence the performance and scaling characteristics? What problems do users solve with dlt? What are the interfaces available for extending/customizing/integrating with dlt? Can you talk through the process of adding a new source/destination? What is the workflow for someone building a pipeline with dlt? How does the experience scale when supporting multiple connections? Given the limited scope of extract and load, and the composable design of dlt it seems like a purpose built companion to dbt (down to th

IBM® Storage as a Service (STaaS) extends your hybrid cloud experience with a new flexible consumption model enabled for both your on-premises and hybrid cloud infrastructure needs, giving you the agility, cash flow efficiency, and services of cloud storage with the flexibility to dynamically scale up or down and only pay for what you use beyond the minimal capacity. This IBM Redpaper provides a detailed introduction to the IBM STaaS service. The paper is targeted for data center managers and storage administrators.

In this session, you will learn about scenarios in a multi-cloud environment where an enterprise must exert control over and manage its own data, wherever it is. We'll also discuss multi-cloud data management strategies which will allow enterprises to take advantage of evolving technology and maintain regulatory compliance at the same time.

This IBM® Redpaper publication is a comprehensive guide that covers the IBM Power E1050 server (9043-MRX) that uses the latest IBM Power10 processor-based technology and supports IBM AIX® and Linux operating systems (OSs). The goal of this paper is to provide a hardware architecture analysis and highlight the changes, new technologies, and major features that are being introduced in this system, such as: The latest IBM Power10 processor design, including the dual-chip module (DCM) packaging, which is available in various configurations from 12 - 24 cores per socket. Support of up to 16 TB of memory. Native Peripheral Component Interconnect Express (PCIe) 5th generation (Gen5) connectivity from the processor socket to deliver higher performance and bandwidth for connected adapters. Open Memory Interface (OMI) connected Differential Dual Inline Memory Module (DDIMM) memory cards delivering increased performance, resiliency, and security over industry-standard memory technologies, including transparent memory encryption. Enhanced internal storage performance with the use of native PCIe-connected Non-volatile Memory Express (NVMe) devices in up to 10 internal storage slots to deliver up to 64 TB of high-performance, low-latency storage in a single 4-socket system. Consumption-based pricing in the Power Private Cloud with Shared Utility Capacity commercial model to allow customers to consume resources more flexibly and efficiently, including AIX, Red Hat Enterprise Linux (RHEL), SUSE Linux Enterprise Server, and Red Hat OpenShift Container Platform workloads. This publication is for professionals who want to acquire a better understanding of IBM Power products. The intended audience includes: IBM Power customers Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper expands the set of IBM Power documentation by providing a desktop reference that offers a detailed technical description of the Power E1050 Midrange server model. This paper does not replace the current marketing materials and configuration tools. It is intended as an extra source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions..

This IBM® Redpaper® publication provides a broad understanding of a new architecture of the IBM Power® E1080 (also known as the Power E1080) server that supports IBM AIX®, IBM i, and selected distributions of Linux operating systems. The objective of this paper is to introduce the Power E1080, the most powerful and scalable server of the IBM Power portfolio, and its offerings and relevant functions: Designed to support up to four system nodes and up to 240 IBM Power10™ processor cores The Power E1080 can be initially ordered with a single system node or two system nodes configuration, which provides up to 60 Power10 processor cores with a single node configuration or up to 120 Power10 processor cores with a two system nodes configuration. More support for a three or four system nodes configuration is to be added on December 10, 2021, which provides support for up to 240 Power10 processor cores with a full combined four system nodes server. Designed to supports up to 64 TB memory The Power E1080 can be initially ordered with the total memory RAM capacity up to 8 TB. More support is to be added on December 10, 2021 to support up to 64 TB in a full combined four system nodes server. Designed to support up to 32 Peripheral Component Interconnect® (PCIe) Gen 5 slots in a full combined four system nodes server and up to 192 PCIe Gen 3 slots with expansion I/O drawers The Power E1080 supports initially a maximum of two system nodes; therefore, up to 16 PCIe Gen 5 slots, and up to 96 PCIe Gen 3 slots with expansion I/O drawer. More support is to be added on December 10, 2021, to support up to 192 PCIe Gen 3 slots with expansion I/O drawers. Up to over 4,000 directly attached serial-attached SCSI (SAS) disks or solid-state drives (SSDs) Up to 1,000 virtual machines (VMs) with logical partitions (LPARs) per system System control unit, providing redundant system master Flexible Service Processor (FSP) Supports IBM Power System Private Cloud Solution with Dynamic Capacity This publication is for professionals who want to acquire a better understanding of Power servers. The intended audience includes the following roles: Customers Sales and marketing professionals Technical support professionals IBM Business Partners Independent software vendors (ISVs) This paper does not replace the current marketing materials and configuration tools. It is intended as an extra source of information that, together with existing sources, can be used to enhance your knowledge of IBM server solutions.

Serverless Machine Learning with Amazon Redshift ML provides a hands-on guide to using Amazon Redshift Serverless and Redshift ML for building and deploying machine learning models. Through SQL-focused examples and practical walkthroughs, you will learn efficient techniques for cloud data analytics and serverless machine learning. What this Book will help me do Grasp the workflow of building machine learning models with Redshift ML using SQL. Learn to handle supervised learning tasks like classification and regression. Apply unsupervised learning techniques, such as K-means clustering, in Redshift ML. Develop time-series forecasting models within Amazon Redshift. Understand how to operationalize machine learning in serverless cloud architecture. Author(s) Debu Panda, Phil Bates, Bhanu Pittampally, and Sumeet Joshi are seasoned professionals in cloud computing and machine learning technologies. They combine deep technical knowledge with teaching expertise to guide learners through mastering Amazon Redshift ML. Their collaborative approach ensures that the content is accessible, engaging, and practically applicable. Who is it for? This book is perfect for data scientists, machine learning engineers, and database administrators using or intending to use Amazon Redshift. It's tailored for professionals with basic knowledge of machine learning and SQL who aim to enhance their efficiency and specialize in serverless machine learning within cloud architectures.

Summary

Data persistence is one of the most challenging aspects of computer systems. In the era of the cloud most developers rely on hosted services to manage their databases, but what if you are a cloud service? In this episode Vignesh Ravichandran explains how his team at Cloudflare provides PostgreSQL as a service to their developers for low latency and high uptime services at global scale. This is an interesting and insightful look at pragmatic engineering for reliability and scale.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Vignesh Ravichandran about building an internal database as a service platform at Cloudflare

Interview

Introduction How did you get involved in the area of data management? Can you start by describing the different database workloads that you have at Cloudflare?

What are the different methods that you have used for managing database instances?

What are the requirements and constraints that you had to account for in designing your current system? Why Postgres? optimizations for Postgres

simplification from not supporting multiple engines

limitations in postgres that make multi-tenancy challenging scale of operation (data volume, request rate What are the most interesting, innovative, or unexpected ways that you have seen your DBaaS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on your internal database platform? When is an internal database as a service the wrong choice? What do you have planned for the future of Postgres hosting at Cloudflare?

Contact Info

LinkedIn Website

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Mac

Unlock the full potential of Microsoft Business Central by integrating it with the Power Platform through this practical and hands-on guide. With step-by-step tutorials, you'll learn how to combine the capabilities of tools like Power Apps, Power Automate, and Dataverse to build scalable and efficient business solutions. By the end of the book, you'll be equipped to streamline business processes and add significant value. What this Book will help me do Effectively deploy Power Platform functionalities for Microsoft Business Central projects. Seamlessly connect Business Central with cloud and on-premises services. Leverage Dataverse and virtual tables to enhance data modeling and accessibility. Build custom applications using Power Apps and automate workflows with Power Automate. Generate advanced visual reports with Power BI directly integrated with Business Central. Author(s) Kim Congleton and Shawn Sissenwein are industry professionals with extensive experience in ERP systems and Microsoft technologies. With a deep knowledge of Business Central and the Power Platform, they bring practical insights into maximizing business value through technological advancements. Their teaching approach focuses on hands-on learning, real-world application, and empowering readers with actionable skills. Who is it for? This book is ideal for Business Central users, consultants, and solution architects aiming to enhance Business Central's capabilities through the Power Platform. If you're familiar with Business Central's basics and seek to optimize and extend its functionality without requiring extensive programming knowledge, then this guide is tailored for you.

By choosing the right database, you can maximize your business potential, improve performance, increase efficiency, and gain a competitive edge. This insightful report examines the benefits of using a simplified data architecture containing cloud-based HTAP (hybrid transactional and analytical processing) database capabilities. You'll learn how this data architecture can help data engineers and data decision makers focus on what matters most: growing your business. Authors Joe McKendrick and Ed Huang explain how cloud native infrastructure supports enterprise businesses and operations with a much more agile foundation. Just one layer up from the infrastructure, cloud-based databases are a crucial part of data management and analytics. Learn how distributed SQL databases containing HTAP capabilities provide more efficient and streamlined data processing to improve cost efficiency and expedite business operations and decision making. This report helps you: Explore industry trends in database development Learn the benefits of a simplified data architecture Comb through the complex and crowded database choices on the market Examine the process of selecting the right database for your business Learn the latest innovations database for improving your company's efficiency and performance

Summary

Generative AI has unlocked a massive opportunity for content creation. There is also an unfulfilled need for experts to be able to share their knowledge and build communities. Illumidesk was built to take advantage of this intersection. In this episode Greg Werner explains how they are using generative AI as an assistive tool for creating educational material, as well as building a data driven experience for learners.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Greg Werner about building IllumiDesk, a data-driven and AI powered online learning platform

Interview

Introduction How did you get involved in the area of data management? Can you describe what Illumidesk is and the story behind it? What are the challenges that educators and content creators face in developing and maintaining digital course materials for their target audiences? How are you leaning on data integrations and AI to reduce the initial time investment required to deliver courseware? What are the opportunities for collecting and collating learner interactions with the course materials to provide feedback to the instructors? What are some of the ways that you are incorporating pedagogical strategies into the measurement and evaluation methods that you use for reports? What are the different categories of insights that you need to provide across the different stakeholders/personas who are interacting with the platform and learning content? Can you describe how you have architected the Illumidesk platform? How have the design and goals shifted since you first began working on it? What are the strategies that you have used to allow for evolution and adaptation of the system in order to keep pace with the ecosystem of generative AI capabilities? What are the failure modes of the content generation that you need to account for? What are the most interesting, innovative, or unexpected ways that you have seen Illumidesk us

This talk was recorded at Crunch Conference 2022. Zoltán and Gergely from Aliz.ai company spoke about FinOps examples using Google Cloud BigQuery.

"In this talk we will talk about the basics of FinOps concept and going to make an introduction to it through real life examples using BigQuery."

The event was organized by Crafthub.

You can watch the rest of the conference talks on our channel.

If you are interested in more speakers, tickets and details of the conference, check out our website: https://crunchconf.com/ If you are interested in more events from our company: https://crafthub.events/

This talk was recorded at Crunch Conference. Wouter from Datadog spoke about data infrastructure in a multi-cloud environment.

"In this talk we'll talk about the Data Engineering Platform Datadog has built in multiple clouds. We'll discuss why, how, how it's used and the challenges we've faced."

The event was organized by Crafthub.

You can watch the rest of the conference talks on our channel.

If you are interested in more speakers, tickets and details of the conference, check out our website: https://crunchconf.com/

If you are interested in more events from our company: https://crafthub.events/

Discover the power of SAP Integration Suite's capabilities with this hands-on guide. Learn how this integration platform (iPaaS) can help you connect and automate your business processes with integrations, connectors, APIs, and best practices for a faster ROI. Over the course of this book, you will explore the powerful capabilities of SAP Integration Suite, including API Management, Open Connectors, Integration Advisor, Trading Partner Management, Migration Assessment, and Integration Assessment. With detailed explanations and real-world examples, this book is the perfect resource for anyone looking to unlock the full potential of SAP Integration Suite. With each chapter, you'll gain a greater understanding of why SAP Integration Suite can be the proverbial swiss army knife in your toolkit to design and develop enterprise integration scenarios, offering simplified integration, security, and governance for your applications. Author Jaspreet Bagga demonstrates howto create, publish, and monitor APIs with SAP API Management, and how to use its features to enhance your API lifecycle. He also provides a detailed walkthrough of how other capabilities of SAP Integration Suite can streamline your connectivity, design, development, and architecture methodology with a tool-based approach completely managed by SAP. Whether you are a developer, an architect, or a business user, this book will help you unlock the potential of SAP's Integration Suite platform, API Management, and accelerate your digital transformation. What You Will Learn Understand what APIs are, what they are used for, and why they are crucial for building effective and reliable applications Gain an understanding of SAP Integration Suite's features and benefits Study SAP Integration assessment process, patterns, and much more Explore tools and capabilities other than the Cloud Integration that address the full value chain of the enterprise integration components Who This Book Is For Web developers and application leads who want to learn SAP API Management.

New to Oracle Global Data Services? You’ve come to the right place. This book will show you how to leverage the power of Oracle GDS to ensure runtime load balancing, region affinity, replication lag tolerance-based workload routing, and inter-database service failover. In particular, you will see how to maximize the utilization of replication investments with Oracle GDS. The book starts by guiding you through the installation and configuration of GDS and provides details for each component in the GDS framework. Next, you’ll learn how to configure various components of Oracle GDS in standalone environments. Hands-on exercises that explore the advantages of GDS with different test cases utilizing Active Data Guard (ADG), Oracle GoldenGate (OGG), and Oracle Real Application Clusters (RAC) will help you put your learning in context. The book concludes with a demonstration of how to add Oracle GDS to OEM for monitoring and troubleshooting. You’ll also see how to monitor Oracle GDS in a centralized location using Oracle Enterprise Manager Cloud Control. After completing this book, you will understand the architecture, components, and implementation strategies of GDS using ADG and OGG in mission-critical environments. What You Will Learn Understand Oracle Global Data Services architecture and its various components Install and configure Oracle Global Data Services Use Global Data Services with Active Data Guard and Oracle Golden Gate. Monitor Global Data Services using Oracle Enterprise Manager Cloud Control. Troubleshoot issues in Global Data Services Who This Book Is For Oracle database administrators, Oracle database architects, Oracle technical managers, Oracle application business analysts, and Oracle data engineers.

As companies scale and become more successful, new horizons open, but with them come unexpected challenges. The influx of revenue and expansion of operations often reveal hidden complexities that can hinder efficiency and inflate costs. In this tricky situation, data teams can find themselves entangled in a web of obstacles that slow down their ability to innovate and respond to ever-changing business needs. Enter cloud analytics—a transformative solution that promises to break down barriers and unleash potential. By migrating analytics to the cloud, organizations can navigate the growing pains of success, cutting costs, enhancing flexibility, and empowering data teams to work with agility and precision. John Knieriemen is the Regional Business Lead for North America at Exasol, the market-leading high-performance analytics database. Prior to joining Exasol, he served as Vice President and General Manager at Teradata during an 11-year tenure with the company. John is responsible for strategically scaling Exasol’s North America business presence across industries and expanding the organization’s partner network.  Solongo Erdenekhuyag is the former Customer Success and Data Strategy Leader at Exasol. Solongo is skilled in strategy, business development, program management, leadership, strategic partnerships, and management. In the episode, Richie, Solongo, and John cover the motivation for moving analytics to the cloud, economic triggers for migration, success stories from organizations who have migrated to the cloud, the challenges and potential roadblocks in migration, the importance of flexibility and open-mindedness and much more.  Links from the Show ExasolAmazon S3Azure Blob StorageGoogle Cloud StorageBigQueryAmazon RedshiftSnowflake[Course] Understanding Cloud Computing[Course] AWS Cloud Concepts

Send us a text He's BACK!  Roger Premo, General Manager, Corporate Strategy and Ventures Development at IBM.  How the world has changed in a short year.  Generative AI and more!   02:29 Meet Roger Premo Take 205:52 A Changing World08:18 Generative AI12:48 Both Sides of the Story14:22 Hybrid Cloud and AI20:50 IBM's watsonx25:53 What Have We Learned?27:46 Enterprise Models29:59 Hugging Face31:03 IBM's Differentiation32:23 The 2 min Bar Pitch35:57 Three Questions42:21 An Intentional Hybrid Cloud Architecture 46:40 Responsible AILinkedin: https://www.linkedin.com/in/ropremo/ Website: https://www.ibm.com/watsonx Want to be featured as a guest on Making Data Simple?  Reach out to us at [email protected] and tell us why you should be next.  The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Daniel Le is the CFO at dbt Labs where he has built multiple teams. He is also the former head of FP&A and operations at Zoom, and he helped scale FP&A as the former finance director at Okta.  In this conversation with Julia, Daniel shares his view as CFO on the challenges SaaS companies face and the importance of finance teams creating a holistic view of their business. Daniel gives advice to data leaders about how they can automate business processes with dbt Cloud and use self-service analytics to automate revenue recognition, generate consistent headcount analytics, and more to impact their organization. Read more about Daniel's story here. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.  The Analytics Engineering Podcast is sponsored by dbt Labs.

While we may not all agree on a “If it ain’t broke, don’t fix it” approach, we can all agree that “If it shows any crack, migrate it to the cloud and completely re-architect it.” Akamai’s CSI (Cloud Security Intelligence) group is responsible for processing massive amounts of security events arriving from our edge network, which is estimated to process 30% of internet traffic, making it accessible by various internal consumers powering customer-facing products.

In this session, we will visit the reasons for migrating one of our mission critical security products and its 10GB ingest pipeline to the cloud, examine our new architecture and its benefits and touch on the challenges we faced during the process (and still do). While our requirements are unique and our solution contains a few proprietary components, this session will provide you with several concepts involving popular off-the-shelf products you can easily use in your own cloud environment.

Talk by: Yaniv Kunda

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

Data and AI are revolutionizing industries and transforming businesses at an unprecedented pace. These advancements pave the way for groundbreaking outcomes such as fresh revenue streams, optimized working capital, and captivating, personalized customer experiences.

Join Hugh Burgin, Luke Pritchard and Dan Diasio as we explore a range of real-world examples of AI and data-driven transformation opportunities being powered by Databricks, including business value realized and technical solutions implemented. We will focus on how to integrate and leverage business insights, a diverse network of cloud-based solutions and Databricks to unleash new business value opportunities. By highlighting real-world use cases we will discuss:

• Examples of how Manufacturing, Retail, Financial Services and other sectors are using Databricks services to scale AI, gain insights that matter and secure their data
• The ways data monetization are changing how companies view data and incentivizing better data management
• Examples of Generative AI and LLMs changing how businesses operate, how their customers engage, and what you can do about it

Talk by: Hugh Burgin and Luke Pritchard

Here’s more to explore: State of Data + AI Report: https://dbricks.co/44i2HBp 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/databricksin