Speakers: Ken Wong, Sr. Director of Product Management, Databricks Miranda Luna, Product Management, Databricks

Speakers: - Alexander Booth, Asst Director of Research & Development, Texas Rangers - Ali Ghodsi, Co-Founder and CEO, Databricks - Bilal Aslam, Sr. Director of Product Management, Databricks - Darshana Sivakumar, Staff Product Manager, Databricks - Hannes Mühleisen, Creator of DuckDB, DuckDB Labs - Matei Zaharia, Chief Technology Officer and Co-Founder, Databricks - Reynold Xin, Chief Architect and Co-Founder, Databricks - Ryan Blue, CEO, Tabular - Tareef Kawaf, President, Posit Software, PBC - Yejin Choi, Sr Research Director Commonsense AI, AI2, University of Washington - Zeashan Pappa, Staff Product Manager, Databricks

About Databricks Databricks is the Data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Conde Nast, Rivian, and Shell, and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI. Databricks is headquartered in San Francisco, with offices around the globe, and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

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

Databricks Data + AI Summit 2024 Keynote Day 1

Experts, researchers and open source contributors — from Databricks and across the data and AI community gathered in San Francisco June 10 - 13, 2024 to discuss the latest technologies in data management, data warehousing, data governance, generative AI for the enterprise, and data in the era of AI.

Hear from Databricks Co-founder and CEO Ali Ghodsi on building generative AI applications, putting your data to work, and how data + AI leads to data intelligence.

Plus a fireside chat between Ali Ghodsi and Nvidia Co-founder and CEO, Jensen Huang, on the expanded partnership between Nvidia and Databricks to accelerate enterprise data for the era of generative AI

Product announcements in the video include: - Databricks Data Intelligence Platform - Native support for NVIDIA GPU acceleration on the Databricks Data Intelligence Platform - Databricks open source model DBRX available as an NVIDIA NIM microservice - Shutterstock Image AI powered by Databricks - Databricks AI/BI - Databricks LakeFlow - Databricks Mosaic AI - Mosaic AI Agent Framework - Mosaic AI Agent Evaluation - Mosaic AI Tools Catalog - Mosaic AI Model Training - Mosaic AI Gateway

In this keynote hear from: - Ali Ghodsi, Co-founder and CEO, Databricks (1:45) - Brian Ames, General Motors (29:55) - Patrick Wendall, Co-founder and VP of Engineering, Databricks (38:00) - Jackie Brosamer, Head of AI, Data and Analytics, Block (1:14:42) - Fei Fei Li, Professor, Stanford University and Denning Co-Director, Stanford Institute for Human-Centered AI (1:23:15) - Jensen Huang, Co-founder and CEO of NVIDIA with Ali Ghodsi, Co-founder and CEO of Databricks (1:42:27) - Reynold Xin, Co-founder and Chief Architect, Databricks (2:07:43) - Ken Wong, Senior Director, Product Management, Databricks (2:31:15) - Ali Ghodsi, Co-founder and CEO, Databricks (2:48:16)

This book serves as the ultimate preparation for aspiring Databricks Certified Associate Developers specializing in Apache Spark. Deep dive into Spark's components, its applications, and exam techniques to achieve certification and expand your practical skills in big data processing and real-time analytics using Python. What this Book will help me do Deeply understand Apache Spark's core architecture for building big data applications. Write optimized SQL queries and leverage Spark DataFrame API for efficient data manipulation. Apply advanced Spark functions, including UDFs, to solve complex data engineering tasks. Use Spark Streaming capabilities to implement real-time and near-real-time processing solutions. Get hands-on preparation for the certification exam with mock tests and practice questions. Author(s) Saba Shah is a seasoned data engineer with extensive experience working at Databricks and leading data science teams. With her in-depth knowledge of big data applications and Spark, she delivers clear, actionable insights in this book. Her approach emphasizes practical learning and real-world applications. Who is it for? This book is ideal for data professionals such as engineers and analysts aiming to achieve Databricks certification. It is particularly helpful for individuals with moderate Python proficiency who are keen to understand Spark from scratch. If you're transitioning into big data roles, this guide prepares you comprehensively.

Ali Ghodsi spoke to an audience of thousands at the Data + AI Summit keynote in San Francisco on the three biggest challenges in enterprise GenAI today and how the Databricks Data Intelligence platform is helping organizations solve them.

In "Data Engineering with Databricks Cookbook," you'll learn how to efficiently build and manage data pipelines using Apache Spark, Delta Lake, and Databricks. This recipe-based guide offers techniques to transform, optimize, and orchestrate your data workflows. What this Book will help me do Master Apache Spark for data ingestion, transformation, and analysis. Learn to optimize data processing and improve query performance with Delta Lake. Manage streaming data processing with Spark Structured Streaming capabilities. Implement DataOps and DevOps workflows tailored for Databricks. Enforce data governance policies using Unity Catalog for scalable solutions. Author(s) Pulkit Chadha, the author of this book, is a Senior Solutions Architect at Databricks. With extensive experience in data engineering and big data applications, he brings practical insights into implementing modern data solutions. His educational writings focus on empowering data professionals with actionable knowledge. Who is it for? This book is ideal for data engineers, data scientists, and analysts who want to deepen their knowledge in managing and transforming large datasets. Readers should have an intermediate understanding of SQL, Python programming, and basic data architecture concepts. It is especially well-suited for professionals working with Databricks or similar cloud-based data platforms.

This book is your gateway to mastering the skills required for achieving the Azure Data Engineer Associate certification (DP-203). Whether you're new to the field or a seasoned professional, it comprehensively prepares you for the challenges of the exam. Learn to design and implement advanced data solutions, secure sensitive information, and optimize data processes effectively. What this Book will help me do Understand and utilize Azure's data services such as Azure Synapse and Azure Databricks for data processing. Master advanced data storage and management solutions, including designing partitions and lake architectures. Learn to secure data with state-of-the-art tools like RBAC, encryption, and Azure Purview. Develop and manage data pipelines and workflows using tools like Azure Data Factory (ADF) and Spark. Prepare for and confidently pass the DP-203 certification exam with the included practical resources and guidance. Author(s) The authors, None Palmieri, Surendra Mettapalli, and None Alex, bring a wealth of expertise in cloud and data engineering. With extensive industry experience, they've designed this guide to be both educational and practical, enabling learners to not only understand but also apply concepts in real-world scenarios. Their goal is to make complex topics approachable, supporting your journey to certification success. Who is it for? This guide is perfect for aspiring and current data engineers aiming to achieve the Azure Data Engineer Associate certification (DP-203). It's particularly useful for professionals familiar with cloud services and basic data engineering concepts who want to delve deeper into Azure's offerings. Additionally, managers and learners preparing for roles involving Azure cloud data solutions will find the content invaluable for career advancement.

Dive into the Databricks Data Intelligence Platform and learn how to harness its full potential for creating, deploying, and maintaining machine learning solutions. This book covers everything from setting up your workspace to integrating state-of-the-art tools such as AutoML and VectorSearch, imparting practical skills through detailed examples and code. What this Book will help me do Set up and manage a Databricks workspace tailored for effective data science workflows. Implement monitoring to ensure data quality and detect drift efficiently. Build, fine-tune, and deploy machine learning models seamlessly using Databricks tools. Operationalize AI projects including feature engineering, data pipelines, and workflows on the Databricks Lakehouse architecture. Leverage integrations with popular tools like OpenAI's ChatGPT to expand your AI project capabilities. Author(s) This book is authored by Stephanie Rivera, Anastasia Prokaieva, Amanda Baker, and Hayley Horn, seasoned experts in data science and machine learning from Databricks. Their collective years of expertise in big data and AI technologies ensure a rich and insightful perspective. Through their work, they strive to make complex concepts accessible and actionable. Who is it for? This book serves as an ideal guide for machine learning engineers, data scientists, and technically inclined managers. It's well-suited for those transitioning to the Databricks environment or seeking to deepen their Databricks-based machine learning implementation skills. Whether you're an ambitious beginner or an experienced professional, this book provides clear pathways to success.

Eric Avidon is a journalist at TechTarget who's interviewed Tristan a few times, and now Tristan gets to flip the script and interview Eric. Eric is a journalist veteran, covering everything from finance to the Boston Red Sox, but now he spends a lot of time with vendors in the data space and has a broad view of what's going on. Eric and Tristan discuss AI and analytics and how mature these features really are today, data quality and its importance, the AI strategies of Snowflake and Databricks, and a lot more. Plus, part way through you can hear Tristan reacting to a mild earthquake that hit the East Coast. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode, we're joined by special guest Maryam Ilyas as we delve into a variety of topics that shape our digital world: Women’s Healthcare Insights: Exploring the Oura ring's commitment during Women's Health Awareness Month and its role in addressing the underrepresentation of female health conditions in research. A Deep Dive into the EU AI Act: Examining the AI Act’s implications, including its classification of AI systems (prohibited, high-risk, limited-risk, and minimal-risk), ethical concerns, regulatory challenges & the act's impact on AI usage, particularly regarding mass surveillance at the Paris Olympics.The Evolution of Music and AI: Reviewing the AI-generated music video for "The Hardest Part" by Washed Out, directed by Paul Trillo, showcasing AI’s growing role in the arts.Hot Takes on Data Tools: Is combining SQL, PySpark (and Python) in Databricks the most powerful tool in the data space? Let's dissect the possibilities and limitations.Don't forget to check us out on Youtube too, where you can find a lot more content beyond the podcast!

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style!

In this episode, we're thrilled to have special guest Mehdi Ouazza  diving into a plethora of hot tech topics: Mehdi Ouazza's Insights into his career, online community and working with DuckDB and MotherDuck.Demystifying DevRel: Definitions and distinctions in the realm of tech influence (dive deeper here).Terraform's Licensing Shift: Reactions to HashiCorp's recent changes and its new IBM collaboration, more details here.Github Copilot Workspace: Exploring the latest in AI-powered coding assistance, comparing with devin.ai and CodySnowflake's Arctic LLM: Discussing the latest enterprise AI capabilities and their real-world applications. Read more about Arctic - what it excels at, and how its performance was measuredMore legal kerfuffle in the GenAI realm: The ongoing legal debates around AI's use in creative industries, highlighted by a dispute over Drake’s use of late rapper Tupac’s AI-generated voice in diss track & the licensing deal between Financial Times and OpenAIFuture of Data Engineering: Examining the integration of LLMs into data engineering tools. Insights on prompt-based feature engineering and Databricks' English SDKAI in Music Creation: A little bonus with an AI generated song about Murilo, created with Suno

Explore the transformative power of Generative AI with Databricks on Google Cloud. Learn to integrate and leverage governed, sensitive data for real-world problem-solving, from advanced RAG applications to traditional challenges. With 88% of enterprises investing in Generative AI, discover how to swiftly develop and deploy tailored solutions, which could include Q&A bots and custom models. Gain insights from a customer’s experience and propel your organization into the forefront of AI innovation. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

The data landscape is evolving rapidly, with generative AI poised to revolutionize insight generation and data culture. Join experts from Databricks, MongoDB, Confluent, and Dataiku for an exclusive executive discussion on harnessing gen AI's transformative potential. We'll explore how to break down multicloud data silos, empowering informed decision-making and unlocking your data's full value with gen AI. Discover strategies for integrating gen AI, addressing challenges, and building a future-proof, innovation-driven data culture.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, "Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications." Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ 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.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don't), where industry insights meet laid-back banter. Whether you're a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let's get into the heart of data, unplugged style! In this episode #42, titled "Unraveling the Fabric of Data: Microsoft's Ecosystem and Beyond," we're joined once again by the tech maestro and newly minted Microsoft MVP, Sam Debruyn. Sam brings to the table a bevy of updates from his recent accolades to the intricacies of Microsoft's data platforms and the world of SQL.

Biz Buzz: From Reddit's IPO to the performance versus utility debate in database selection, we dissect the big moves shaking up the business side of tech. Read about Reddit's IPO.Microsoft's Fabric Unraveled: Get the lowdown on Microsoft's Fabric, the one-stop AI platform, as Sam Debruyn gives us a deep dive into its capabilities and integration with Azure Databricks and Power BI. Discover more about Fabric and dive into Sam's blog.dbt Developments: Sam talks dbt and the exciting new SQL tool for data pipeline building with upcoming unit testing capabilities.Polaris Project: Delving into Microsoft's internal storage projects, including insights on Polaris and its integration with Synapse SQL. Read the paper here.AI Advances: From the release of Grok-1 and Apple's MM1 AI model to GPT-4's trillion parameters, we discuss the leaps in artificial intelligence.Stability in Motion: After OpenAI's Sora, we look at Stability AI's new venture into motion with Stable Video. Check out Stable Video.Benchmarking Debate: A critical look at performance benchmarks in database selection and the ongoing search for the 'best' database. Contemplate benchmarking perspectives.Versioning Philosophy: Hot takes on semantic versioning and what stability really means in software development. Dive into Semantic Versioning.

This comprehensive guide to Azure Data Factory shows you how to create robust data pipelines and workflows to handle both cloud and on-premises data solutions. Through practical recipes, you will learn to build, manage, and optimize ETL, hybrid ETL, and ELT processes. The book offers detailed explanations to help you integrate technologies like Azure Synapse, Data Lake, and Databricks into your projects. What this Book will help me do Master building and managing data pipelines using Azure Data Factory's latest versions and features. Leverage Azure Synapse and Azure Data Lake for streamlined data integration and analytics workflows. Enhance your ETL/ELT solutions with Microsoft Fabric, Databricks, and Delta tables. Employ debugging tools and workflows in Azure Data Factory to identify and solve data processing issues efficiently. Implement industry-grade best practices for reliable and efficient data orchestration and integration pipelines. Author(s) Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, and Xenia Ireton collectively bring years of expertise in data engineering and cloud-based solutions. They are recognized professionals in the Azure ecosystem, dedicated to sharing their knowledge through detailed and actionable content. Their collaborative approach ensures that this book provides practical insights for technical audiences. Who is it for? This book is ideal for data engineers, ETL developers, and professional architects who work with cloud and hybrid environments. If you're looking to upskill in Azure Data Factory or expand your knowledge into related technologies like Synapse Analytics or Databricks, this is for you. Readers should have a foundational understanding of data warehousing concepts to fully benefit from the material.

We’ve heard so much about the value and capabilities of generative AI over the past year, and we’ve all become accustomed to the chat interfaces of our preferred models. One of the main concerns many of us have had has been privacy. Is OpenAI keeping the data and information I give to ChatGPT secure? One of the touted solutions to this problem is running LLMs locally on your own machine, but with the hardware cost that comes with it, running LLMs locally has not been possible for many of us. That might now be starting to change. Nuri Canyaka is VP of AI Marketing at Intel. Prior to Intel, Nuri spent 16 years at Microsoft, starting out as a Technical Evangelist, and leaving the organization as the Senior Director of Product Marketing. He ran the GTM team that helped generate adoption of GPT in Microsoft Azure products. La Tiffaney Santucci is Intel’s AI Marketing Director, specializing in their Edge and Client products. La Tiffaney has spent over a decade at Intel, focussing on partnerships with Dell, Google Amazon and Microsoft.  In the episode, Richie, Nuri and La Tiffaney explore AI’s impact on marketing analytics, the adoptions of AI in the enterprise, how AI is being integrated into existing products, the workflow for implementing AI into business processes and the challenges that come with it, the importance of edge AI for instant decision-making in uses-cases like self-driving cars, the emergence of AI engineering as a distinct field of work, the democratization of AI, what the state of AGI might look like in the near future and much more.  About the AI and the Modern Data Stack DataFramed Series This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratizationAdding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data managementAccelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI Links Mentioned in the Show: Intel OpenVINO™ toolkitIntel Developer Clouds for Accelerated ComputingAWS Re:Invent[Course] Implementing AI Solutions in BusinessRelated Episode: Intel CTO Steve Orrin on How Governments Can Navigate the Data & AI RevolutionSign up to a href="https://www.datacamp.com/radar-analytics-edition"...

Snowflake has been foundational in the data space for years. In the mid-2010s, the platform was a major driver of moving data to the cloud. More recently, it's become apparent that combining data and AI in the cloud is key to accelerating innovation. Snowflake has been rapidly adding AI features to provide value to the modern data stack, but what’s really been going on under the hood? At the time of recording, Sridhar Ramaswamy was the SVP of AI at Snowflake, being appointed CEO at Snowflake in February 2024. Sridhar was formerly Co-Founder of Neeva, acquired in 2023 by Snowflake. Before founding Neeva, Ramaswamy oversaw Google's advertising products, including search, display, video advertising, analytics, shopping, payments, and travel. He joined Google in 2003 and was part of the growth of AdWords and Google's overall advertising business. He spent more than 15 years at Google, where he started as a software engineer and rose to SVP of Ads & Commerce.  In the episode, Richie and Sridhar explore Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, how NLP and AI have impacted enterprise business operations as well as new applications of AI in an enterprise environment, the challenges of enterprise search, the importance of data quality, management and the role of semantic layers in the effective use of AI, a look into Snowflakes products including Snowpilot and Cortex, the collaboration required for successful data and AI projects, advice for organizations looking to improve their data management and much more.     About the AI and the Modern Data Stack DataFramed Series This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratizationAdding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data managementAccelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI Links Mentioned in the Show: SnowflakeSnowflake acquires Neeva to accelerate search in the Data Cloud through generative AIUse AI in Seconds with Snowflake Cortex[Course] Introduction to SnowflakeRelated Episode: Why AI will Change Everything—with Former Snowflake CEO, Bob MugliaSign up to a...

Databricks started out as a platform for using Spark, a big data analytics engine, but it's grown a lot since then. Databricks now allows users to leverage their data and AI projects in the same place, ensuring ease of use and consistency across operations. The Databricks platform is converging on the idea of data intelligence, but what does this mean, how will it help data teams and organizations, and where does AI fit in the picture? Ari is Databricks’ Head of Evangelism and "The Real Moneyball Guy" - the popular movie was partly based on his analytical innovations in Major League Baseball. He is a leading influencer in analytics, artificial intelligence, data science, and high-growth business innovation. Ari was previously the Global AI Evangelist at DataRobot, Nielsen’s regional VP of Analytics, Caltech Alumni of the Decade, President Emeritus of the worldwide Independent Oracle Users Group, on Intel’s AI Board of Advisors, Sports Illustrated Top Ten GM Candidate, an IBM Watson Celebrity Data Scientist, and on the Crain’s Chicago 40 Under 40. He's also written 5 books on analytics, databases, and baseball. Robin is the Field CTO at Databricks. She has consulted with hundreds of organizations on data strategy, data culture, and building diverse data teams. Robin has had an eclectic career path in technical and business functions with more than two decades in tech companies, including Microsoft and Databricks. She also has achieved multiple academic accomplishments from her juris doctorate to a masters in law to engineering leadership. From her first technical role as an entry-level consumer support engineer to her current role in the C-Suite, Robin supports creating an inclusive workplace and is the current co-chair of Women in Data Safety Committee. She was also recognized in 2023 as a Top 20 Women in Data and Tech, as well as DataIQ 100 Most Influential People in Data. In the episode, Richie, Ari, and Robin explore Databricks, the application of generative AI in improving services operations and providing data insights, data intelligence, and lakehouse technology, the wide-ranging applications of generative AI, how AI tools are changing data democratization, the challenges of data governance and management and how tools like Databricks can help, how jobs in data and AI are changing and much more.  About the AI and the Modern Data Stack DataFramed Series This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratizationAdding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data managementAccelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI Links Mentioned in the Show: DatabricksDelta Lakea href="https://mlflow.org/" rel="noopener...

One of the biggest surprises of the generative AI revolution over the past 2 years lies in the counter-intuitiveness of its most successful use cases. Counter to most predictions made about AI years ago, AI-assisted coding, specifically AI-assisted data work, has been surprisingly one of the biggest killer apps of generative AI tools and copilots. However, what happens when we take this notion even further? How will analytics workflows look like when generative AI tools can also assist us in problem-solving? What type of analytics use cases can we expect to operationalize, and what tools can we expect to work with when AI systems can provide scalable qualitative data instead of relying on imperfect quantitative proxies? Today’s guest calls this future “weird”.  Benn Stancil is the Field CTO at ThoughtSpot. He joined ThoughtSpot in 2023 as part of its acquisition of Mode, where he was a Co-Founder and CTO. While at Mode, Benn held roles leading Mode’s data, product, marketing, and executive teams. He regularly writes about data and technology at benn.substack.com. Prior to founding Mode, Benn worked on analytics teams at Microsoft and Yammer. Throughout the episode, Benn and Adel talk about the nature of AI-assisted analytics workflows, the potential for generative AI in assisting problem-solving, how he imagines analytics workflows to look in the future, and a lot more.  About the AI and the Modern Data Stack DataFramed Series This week we’re releasing 4 episodes focused on how AI is changing the modern data stack and the analytics profession at large. The modern data stack is often an ambiguous and all-encompassing term, so we intentionally wanted to cover the impact of AI on the modern data stack from different angles. Here’s what you can expect: Why the Future of AI in Data will be Weird with Benn Stancil, CTO at Mode & Field CTO at ThoughtSpot — Covering how AI will change analytics workflows and tools How Databricks is Transforming Data Warehousing and AI with Ari Kaplan, Head Evangelist & Robin Sutara, Field CTO at Databricks — Covering Databricks, data intelligence and how AI tools are changing data democratizationAdding AI to the Data Warehouse with Sridhar Ramaswamy, CEO at Snowflake — Covering Snowflake and its uses, how generative AI is changing the attitudes of leaders towards data, and how to improve your data managementAccelerating AI Workflows with Nuri Cankaya, VP of AI Marketing & La Tiffaney Santucci, AI Marketing Director at Intel — Covering AI’s impact on marketing analytics, how AI is being integrated into existing products, and the democratization of AI Links Mentioned in the Show: Mode AnalyticsThoughtSpot acquires Mode: Empowering data teams to bring Generative AI to BIEverybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are[Course] Generative AI for Business[Skill Track] SQL FundamentalsRelated Episode: The Future of Marketing Analytics with Cory Munchbach, CEO at...