Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Sinch⁠ — Connect with customers at every step of their journey. •⁠ Modal⁠ — The cloud platform for building AI applications. — How has Microsoft changed since its founding in 1975, especially in how it builds tools for developers? In this episode of The Pragmatic Engineer, I sit down with Scott Guthrie, Executive Vice President of Cloud and AI at Microsoft. Scott has been with the company for 28 years. He built the first prototype of ASP.NET, led the Windows Phone team, led up Azure, and helped shape many of Microsoft’s most important developer platforms. We talk about Microsoft’s journey from building early dev tools to becoming a top cloud provider—and how it actively worked to win back and grow its developer base. In this episode, we cover: • Microsoft’s early years building developer tools  • Why Visual Basic faced resistance from devs back in the day: even though it simplified development at the time • How .NET helped bring a new generation of server-side developers into Microsoft’s ecosystem • Why Windows Phone didn’t succeed  • The 90s Microsoft dev stack: docs, debuggers, and more • How Microsoft Azure went from being the #7 cloud provider to the #2 spot today • Why Microsoft created VS Code • How VS Code and open source led to the acquisition of GitHub • What Scott’s excited about in the future of developer tools and AI • And much more! — Timestamps (00:00) Intro (02:25) Microsoft’s early years building developer tools (06:15) How Microsoft’s developer tools helped Windows succeed (08:00) Microsoft’s first tools were built to allow less technically savvy people to build things (11:00) A case for embracing the technology that’s coming (14:11) Why Microsoft built Visual Studio and .NET (19:54) Steve Ballmer’s speech about .NET (22:04) The origins of C# and Anders Hejlsberg’s impact on Microsoft  (25:29) The 90’s Microsoft stack, including documentation, debuggers, and more (30:17) How productivity has changed over the past 10 years  (32:50) Why Gergely was a fan of Windows Phone—and Scott’s thoughts on why it didn’t last (36:43) Lessons from working on (and fixing)  Azure under Satya Nadella  (42:50) Codeplex and the acquisition of GitHub (48:52) 2014: Three bold projects to win the hearts of developers (55:40) What Scott’s excited about in new developer tools and cloud computing  (59:50) Why Scott thinks AI will enhance productivity but create more engineering jobs — The Pragmatic Engineer deepdives relevant for this episode: • Microsoft is dogfooding AI dev tools’ future • Microsoft’s developer tools roots • Why are Cloud Development Environments spiking in popularity, now? • Engineering career paths at Big Tech and scaleups • How Linux is built with Greg Kroah-Hartman — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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Organizations struggle to make sense of numerous programs and projects that overlap or operate in silos. This research will weave together data and analytics governance, MDM and data quality into one organized initiative that every CDAO should be interested in.

Try Julius.ai 👉 https://bit.ly/4jn4cFF Coupon code: AVERY25 AI is transforming how we work, how we make decisions, and how we understand the world through data. In this episode, I explore how Julius AI can simplify your data tasks, automate repetitive work, and offer valuable insights in MINUTES. Dive into the future of data analysis and get ready to 10x your productivity! Get my weekly newsletters (free): https://www.datacareerjumpstart.com/newsletter

Learn more about my CRM Course Creator 360: https://coursecreatorpro.com/registeremailaffiliate?am_id=avery8756

How This Delivery Driver Became a FAANG Data Analyst (Jen Hawkins) https://youtu.be/f-BWp_IJZ-I?si=2_tKqHEng_EYNRCB

💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator

⌚ TIMESTAMPS 00:00 - Introduction 01:55 - My CRM Data Analysis 02:23 Exploring and Cleaning Data with Julius AI 06:15 Email Analysis and Insights 13:39 Sales Cycle Length Analysis 15:27 The Power of AI in Data Analysis

🔗 CONNECT WITH MY TOP NEWSLETTER ENGAGERS! Isaac Oresanya: https://www.linkedin.com/in/isaac-oresanya/ Jen Hawkins: https://www.linkedin.com/in/jeandriska/ David Mills: https://www.linkedin.com/in/david-mills/ Mukta Pandey: https://www.linkedin.com/in/mukta-pandey-30a89b243/

🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

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Analytics is experiencing another monumental change. Just as visual drag and drop BI tools and augmented insights led to changes in analytics delivery, we now experience conversational interfaces, automated workflows and AI agents that cause us to rethink how analytics will be done. Join this session to learn the new technologies that are making an impact and how this will affect plans for future investment in analytics tools, platforms and solutions.

Urgent Investments in data, analytics and AI use cases has put the spotlight once more on strong data management foundations. Is our Data even Ready for upcoming AI, analytics and data sharing initiatives is now top of mindshare for heads of data, CDAOs and their counterparts. Data Fabrics have emerged as a long term, foundational data management architecture that you should now pursue for sustained D&A success. This session will:
1. Help understand what data Fabrics are and what they mean for your data strategy and architecture
2. Help decide how to build and where to buy
3. Navigate the vendor landscape to assist in tech procurement decisions to aid your fabric journey

Data catalogs suffer from a high failure rate that is often blamed on poor technology choices but are more often the result of nontechnical issues. This session will provide insight to data and analytics leaders in evaluating metadata management requirements, assessing readiness of organizations and best practices for getting started to implement a data catalog.

You've likely encountered human resistance to your data and analytics governance initiative, which can be frustrating because what you are doing seems like common sense. The very people you think your governance initiative will help can derail or block it. This session explores the people challenges encountered, and lessons learned on the journey to governance failures and successes.

This presentation provides a look at the multiple Hype Cycles that are pertinent to AI initiatives. This would include: Hype Cycle for AI, generative AI, data science and machine learning platforms, analytics and business intelligence, and possibly others.

This session will share proven enterprise architecture best practices for augmenting Snowflake with data virtualization to deliver real-time insights. We'll explore how to address latency-sensitive use cases—such as month-end financial reconciliations—while ensuring data security and supporting cloud migration using Denodo. Attendees will learn how the combination of Snowflake and Denodo enables scalable, low-latency analytics across highly customized and distributed data environments.

In today’s data-driven world, organizations are challenged to extract meaningful insights from complex, distributed information. A modern data intelligence platform brings together data management, AI/ML, and analytics to turn raw data into strategic advantage. This session explores how unified data architectures, augmented analytics, and intelligent applications are enabling smarter decisions and better business outcomes across industries. Real-world use cases—from demand forecasting to regulatory compliance—highlight the transformative impact of data intelligence. Powered by Oracle, this approach helps enterprises stay agile, informed, and competitive.

As AI evolves into more agentic forms, capable of autonomous decision-making and complex interactions, the readiness of your data becomes a mission-critical priority. This roundtable gathers data & analytics leaders to explore the unique challenges of preparing data ecosystems for agentic AI. Discussions will focus on overcoming barriers such as data quality gaps, governance complexities, and scalability issues, while highlighting the transformative role of technologies like generative AI, data fabrics, and metadata-driven governance

Traditional approaches and thinking around data quality are out of date and not sufficient in the era of AI. Data, analytics and AI leaders will need to reconsider their approach to data quality going beyond the traditional six data quality dimensions. This session will help data leaders learn to think about data quality in a holistic way that support making data AI-ready.

Data and analytics leaders expect their data and analytics investments to deliver business results. But unless they address data, analytics and AI risk, their initiatives will fail, leading to higher business risk exposure. This session will help D&A leaders target three key areas where better data and analytics risk practices will yield better business results.

Asking your colleagues how analytics can help them often results in blank stares, defensiveness, or wildly incoherent suggestions involving AI. This session will show you how to work with your colleagues to pinpoint how you can help, identify the most helpful capabilities to build, and explain how to measure your impact.

AI is having a huge impact, but is not the only thing with societal, technological, and organizational implications driving change in data and analytics. We examine trends in areas such as complexity, trust, and empowerment facing leaders and teams as they make decisions in all aspects of their bet-the-business D&A strategy.

Summary In this episode of the Data Engineering Podcast Mai-Lan Tomsen Bukovec, Vice President of Technology at AWS, talks about the evolution of Amazon S3 and its profound impact on data architecture. From her work on compute systems to leading the development and operations of S3, Mylan shares insights on how S3 has become a foundational element in modern data systems, enabling scalable and cost-effective data lakes since its launch alongside Hadoop in 2006. She discusses the architectural patterns enabled by S3, the importance of metadata in data management, and how S3's evolution has been driven by customer needs, leading to innovations like strong consistency and S3 tables.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.This is a pharmaceutical Ad for Soda Data Quality. Do you suffer from chronic dashboard distrust? Are broken pipelines and silent schema changes wreaking havoc on your analytics? You may be experiencing symptoms of Undiagnosed Data Quality Syndrome — also known as UDQS. Ask your data team about Soda. With Soda Metrics Observability, you can track the health of your KPIs and metrics across the business — automatically detecting anomalies before your CEO does. It’s 70% more accurate than industry benchmarks, and the fastest in the category, analyzing 1.1 billion rows in just 64 seconds. And with Collaborative Data Contracts, engineers and business can finally agree on what “done” looks like — so you can stop fighting over column names, and start trusting your data again.Whether you’re a data engineer, analytics lead, or just someone who cries when a dashboard flatlines, Soda may be right for you. Side effects of implementing Soda may include: Increased trust in your metrics, reduced late-night Slack emergencies, spontaneous high-fives across departments, fewer meetings and less back-and-forth with business stakeholders, and in rare cases, a newfound love of data. Sign up today to get a chance to win a $1000+ custom mechanical keyboard. Visit dataengineeringpodcast.com/soda to sign up and follow Soda’s launch week. It starts June 9th.Your host is Tobias Macey and today I'm interviewing Mai-Lan Tomsen Bukovec about the evolutions of S3 and how it has transformed data architectureInterview IntroductionHow did you get involved in the area of data management?Most everyone listening knows what S3 is, but can you start by giving a quick summary of what roles it plays in the data ecosystem?What are the major generational epochs in S3, with a particular focus on analytical/ML data systems?The first major driver of analytical usage for S3 was the Hadoop ecosystem. What are the other elements of the data ecosystem that helped shape the product direction of S3?Data storage and retrieval have been core primitives in computing since its inception. What are the characteristics of S3 and all of its copycats that led to such a difference in architectural patterns vs. other shared data technologies? (e.g. NFS, Gluster, Ceph, Samba, etc.)How does the unified pool of storage that is exemplified by S3 help to blur the boundaries between application data, analytical data, and ML/AI data?What are some of the default patterns for storage and retrieval across those three buckets that can lead to anti-patterns which add friction when trying to unify those use cases?The age of AI is leading to a massive potential for unlocking unstructured data, for which S3 has been a massive dumping ground over the years. How is that changing the ways that your customers think about the value of the assets that they have been hoarding for so long?What new architectural patterns is that generating?What are the most interesting, innovative, or unexpected ways that you have seen S3 used for analytical/ML/Ai applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on S3?When is S3 the wrong choice?What do you have planned for the future of S3?Contact Info LinkedInParting 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 AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links AWS S3KinesisKafkaSQSEMRDrupalWordpressNetflix Blog on S3 as a Source of TruthHadoopMapReduceNasa JPLFINRA == Financial Industry Regulatory AuthorityS3 Object VersioningS3 Cross RegionS3 TablesIcebergParquetAWS KMSIceberg RESTDuckDBNFS == Network File SystemSambaGlusterFSCephMinIOS3 MetadataPhotoshop Generative FillAdobe FireflyTurbotax AI AssistantAWS Access AnalyzerData ProductsS3 Access PointAWS Nova ModelsLexisNexis ProtegeS3 Intelligent TieringS3 Principal Engineering TenetsThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

What can you build with Sigma Data Apps in just two hours? In this friendly competition, three contestants tackle a real-world challenge: creating a rent-versus-buy calculator using Denver metro real estate data.

Each contestant was given the same dataset and requirements:

Feature data warehouse writeback to build a workflow Use a scoring model to make a clear buy vs. rent recommendation Demonstrate Sigma’s capabilities and ease of app development See the final app demos, hear direct feedback from the judges, and witness the crowning of the first-ever Sigma Data Apps Golden GOAT Champion. Whether you're a seasoned data pro or just getting started, this competition proves that with Sigma, anyone can build powerful applications—fast.

Participants Ashley Bennett Senior Associate, Spaulding Ridge Eric Heidbreder Owner & Principal Consultant, Chicago Analytics Services Katrina Menne Alliance and Tech Lead, Aimpoint Digital

Judges David Porter Head of Partner Engineering, Sigma Donny Alfano SI Partner Solutions Director, Sigma Alfonso Franklin VP of Solution Engineering, Sigma

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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AdventHealth’s investment team needed a better way to merge the need for real-time reporting for portfolio metrics and managing portfolio adjustments, moving beyond outdated, static reports that slowed decision-making and introduced inefficiencies.

In this session, Kelly Booth (Director of Data Strategy & Analytics, AdventHealth) and Jared Flores (Founder & Managing Director, Data Flow) will share how they partnered to modernize investment portfolio metrics reporting with a data app that enables real-time value adjustments, audit logging, and vendor tracking. This tool has helped:

Reduce manual errors by enabling real-time portfolio adjustments Save hours of work by eliminating back-and-forth data corrections Increase confidence in reporting with a fully auditable, write-back-enabled workflow Join us to see a live demo of the app in action and hear how AdventHealth uses Sigma Data Apps to drive efficiency and accuracy in investment data management.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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sigma #sigmacomputing #dataanalytics #dataanalysis #businessintelligence #cloudcomputing #clouddata #datacloud #datastructures #datadriven #datadrivendecisionmaking #datadriveninsights #businessdecisions #datadrivendecisions #embeddedanalytics #cloudcomputing #SigmaAI #AI #AIdataanalytics #AIdataanalysis #GPT #dataprivacy #python #dataintelligence #moderndataarchitecture

Managing customer interactions across multiple disconnected platforms creates inefficiencies and delays in resolving support tickets. At WHOOP, support agents had to manually navigate through siloed data across payments, ERP, and ticketing systems, slowing down response times and impacting customer satisfaction.In this session, Matt Luizzi (Director of Business Analytics, WHOOP) and Brendan Farley (Sales Engineer, Snowflake) will showcase how WHOOP:

Consolidated fragmented data from multiple systems into a unified customer support app. Enabled real-time access to customer history, allowing agents to quickly surface relevant insights. Eliminated the need for custom engineering by leveraging Sigma’s no-code interface to build interactive workflows. Accelerated ticket resolution by allowing support teams to take action directly within Sigma, reducing dependency on multiple SaaS tools. Improved forecasting and decision-making by implementing AI-powered analytics on top of Snowflake. Before Sigma, getting a full view of customer issues required navigating across multiple tools—now, WHOOP’s customer support team can access, analyze, and act on real-time data in a single interface. Join us for an inside look at how WHOOP and Snowflake partnered to build a modern customer support data app that enhances efficiency and customer experience.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

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sigma #sigmacomputing #dataanalytics #dataanalysis #businessintelligence #cloudcomputing #clouddata #datacloud #datastructures #datadriven #datadrivendecisionmaking #datadriveninsights #businessdecisions #datadrivendecisions #embeddedanalytics #cloudcomputing #SigmaAI #AI #AIdataanalytics #AIdataanalysis #GPT #dataprivacy #python #dataintelligence #moderndataarchitecture

Conagra’s brand teams needed a dynamic, centralized way to track and analyze macro and micro events affecting business performance. Previously, teams relied on manual Excel processes to compare shipments, consumption trends, and promotions against external factors like weather and consumer pricing data. This approach led to inefficiencies, inconsistencies, and limited adoption.

In this session, Matthew Henkel (Sr Analyst Advanced Analytics) and Manav Purohit (Director Analytics Engineering) will showcase the Event Impact Tracker, a Sigma-powered data app that:

Unifies external & internal data for better event-driven decision-making Leverages input tables to allow users to log and track internal events Enables easy adoption with an Excel-like, user-friendly experience Join this session to see a demo of the app and hear how Conagra transformed event tracking into a scalable, data-driven process with Sigma Data Apps.

➡️ Learn more about Data Apps: https://www.sigmacomputing.com/product/data-applications?utm_source=youtube&utm_medium=organic&utm_campaign=data_apps_conference&utm_content=pp_data_apps

➡️ Sign up for your free trial: https://www.sigmacomputing.com/go/free-trial?utm_source=youtube&utm_medium=video&utm_campaign=free_trial&utm_content=free_trial

sigma #sigmacomputing #dataanalytics #dataanalysis #businessintelligence #cloudcomputing #clouddata #datacloud #datastructures #datadriven #datadrivendecisionmaking #datadriveninsights #businessdecisions #datadrivendecisions #embeddedanalytics #cloudcomputing #SigmaAI #AI #AIdataanalytics #AIdataanalysis #GPT #dataprivacy #python #dataintelligence #moderndataarchitecture