Google Public Sector is committed to providing mutually beneficial programs that help small businesses compete and win government contracts.The Google Public Sector Small Business Program unlocks incentives that meet funding, marketing and enablement needs of small business partners. In this session, attendees will hear from small business owners who have achieved incredible success thanks to the resources of this program. The CTA will be for attendees to apply to the program.

Supported by Our Partners •⁠ CodeRabbit⁠⁠ — Cut code review time and bugs in half. Use the code PRAGMATIC to get one month free. •⁠ Modal⁠ — The cloud platform for building AI applications. — How will AI tools change software engineering? Tools like Cursor, Windsurf and Copilot are getting better at autocomplete, generating tests and documentation. But what is changing, when it comes to software design? Stanford professor John Ousterhout thinks not much. In fact, he believes that great software design is becoming even more important as AI tools become more capable in generating code.  In this episode of The Pragmatic Engineer, John joins me to talk about why design still matters and how most teams struggle to get it right. We dive into his book A Philosophy of Software Design, unpack the difference between top-down and bottom-up approaches, and explore why some popular advice, like writing short methods or relying heavily on TDD, does not hold up, according to John. We also explore:  • The differences between working in industry vs. academia  • Why John believes software design will become more important as AI capabilities expand • The top-down and bottoms-up design approaches – and why you should use both • John’s “design it twice” principle • Why deep modules are essential for good software design  • Best practices for special cases and exceptions • The undervalued trait of empathy in design thinking • Why John advocates for doing some design upfront • John’s criticisms of the single-responsibility principle, TDD, and why he’s a fan of well-written comments  • And much more! As a fun fact: when we recorded this podcast, John was busy contributing to the Linux kernel: adding support to the Homa Transport Protocol – a protocol invented by one of his PhD students. John wanted to make this protocol available more widely, and is putting in the work to do so. What a legend! (We previously covered how Linux is built and how to contribute to the Linux kernel) — Timestamps (00:00) Intro  (02:00) Why John transitioned back to academia (03:47) Working in academia vs. industry  (07:20) Tactical tornadoes vs. 10x engineers (11:59) Long-term impact of AI-assisted coding (14:24) An overview of software design (15:28) Why TDD and Design Patterns are less popular now  (17:04) Two general approaches to designing software  (18:56) Two ways to deal with complexity  (19:56) A case for not going with your first idea  (23:24) How Uber used design docs (26:44) Deep modules vs. shallow modules (28:25) Best practices for error handling (33:31) The role of empathy in the design process (36:15) How John uses design reviews  (38:10) The value of in-person planning and using old-school whiteboards  (39:50) Leading a planning argument session and the places it works best (42:20) The value of doing some design upfront  (46:12) Why John wrote A Philosophy of Software of Design  (48:40) An overview of John’s class at Stanford (52:20) A tough learning from early in Gergely’s career  (55:48) Why John disagrees with Robert Martin on short methods (1:10:40) John’s current coding project in the Linux Kernel  (1:14:13) Updates to A Philosophy of Software Design in the second edition (1:19:12) Rapid fire round (1:01:08) John’s criticisms of TDD and what he favors instead  (1:05:30) Why John supports the use of comments and how to use them correctly (1:09:20) How John uses ChatGPT to help explain code in the Linux Kernel — The Pragmatic Engineer deepdives relevant for this episode: • Engineering Planning with RFCs, Design Documents and ADRs • Paying down tech debt • Software architect archetypes • Building Bluesky: a distributed social network — 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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Artificial intelligence is no longer on the horizon – it’s the defining force shaping business today. During this fireside chat, Thomas Kurian, CEO of Google Cloud, will sit down with Google Cloud's VP of Marketing, Alison Wagonfeld for a candid conversation on navigating the AI revolution, unlocking new opportunities for innovation, and building a future-ready organization. They’ll explore Google Cloud’s strategic vision and delve into both the profound impact of AI across industries and actionable strategies for businesses to leverage this technology.

Topics will include: 

• AI as the competitive differentiator
• The role of Google Cloud in the AI era
• Navigating leadership in the age of AI

The explosion of content in market research has created a paradox - more information but less time to consume it. Companies are now turning to AI chatbots to solve this problem, transforming how professionals interact with research data. Instead of expecting teams to read everything, these tools allow users to extract precisely what they need when they need it. This approach is proving not just more efficient but actually increases engagement with underlying content. How might your organization benefit from more targeted access to insights? What valuable information might be buried in your existing research that AI could help surface? With over 30 years of experience in marketing, media, and technology, Dan Coates is the President and co-founder of YPulse, the leading authority on Gen Z and Millennials. YPulse helps brands like Apple, Netflix, and Xbox understand and communicate with consumers aged 13–39, using data and insights from over 400,000 interviews conducted annually across seven countries. Prior to founding YPulse, Dan co-founded SurveyU, an online community and insights platform targeting youth, which merged with YPulse in 2009. He also led the introduction of Globalpark’s SAAS platform into the North American market, until its acquisition by QuestBack in 2011. In addition, Dan has held senior roles at Polimetrix, SPSS, PlanetFeedback, and Burke, where he developed cutting-edge practices and products for online marketing insights and transitioned several ventures from early stages to high-value acquisitions. In the episode, Richie and Dan explore the creation of an AI chatbot for market research, addressing customer engagement challenges, the integration of AI in content consumption, the impact of AI on business strategies, and the future of AI in market research, and much more. Links Mentioned in the Show: YPulseConnect with DanHaystack by DeepsetUnmanaged: Master the Magic of Creating Empowered and Happy Organizations by Jack SkeelsSkill Track: AI FundamentalsRelated Episode: Can You Use AI-Driven Pricing Ethically? with Jose Mendoza, Academic Director & Clinical Associate Professor at NYURewatch sessions from RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Supported by Our Partners • Swarmia — The engineering intelligence platform for modern software organizations. • Sentry — Error and performance monitoring for developers. — Why did Meta build its own internal developer tooling instead of using industry-standard solutions like GitHub? Tomas Reimers, former Meta engineer and co-founder of Graphite, joins the show to talk about Meta's custom developer tools – many of which were years ahead of the industry. From Phababricator to Sandcastle and Butterflybot, Tomas shares examples of Meta’s internal tools that transformed developer productivity at the tech giant. Why did working with stacked diffs and using monorepos become best practices at Meta? How are these practices influencing the broader industry? Why are code reviews and testing looking to become even more critical as AI transforms how we write software? We answer these, and also discuss: • Meta's custom internal developer tools • Why more tech companies are transitioning from polyrepos to monorepos • A case for different engineering constraints within the same organization • How stacked diffs solve the code review bottleneck • Graphite’s origin story and pivot to their current product  • Why code reviews will become a lot more important, the more we use AI coding tools • Tomas’s favorite engineering metric  • And much more! — Timestamps (00:00) Intro (02:00) An introduction to Meta’s in-house tooling  (05:07) How Meta’s integrated tools work and who built the tools (10:20) An overview of the rules engine, Herald  (12:20) The stages of code ownership at Facebook and code ownership at Google and GitHub (14:39) Tomas’s approach to code ownership  (16:15) A case for different constraints within different parts of an organization  (18:42) The problem that stacked diffs solve for  (25:01) How larger companies drive innovation, and who stacking diffs not for  (30:25) Monorepos vs. polyrepos and why Facebook is transitioning to a monorepo (35:31) The advantages of monorepos and why GitHub does not support them  (39:55) AI’s impact on software development  (42:15) The problems that AI creates, and possible solutions (45:25) How testing might change and the testing AI coding tools are already capable of  (48:15) How developer accountability might be a way to solve bugs and bad AI code (53:20) Why stacking hasn’t caught on and Graphite’s work  (57:10) Graphite’s origin story  (1:01:20) Engineering metrics that matter  (1:06:07) Learnings from building a company for developers  (1:08:41) Rapid fire round (1:12:41) Closing — The Pragmatic Engineer deepdives relevant for this episode: • Stacked Diffs (and why you should know about them) • Inside Meta’s engineering culture • Shipping to production • How Uber is measuring engineering productivity — 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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Send us a text ✨ Updated April 1, 2025 What do Tickle Me Elmo, bourbon, and Taylor Swift tickets have in common? Scarcity. And in the world of marketing, it's one of the most powerful forces you can harness. This week, we’re throwing it back to one of our most insightful interviews — a conversation with Dr. Mindy Weinstein, Founder and CEO of Market MindShift, marketing professor at Grand Canyon University, Columbia Business School, and Wharton, and author of The Power of Scarcity. We dig into: The psychology behind scarcity and why it drives us to act nowThe four types of scarcity (you’ll want to write these down!)How top brands — and yes, bourbon sellers — use scarcity to spark actionWhy "reaching humans" in digital marketing is more nuanced than everHow you can ethically and effectively use scarcity to boost business results📚 About the Book:  In The Power of Scarcity, Dr. Weinstein combines her background in marketing and psychology to break down how scarcity messaging influences decision-making — and how you can leverage it to drive revenue, deepen loyalty, and create urgency without manipulation. With research, real-world examples, and interviews from brands like McDonald’s and 1-800-Flowers, it’s a must-read for anyone looking to up their marketing game. 📌 Timestamps: 01:41 Meet "Marketer" Mindy Weinstein 04:42 Technology in Marketing 07:50 One of the top women in digital marketing 09:12 The Power of Scarcity 19:16 The Four Types of Scarcity 20:41 Bourbon Scarcity 21:47 Businesses Leveraging Scarcity 🧠 Connect with Dr. Weinstein:  🔗 LinkedIn: linkedin.com/in/mindydweinstein 📘 Book: persuasioninbusiness.com 🌐 Website: marketmindshift.com 🎧 Originally aired: Season 7, Episode 5 Want to be featured on Making Data Simple? Drop us a note at [email protected] and tell us why you should be next! Hosted by Al Martin, WW VP Technical Sales at IBM — where we explore cutting-edge tech, business innovation, and the human side of leadership... all 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.

On this podcast episode of Data Unchained, Sanjay Annadate, Vice President and Business Head EMEA at Latent View Analytics, joins us to talk about optimizing marketing spend to making supply chains more sustainable. Discover the top four challenges clients bring to LatentView as Sanjay shares how his team uses pre-built analytics solutions to solve problems fast and unlock performance across industries.

DataUnchained #AIinBusiness #DigitalTransformation #DataAnalytics #AICompliance #EnterpriseAI #DataStrategy #CustomerInsights #DataDriven #MarketingAnalytics #SupplyChainOptimization #LatentView #BusinessIntelligence #TechPodcast #InnovationLeadership #FutureOfData #AIGovernance #SmartData #AITrends #EuropeanTech

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

Supported by Our Partners • Graphite — The AI developer productivity platform.  • Sonar —  Code quality and code security for ALL code.  • Chronosphere — The observability platform built for control. — How do you take a new product idea, and turn it into a successful product? Figma Slides started as a hackathon project a year and a half ago – and today it’s a full-on product, with more than 4.5M slide decks created by users. I’m joined by two founding engineers on this project: Jonathan Kaufman and Noah Finer. In our chat, Jonathan and Noah pull back the curtain on what it took to build Figma Slides. They share engineering challenges faced, interesting engineering practices utilized, and what it's like working on a product used by millions of designers worldwide. We talk about: • An overview of Figma Slides • The tech stack behind Figma Slides • Why the engineering team built grid view before single slide view • How Figma ensures that all Figma files look the same across browsers • Figma’s "vibe testing" approach • How beta testing helped experiment more • The “all flags on”, “all flags off” testing approach • Engineering crits at Figma • And much more! — Timestamps (00:00) Intro (01:45) An overview of Figma Slides and the first steps in building it (06:41) Why Figma built grid view before single slide view (10:00) The next steps of building UI after grid view  (12:10) The team structure and size of the Figma Slides team  (14:14) The tech stack behind Figma Slides (15:31) How Figma uses C++ with bindings  (17:43) The Chrome debugging extension used for C++ and WebAssembly  (21:02) An example of how Noah used the debugging tool (22:18) Challenges in building Figma Slides  (23:15) An explanation of multiplayer cursors  (26:15) Figma’s philosophy of building interconnected products—and the code behind them (28:22) An example of a different mouse behavior in Figma  (33:00) Technical challenges in developing single slide view  (35:10) Challenges faced in single-slide view while maintaining multiplayer compatibility (40:00) The types of testing used on Figma Slides (43:42) Figma’s zero bug policy  (45:30) The release process, and how engineering uses feature flags  (48:40) How Figma tests Slides with feature flags enabled and then disabled (51:35) An explanation of eng crits at Figma  (54:53) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Inside Figma’s engineering culture • Quality Assurance across the tech industry • Shipping to production • Design-first software engineering — 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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Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS. • Vanta — Automate compliance and simplify security with Vanta. — Linux is the most widespread operating system, globally – but how is it built? Few people are better to answer this than Greg Kroah-Hartman: a Linux kernel maintainer for 25 years, and one of the 3 Linux Kernel Foundation Fellows (the other two are Linus Torvalds and Shuah Khan). Greg manages the Linux kernel’s stable releases, and is a maintainer of multiple kernel subsystems. We cover the inner workings of Linux kernel development, exploring everything from how changes get implemented to why its community-driven approach produces such reliable software. Greg shares insights about the kernel's unique trust model and makes a case for why engineers should contribute to open-source projects. We go into: • How widespread is Linux? • What is the Linux kernel responsible for – and why is it a monolith? • How does a kernel change get merged? A walkthrough • The 9-week development cycle for the Linux kernel • Testing the Linux kernel • Why is Linux so widespread? • The career benefits of open-source contribution • And much more! — Timestamps (00:00) Intro (02:23) How widespread is Linux? (06:00) The difference in complexity in different devices powered by Linux  (09:20) What is the Linux kernel? (14:00) Why trust is so important with the Linux kernel development (16:02) A walk-through of a kernel change (23:20) How Linux kernel development cycles work (29:55) The testing process at Kernel and Kernel CI  (31:55) A case for the open source development process (35:44) Linux kernel branches: Stable vs. development (38:32) Challenges of maintaining older Linux code  (40:30) How Linux handles bug fixes (44:40) The range of work Linux kernel engineers do  (48:33) Greg’s review process and its parallels with Uber’s RFC process (51:48) Linux kernel within companies like IBM (53:52) Why Linux is so widespread  (56:50) How Linux Kernel Institute runs without product managers  (1:02:01) The pros and cons of using Rust in Linux kernel  (1:09:55) How LLMs are utilized in bug fixes and coding in Linux  (1:12:13) The value of contributing to the Linux kernel or any open-source project  (1:16:40) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: What TPMs do and what software engineers can learn from them The past and future of modern backend practices Backstage: an open-source developer portal — 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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Send us a text 🚀 What’s the secret to building a world-class influencer marketing program? Ryan Debenham, CEO of GRIN, joins Making Data Simple to break down the future of creator-driven marketing, AI’s role in the space, and how GRIN is reshaping brand-influencer partnerships. 💡 From Red Bull’s marketing playbook to a key conversation about the “Secret Sauce”, Ryan shares the insights, technology, and mindset needed to scale a creator-powered brand in today’s digital economy. 🔹 Key Topics: ✅ What makes a great influencer? ✅ How GRIN is redefining the industry ✅ AI’s impact on marketing ✅ The future of remote companies ⌛️Minute Markers 02:11 Meet Ryan Debenham 07:54 Running a Startup 08:46 Modeled after Red Bull 12:01 GRIN 15:44 Influencer Marketing 17:52 Who are the Influencers 18:45 Secret Sauce 22:51 The Technology 24:51 The 2-Min GRIN Pitch 31:16 AI in Marketing 34:32 GRIN's Future 36:06 A Remote Company 38:35 In Closing 📢 Listen now! 🎧 Guest: Ryan Debenham | LinkedIn | Website 🌍 Host: Al Martin, WW VP Technical Sales, IBM | LinkedIn 📩 Want to be a guest? Email us at [email protected] 🔗 Hashtags: #MakingDataSimple #InfluencerMarketing #CreatorEconomy #AIinMarketing #BrandGrowth #MarketingTech #Leadership #GRIN #RyanDebenham 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.

Supported by Our Partners • Sentry — Error and performance monitoring for developers. • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well. — In today’s episode of The Pragmatic Engineer, I am joined by former Uber colleague, Gautam Korlam. Gautam is the Co-Founder of Gitar, an agentic AI startup that automates code maintenance. Gautam was mobile engineer no. 9 at Uber and founding engineer for the mobile platform team – and so he learned a few things about scaling up engineering teams. We talk about: • How Gautam accidentally deleted Uber’s Java monorepo – really! • Uber's unique engineering stack and why custom solutions like SubmitQueue were built in-house • Monorepo: the benefits and downsides of this approach • From Engineer II to Principal Engineer at Uber: Gautam’s career trajectory • Practical strategies for building trust and gaining social capital  • How the platform team at Uber operated with a product-focused mindset • Vibe coding: why it helps with quick prototyping • How AI tools are changing developer experience and productivity • Important skills for devs to pick up to remain valuable as AI tools spread • And more! — Timestamps (00:00) Intro (02:11) How Gautam accidentally deleted Uber’s Java Monorepo (05:40) The impact of Gautam’s mistake (06:35) Uber’s unique engineering stack (10:15) Uber’s SubmitQueue (12:44) Why Uber moved to a monorepo (16:30) The downsides of a monorepo (18:35) Measurement products built in-house  (20:20) Measuring developer productivity and happiness  (22:52) How Devpods improved developer productivity  (27:37) The challenges with cloud development environments (29:10) Gautam’s journey from Eng II to Principal Engineer (32:00) Building trust and gaining social capital  (36:17) An explanation of Principal Engineer at Uber—and the archetypes at Uber  (45:07) The platform and program split at Uber (48:15) How Gautam and his team supported their internal users  (52:50) Gautam’s thoughts on developer productivity  (59:10) How AI enhances productivity, its limitations, and the rise of agentic AI (1:04:00) An explanation of Vibe coding (1:07:34) An overview of Gitar and all it can help developers with  (1:10:44) Top skills to cultivate to add value and stay relevant (1:17:00) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The Platform and Program split at Uber • How Uber is measuring engineering productivity • Inside Uber’s move to the Cloud • How Uber built its observability platform • Software Architect Archetypes — 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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Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS. • The Software Engineer’s Guidebook: Written by me (Gergely) – now out in audio form as well • Augment Code — AI coding assistant that pro engineering teams love — Not many people know that I have a brother: Balint Orosz. Balint is also in tech, but in many ways, is the opposite of me. While I prefer working on backend and business logic, he always thrived in designing and building UIs. While I opted to work at more established companies, he struck out on his own and started his startup, Distinction. And yet, our professional paths have crossed several times: at one point in time I accepted an offer to join Skyscanner as a Principal iOS Engineer – and as part of the negotiation, I added a clause to my contrac that I will not report directly or indirectly to the Head of Mobile: who happened to be my brother, thanks to Skyscanner acquiring his startup the same month that Skyscanner made an offer to hire me. Today, Balint is the founder and CEO of Craft, a beloved text editor known for its user-friendly interface and sleek design – an app that Apple awarded the prestigious Mac App of the Year in 2021. In our conversation, we explore how Balint approaches building opinionated software with an intense focus on user experience. We discuss the lessons he learned from his time building Distinction and working at Skyscanner that have shaped his approach to Craft and its development. In this episode, we discuss: • Balint’s first startup, Distinction, and his time working for Skyscanner after they acquired it • A case for a balanced engineering culture with both backend and frontend priorities  • Why Balint doesn’t use iOS Auto Layout • The impact of Craft being personal software on front-end and back-end development • The balance between customization and engineering fear in frontend work • The resurgence of local-first software and its role in modern computing • The value of building a physical prototype  • How Balint uses GenAI to assist with complicated coding projects  • And much more! — Timestamps (00:00) Intro (02:13) What it’s like being a UX-focused founder  (09:00) Why it was hard to gain recognition at Skyscanner  (13:12) Takeaways from Skyscanner that Balint brought to Craft  (16:50) How frameworks work and why they aren’t always a good fit (20:35) An explanation of iOS Auto Layout and its pros and cons  (23:13) Why Balint doesn’t use Auto Layout  (24:23) Why Craft has one code base  (27:46) Craft’s unique toolbar features and a behind the scenes peek at the code  (33:15) Why frontend engineers have fear around customization  (37:11) How Craft’s design system differs from most companies  (42:33) Behaviors and elements Craft uses rather than having a system for everything  (44:12) The back and frontend architecture in building personal software  (48:11) Shifting beliefs in personal computing  (50:15) The challenges faced with operating system updates  (50:48) The resurgence of local-first software (52:31) The value of opinionated software for consumers  (55:30) Why Craft’s focus is on the user’s emotional experience (56:50) The size of Craft’s engineering department and platform teams (59:20) Why Craft moves faster with smaller teams (1:01:26) Balint’s advice for frontend engineers looking to demonstrate value  (1:04:35) Balint’s breakthroughs using GenAI (1:07:50) Why Balint still writes code (1:09:44) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • The AI hackathon at Craft Docs • Engineering career paths at Big Tech and scaleups • Thriving as a Founding Engineer: lessons from the trenches • The past and future of modern backend practices — 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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Generative AI has transformed the financial services sector, sparking interest at all organizational levels. As AI becomes more accessible, professionals are exploring its potential to enhance their work. How can AI tools improve personalization and fraud detection? What efficiencies can be gained in product development and internal processes? These are the questions driving the adoption of AI as companies strive to innovate responsibly while maximizing value. Andrew serves as the Chief Data Officer for Mastercard, leading the organization’s data strategy and innovation efforts while navigating current and future data risks. Andrews's prior roles at Mastercard include Senior Vice President, Data Management, in which he was responsible for the quality, collection, and use of data for Mastercard’s information services and advisory business, and Mastercard’s Deputy Chief Privacy Officer, in which he was responsible for privacy and data protection issues globally for Mastercard. Andrew also spent many years as a Privacy & Intellectual Property Council advising direct marketing services, interactive advertising, and industrial chemicals industries. Andrew holds Juris Doctor from Columbia University School of Law and has his bachelor’s degree, cum laude, in Chemical Engineering from the University of Delaware. Andrew is a retired member of the State Bar of New York. In the episode, Adel and Andrew explore GenAI's transformative impact on financial services, the democratization of AI tools, efficiency gains in product development, the importance of AI governance and data quality, the cultural shifts and regulatory landscapes shaping AI's future, and much more. Links Mentioned in the Show: MastercardConnect with AndrewSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: How Generative AI is Changing Leadership with Christie Smith, Founder of the Humanity Institute and Kelly Monahan, Managing Director, Research InstituteSign up to attend RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS. • Graphite — The AI developer productivity platform.  • Formation — Level up your career and compensation with Formation. — In today’s episode of The Pragmatic Engineer, I am joined by a senior software engineer and cartoonist, Manu Cornet. Manu spent over a decade at Google, doing both backend and frontend development. He also spent a year and a half at Twitter before Elon Musk purchased it and rebranded it to X. But what Manu is most known for are his hilarious internet comics about the tech world, including his famous org chart comic from 2011 about Facebook, Apple, Amazon, and Microsoft. In today’s conversation, we explore many of his comics, discuss the meaning behind them, and talk about the following topics:  • The viral org chart comic that captured the structure of Big Tech companies • Why Google is notorious for confusing product names • The comic that ended up on every door at Google • How Google’s 20% time fostered innovation—and what projects came from it • How one of Manu’s comics predicted Google Stadia’s failure—and the reasons behind it • The value of connecting to users directly  • Twitter’s climate before and after Elon Musk’s acquisition and the mass layoffs that followed • And more! — Timestamps (00:00) Intro (02:01) Manu’s org structure comic  (07:10) Manu’s “Who Sues Who” comic (09:15) Google vs. Amazon comic (14:10) Confusing names at Google (20:00) Different approaches to sharing information within companies (22:20) The two ways of doing things at Google (25:15) Manu’s code reviews comic (27:45) The comic that was printed on every single door of Google (30:55) An explanation of 20% at Google (36:00) Gmail Labs and Google Stadia (41:36) Manu’s time at Twitter and the threat of Elon Musk buying (47:07) How Manu helped Gergely with a bug on Twitter (49:05) Musk’s acquirement of Twitter and the resulting layoffs (59:00) Manu’s comic about his disillusionment with Twitter and Google (1:02:37) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How Manu creates comics • Consolidating technologies • Is Big Tech becoming more cutthroat? — 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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S1 Ep#33 Bridging Business and Data: The Art of Data Product Management The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. 

In Season 01, Episode 33, Amritha, our newest host, chats with Sagar Nikam, Head of Product at CK Delta. Sagar shares his journey from finance to data product management, highlighting the art of translating complex AI/ML models into actionable business strategies. He discusses the challenges of defining data products, the importance of clear communication, and why adoption often outweighs accuracy. Sagar also offers insights on handling uncertainty, setting success metrics, and the cross-industry applicability of data product management skills. Tune in for a deep dive into making data-driven decisions that drive real business impact.

About our host Amritha Arun Babu: Amritha is an accomplished Product Leader with over a decade of experience building and scaling products across AI platforms, supply chain systems, and enterprise workflows in industries such as e-commerce, AI/ML, and marketing automation. At Amazon, she led machine learning platform products powering recommendation and personalization engines, building tools for model experimentation, deployment, and monitoring that improved efficiency for 1,500+ ML scientists. At Wayfair, she managed international supply chain systems, overseeing contracts, billing, product catalogs, and vendor operations, delivering cost savings and optimizing large-scale workflows. At Klaviyo, she drives both AI infrastructure and customer-facing AI tools, including recommendation engines, content generation assistants, and workflow automation agents, enabling scalable and personalized marketing workflows. Earlier, she worked on enterprise systems and revenue operations workflows, focusing on cost optimization and process improvements in complex technical environments. Amritha excels at bridging technical depth with strategic clarity, leading cross-functional teams, and delivering measurable business outcomes across diverse domains. Connect with Amritha on LinkedIn.
About our guest Sagar Nikam: Sagar is the Head of Products at CKDelta, leading the development of AI-driven solutions, AI Agents, and intelligent applications that enhance decision-making and automation across industries. With experience in banking, utilities, and SaaS, he has successfully launched and scaled AI-powered products that drive real business impact. Sagar helps product teams seamlessly integrate AI, navigate challenges, and build solutions that are user-centric, explainable, and trusted. Connect with Sagar on LinkedIn.  

All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. 

Join the conversation on LinkedIn. 

Apply to be a guest or nominate someone that you know. 

Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

The Data Product Management In Action podcast, brought to you by executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. In this episode of Data Product Management in Action, we introduce our new host, Amritha Arun Babu! With over eight years of experience, Amritha shares her transition from engineering to product management and her impressive journey. She discusses scaling Amazon Today’s same-day delivery program, building code-to-cash products, and her current role at Klaviyo, where she’s shaping AI-driven features and refining ML platforms. Amritha emphasizes the value of understanding user needs, designing secure, scalable systems, and overcoming cross-functional challenges. Her advice to fellow product managers: network, share your experiences, and enjoy the ride! About our Host Amritha Arun Babu: Amritha is an accomplished Product Leader with over a decade of experience building and scaling products across AI platforms, supply chain systems, and enterprise workflows in industries such as e-commerce, AI/ML, and marketing automation. At Amazon, she led machine learning platform products powering recommendation and personalization engines, building tools for model experimentation, deployment, and monitoring that improved efficiency for 1,500+ ML scientists. At Wayfair, she managed international supply chain systems, overseeing contracts, billing, product catalogs, and vendor operations, delivering cost savings and optimizing large-scale workflows. At Klaviyo, she drives both AI infrastructure and customer-facing AI tools, including recommendation engines, content generation assistants, and workflow automation agents, enabling scalable and personalized marketing workflows. Earlier, she worked on enterprise systems and revenue operations workflows, focusing on cost optimization and process improvements in complex technical environments. Amritha excels at bridging technical depth with strategic clarity, leading cross-functional teams, and delivering measurable business outcomes across diverse domains. Connect with Amritha on LinkedIn. All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else.  Join the conversation on LinkedIn.  Apply to be a guest or nominate someone that you know.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights! 

Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS • CodeRabbit — Cut code review time and bugs in half • Augment Code — AI coding assistant that pro engineering teams love — How do you architect a live streaming system to deal with more load than it’s ever been done before? Today, we hear from an architect of such a system: Ashutosh Agrawal, formerly Chief Architect of JioCinema (and currently Staff Software Engineer at Google DeepMind.) We take a deep dive into video streaming architecture, tackling the complexities of live streaming at scale (at tens of millions of parallel streams) and the challenges engineers face in delivering seamless experiences. We talk about the following topics:  • How large-scale live streaming architectures are designed • Tradeoffs in optimizing performance • Early warning signs of streaming failures and how to detect them • Why capacity planning for streaming is SO difficult • The technical hurdles of streaming in APAC regions • Why Ashutosh hates APMs (Application Performance Management systems) • Ashutosh’s advice for those looking to improve their systems design expertise • And much more! — Timestamps (00:00) Intro (01:28) The world record-breaking live stream and how support works with live events (05:57) An overview of streaming architecture (21:48) The differences between internet streaming and traditional television.l (22:26) How adaptive bitrate streaming works (25:30) How throttling works on the mobile tower side  (27:46) Leading indicators of streaming problems and the data visualization needed (31:03) How metrics are set  (33:38) Best practices for capacity planning  (35:50) Which resources are planned for in capacity planning  (37:10) How streaming services plan for future live events with vendors (41:01) APAC specific challenges (44:48) Horizontal scaling vs. vertical scaling  (46:10) Why auto-scaling doesn’t work (47:30) Concurrency: the golden metric to scale against (48:17) User journeys that cause problems  (49:59) Recommendations for learning more about video streaming  (51:11) How Ashutosh learned on the job (55:21) Advice for engineers who would like to get better at systems (1:00:10) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Software architect archetypes https://newsletter.pragmaticengineer.com/p/software-architect-archetypes  • Engineering leadership skill set overlaps https://newsletter.pragmaticengineer.com/p/engineering-leadership-skillset-overlaps  • Software architecture with Grady Booch https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-booch — 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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Optimization and decision intelligence are reshaping industries, from logistics to finance. But what does this mean for professionals navigating daily challenges? Whether you're scheduling employees or managing power grids, finding the optimal solution can mean the difference between success and failure. How do you leverage optimization to make smarter, data-driven decisions? And how do you ensure these solutions are embraced by your team? Join us as we delve into the practical applications of optimization in the workplace. Duke Perrucci is the CEO at Gurobi Optimization. Prior to being appointed CEO, Duke served as CRO and COO since 2018. Perrucci has over 25 years of experience in sales, marketing, and analytics roles. Before joining Gurobi, he served at Cambridge Analytica, FocusVision, and Unilever. He also spent nine years with Information Resources, Inc., where he worked across the entire PepsiCo enterprise. Dr. Ed Klotz is a Senior Mathematical Optimization Specialist at Gurobi Optimization. Klotz has over 30 years of experience in the mathematical optimization software industry. He is a technical expert who has helped customers solve some of the world’s most challenging mathematical optimization problems. Dr. Klotz works closely with Gurobi's customers to support them in implementing and utilizing mathematical optimization in their organizations. He also interacts heavily with the R&D team based on his experiences with the customers. In the episode, Richie, Duke, and Ed explore decision intelligence, optimization in various industries, the synergy between optimization and machine learning, overcoming challenges in model building, the role of large language models in democratizing optimization, and much more. Links Mentioned in the Show: Gurobi OptimizationConnect with Duke and EdSkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: Making Better Decisions using Data & AI with Cassie Kozyrkov, Google's First Chief Decision ScientistSign up to RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Thought leadership is more than just a buzzword—it's a strategic tool that can significantly influence business decisions and relationships. But what makes thought leadership effective? How do you ensure your insights are not only heard but also trusted and acted upon? What role does generative AI play in enhancing the storytelling process, and how can it be leveraged to create compelling narratives that resonate with your audience? Cindy Anderson is the Chief Marketing Officer/Global Lead for Engagement & Eminence at the IBM Institute for Business Value (IBV).  She has co-authored research reports, published numerous articles, and delivered presentations on thought leadership, diversity, strategy implementation, project management, and technology to global audiences. She oversees a team of 30 editors, designers, and social media/email marketers. She is a founding board member of the Global Thought Leadership Institute at APQC, a new association that advances the practice of thought leadership. Anthony Marshall is the Chair of the Board of Advisors for The Global Thought Leadership Institute at APQC and the Senior Research Director of thought leadership at the IBM Institute for Business Value (IBV), leading the top-rated thought leadership and analysis program. He oversees a global team of 60 technology and industry experts, statisticians, economists, and analysts. Anthony conducts original thought leadership and has authored dozens of refereed articles and studies on topics including generative AI, innovation, digital and business transformation and ecosystems, open collaboration and skills. In the episode, Richie, Cindy, and Anthony explore the framework for thought leadership storytelling, the role of generative AI in thought leadership, the ROI of thought leadership, building trust and quality in research, and much more. Links Mentioned in the Show: The ROI of Thought Leadership book by Cindy and AnthonyAPQCConnect with Cindy and AnthonySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: How Generative AI is Changing Leadership with Christie Smith, Founder of the Humanity Institute and Kelly Monahan, Managing Director, Research InstituteSign up to RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Supported by Our Partners • WorkOS — The modern identity platform for B2B SaaS • CodeRabbit — Cut code review time and bugs in half • Augment Code — AI coding assistant that pro engineering teams love — How do you architect a live streaming system to deal with more load than it’s ever been done before? Today, we hear from an architect of such a system: Ashutosh Agrawal, formerly Chief Architect of JioCinema (and currently Staff Software Engineer at Google DeepMind.) We take a deep dive into video streaming architecture, tackling the complexities of live streaming at scale (at tens of millions of parallel streams) and the challenges engineers face in delivering seamless experiences. We talk about the following topics:  • How large-scale live streaming architectures are designed • Tradeoffs in optimizing performance • Early warning signs of streaming failures and how to detect them • Why capacity planning for streaming is SO difficult • The technical hurdles of streaming in APAC regions • Why Ashutosh hates APMs (Application Performance Management systems) • Ashutosh’s advice for those looking to improve their systems design expertise • And much more! — Timestamps (00:00) Intro (01:28) The world record-breaking live stream and how support works with live events (05:57) An overview of streaming architecture (21:48) The differences between internet streaming and traditional television.l (22:26) How adaptive bitrate streaming works (25:30) How throttling works on the mobile tower side  (27:46) Leading indicators of streaming problems and the data visualization needed (31:03) How metrics are set  (33:38) Best practices for capacity planning  (35:50) Which resources are planned for in capacity planning  (37:10) How streaming services plan for future live events with vendors (41:01) APAC specific challenges (44:48) Horizontal scaling vs. vertical scaling  (46:10) Why auto-scaling doesn’t work (47:30) Concurrency: the golden metric to scale against (48:17) User journeys that cause problems  (49:59) Recommendations for learning more about video streaming  (51:11) How Ashutosh learned on the job (55:21) Advice for engineers who would like to get better at systems (1:00:10) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • Software architect archetypes https://newsletter.pragmaticengineer.com/p/software-architect-archetypes  • Engineering leadership skill set overlaps https://newsletter.pragmaticengineer.com/p/engineering-leadership-skillset-overlaps  • Software architecture with Grady Booch https://newsletter.pragmaticengineer.com/p/software-architecture-with-grady-booch — 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].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe