What if your next data presentation felt less like a spreadsheet and more like a blockbuster movie? In this episode, we're joined by Angelica Lo Duca, Researcher at IIT-CNR and a master of transforming numbers into narratives, to explore the intersection of cinematography and data storytelling. Learn how to extract a "hero" from your data, structure your analysis like a compelling story, and tailor your presentations for different audiences to ensure maximum impact. Angelica reveals how the techniques filmmakers use—focusing on key characters, building tension, and delivering climactic resolutions—can be applied to data storytelling. She'll also discuss the role of the data storyteller as a guide and how to strike a balance between clarity and creativity. Whether you're presenting to executives, stakeholders, or teammates, this episode will help you level up your communication skills and make your insights unforgettable. What You'll Learn: How to identify and extract a "hero" from your data to drive a compelling narrative. Cinematic techniques that turn complex analyses into engaging stories. How to tailor your data presentation to resonate with different audiences. The responsibilities and creative power of the data storyteller.   Angelica is the author of Become a Great Storyteller: Learn How You Can Drive Change with Data. Find it here!     Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

In this episode, we look at the real story behind transformation in data and AI, and why the classic big bang approach often fails to deliver lasting impact. Jason explores when large-scale transformation programmes do make sense, like when you're starting from a fundamentally broken place, or when disruption is the goal. But he also digs into the messy reality of what usually happens: slow delivery, rigid plans, lost trust, and a disconnect between activity and real outcomes. He then makes the case for iterative change. A more human, responsive, and sustainable way to build meaningful transformation over time. With real-world examples and sharp reflections, Jason shares how small, focused steps can create big shifts, and how to blend bold vision with practical delivery. This episode is full of insight for business and data leaders navigating change, delivering transformation, or just trying to make something actually stick. ****    Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. • Graphite — The AI developer productivity platform. — There’s no shortage of bold claims about AI and developer productivity, but how do you separate signal from noise? In this episode of The Pragmatic Engineer, I’m joined by Laura Tacho, CTO at DX, to cut through the hype and share how well (or not) AI tools are actually working inside engineering orgs. Laura shares insights from DX’s research across 180+ companies, including surprising findings about where developers save the most time, why devs don’t use AI at all, and what kinds of rollouts lead to meaningful impact. We also discuss:  • The problem with oversimplified AI headlines and how to think more critically about them • An overview of the DX AI Measurement framework • Learnings from Booking.com’s AI tool rollout • Common reasons developers aren’t using AI tools • Why using AI tools sometimes decreases developer satisfaction • Surprising results from DX’s 180+ company study • How AI-generated documentation differs from human-written docs • Why measuring developer experience before rolling out AI is essential • Why Laura thinks roadmaps are on their way out • And much more! — Timestamps (00:00) Intro (01:23) Laura’s take on AI overhyped headlines  (10:46) Common questions Laura gets about AI implementation  (11:49) How to measure AI’s impact  (15:12) Why acceptance rate and lines of code are not sufficient measures of productivity (18:03) The Booking.com case study (20:37) Why some employees are not using AI  (24:20) What developers are actually saving time on  (29:14) What happens with the time savings (31:10) The surprising results from the DORA report on AI in engineering  (33:44) A hypothesis around AI and flow state and the importance of talking to developers (35:59) What’s working in AI architecture  (42:22) Learnings from WorkHuman’s adoption of Copilot  (47:00) Consumption-based pricing, and the difficulty of allocating resources to AI  (52:01) What DX Core 4 measures  (55:32) The best outcomes of implementing AI  (58:56) Why highly regulated industries are having the best results with AI rollout (1:00:30) Indeed’s structured AI rollout  (1:04:22) Why migrations might be a good use case for AI (and a tip for doing it!)  (1:07:30) Advice for engineering leads looking to get better at AI tooling and implementation  (1:08:49) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • AI Engineering in the real world • Measuring software engineering productivity • The AI Engineering stack • A new way to measure developer productivity – from the creators of DORA and SPACE — 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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Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: Inscrições do Data Hackers Challenge 2025 Live Zoho: Decisões Baseadas em Dados: Aplicando Machine Learning com o Zoho Analytics Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

I’ve spent the last 10 years working as a data analyst, data scientist, and data engineer for some pretty cool companies like ExxonMobil, MIT, the Utah Jazz, and others. And the last 4, I’ve spent them teaching others how to land their first data job. My students now work at Apple, Amazon, Rivian, Tesla, and other cool companies. Let me share the 13 things I wish I knew when I was getting started. 💌 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 00:28 - 1. Your Skills Aren't Holding You Back 01:56 - 2. You Will Get Paid to Learn on the Job 03:25 - 3. You Don't Have to Know Everything 04:27 - 4. Who You Know Matters More Than What You Do 07:08 - 5. Your Domain Expertise Matters 09:20 - 6. Don't Take Job Rejections Personally 12:07 - 7. Data Job Titles Are Confusing 13:29 - 8. Data Tools Matter Less Than You Think 14:38 - 9. The Bookends of Analysis Are Most Important 16:14 - 10. How You Present Your Digital Self Is Important 17:42 - 11. All Industries Experience Cycles 20:11 - 12. Mentorship is the Shortcut to Results 22:11 - 13. You'll Never Stop Learning 🔗 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.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

In this episode of Experiencing Data, I chat with Irina Malkova who is the VP of AI Engineering and VP of Data and Analytics for Tech and Product at Salesforce. Irina shares how her teams are reinventing internal analytics, combining classic product data work with cutting-edge AI engineering—and her recent post on LinkedIn titled “AI adoption moves at the speed of user trust,” having a strong design-centered perspective, inspires today’s episode. (I even quoted her on this in a couple recent product design conference talks I gave!)  In today’s drop, Irina shares how they’re enabling analytical insights at Salesforce via a Slack-based AI agent, how they have changed their AI and engineering org structures (and why), the bad advice they got on organizing their data product teams, and more. This is a great episode for senior data product and AI executives managing complex orgs and technology environments who want to see how Salesforce is scaling AI for smarter, faster decisions.

In this season of the Analytics Engineering podcast, Tristan is deep into the world of developer tools and databases. If you're following us here, you've almost definitely used Amazon S3 it and its Blob Storage siblings. They form the foundation for nearly all data work in the cloud. In many ways, it was the innovations that happened inside of S3 that have unlocked all of the progress in cloud data over the last decade. In this episode, Tristan talks with Andy Warfield, VP and senior principal engineer at AWS, where he focuses primarily on storage. They go deep on S3, how it works, and what it unlocks. They close out italking about Iceberg, S3 table buckets, and what this all suggests about the outlines of the S3 product roadmap moving forward. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Automotive economists Jonathan Smoke (Cox Automotive) and Michael Brisson (Moody's Analytics) join Mark and Cris to discuss industry conditions, tariff impacts on production and pricing, and their divergent views on auto credit's future. Inside Economics producer, Sara Rodriguez, makes a special guest appearance to settle the podcast's ongoing chit-chat debate. Read more articles by Jonathan Smoke here Related Research on today's topic: Click here and here Guests: Mike Brisson - director - Economic Research, Jonathan Smoke - Chief Economist & Economic Advisor for Cox Automotive Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' and BlueSky @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn Questions or Comments, please email us at [email protected]. We would love to hear from you.   

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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In this episode, we'll chat with Carly Taylor, Field CTO of Gaming at Databricks, to explore the fascinating world of data analytics in the gaming industry, where every click, quest, and respawn generates insights that shape the games we love. Carly shares her experience working in gaming to help harness data for better gameplay and smarter monetization. She'll break down what analysts, data scientists, and sales engineers actually do in gaming and how teams turn raw data into real-time decisions. Whether you're a player, a data nerd, or someone who wants to turn both into a career, this episode is your walkthrough guide to data in gaming. What You'll Learn: How gaming companies use data to optimize player experience and business outcomes  What it's like to work in a field engineering or customer-facing analyst role The tools, KPIs, and best practices for success How to break into a data role in gaming and what skills to focus on   Stay updated with Carly's latest by subscribing to her Substack   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Understand the fundamentals of SAP supply chain management and intelligence with the new Business Network for Logistics (BN4L) & Global Track and Trace (GTT) systems for SAP customers. It highlights how SAP Business Network enhances collaboration between suppliers, manufacturers, and logistics providers by leveraging shared business objects and real-time data. These integrations helps businesses achieve greater efficiency, transparency, and collaboration across their supply chain operations by leveraging intelligent insights and real-time data so they can better meet the demands of their customers. Getting Started with BN4L and GTT Integrations for SAP will not only provide you with the key concepts and definitions but also system configurations and real-world case studies giving you the skills and knowledge to start using SAP BN4L & GTT with confidence. You Will: Gain insights into the BN4 & GTT Intelligence Network and how it is used in the SAP applications. Learn the network for freight collaboration & shipment status. Learn the basics of systems administration, master data for the network, and integration scenarios with SAP S/4HANA. Understand a high level of end-to-end business processes. How it all works together and review analytics to get a better understanding of shared business objects. Who is this Book for: SAP Supply Chain Consultants, SAP Supply Chain Architect, SAP Technical Consultant and SAP Customers

Moody’s Analytics Mark Zandi and Cris deRitis are joined by Ira Goldstein from The Reinvestment Fund, Maggie McCullough from PolicyMap, and Jim Parrott from the Urban Institute to discuss their new study that takes a deep dive into understanding the nature of the decade-long housing shortfall. This housing crisis has driven up house prices and rents, and undermined housing affordability. But despite the heightened political attention on the problem, there remains confusion over its true scale and scope. This team of self-avowed housers dissect the shortage down to the census tract and come to some surprising conclusions. To learn more and access the full research paper: https://www.economy.com/bringing-the-housing-shortage-into-sharper-focus Guest: Ira Goldstein, Senior Advisor at The Reinvestment Fund Guest: Maggie McCullough, CEO and Founder of PolicyMap Guest: Jim Parrott, Nonresident Fellow at the Urban Institute Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' and BlueSky @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

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Supported by Our Partners •⁠ WorkOS — The modern identity platform for B2B SaaS. •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Sonar — Code quality and code security for ALL code. — Steve Yegge⁠ is known for his writing and “rants”, including the famous “Google Platforms Rant” and the evergreen “Get that job at Google” post. He spent 7 years at Amazon and 13 at Google, as well as some time at Grab before briefly retiring from tech. Now out of retirement, he’s building AI developer tools at Sourcegraph—drawn back by the excitement of working with LLMs. He’s currently writing the book Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond. In this episode of The Pragmatic Engineer, I sat down with Steve in Seattle to talk about why Google consistently failed at building platforms, why AI coding feels easy but is hard to master, and why a new role, the AI Fixer, is emerging. We also dig into why he’s so energized by today’s AI tools, and how they’re changing the way software gets built. We also discuss:  • The “interview anti-loop” at Google and the problems with interviews • An inside look at how Amazon operated in the early days before microservices   • What Steve liked about working at Grab • Reflecting on the Google platforms rant and why Steve thinks Google is still terrible at building platforms • Why Steve came out of retirement • The emerging role of the “AI Fixer” in engineering teams • How AI-assisted coding is deceptively simple, but extremely difficult to steer • Steve’s advice for using AI coding tools and overcoming common challenges • Predictions about the future of developer productivity • A case for AI creating a real meritocracy  • And much more! — Timestamps (00:00) Intro (04:55) An explanation of the interview anti-loop at Google and the shortcomings of interviews (07:44) Work trials and why entry-level jobs aren’t posted for big tech companies (09:50) An overview of the difficult process of landing a job as a software engineer (15:48) Steve’s thoughts on Grab and why he loved it (20:22) Insights from the Google platforms rant that was picked up by TechCrunch (27:44) The impact of the Google platforms rant (29:40) What Steve discovered about print ads not working for Google  (31:48) What went wrong with Google+ and Wave (35:04) How Amazon has changed and what Google is doing wrong (42:50) Why Steve came out of retirement  (45:16) Insights from “the death of the junior developer” and the impact of AI (53:20) The new role Steve predicts will emerge  (54:52) Changing business cycles (56:08) Steve’s new book about vibe coding and Gergely’s experience  (59:24) Reasons people struggle with AI tools (1:02:36) What will developer productivity look like in the future (1:05:10) The cost of using coding agents  (1:07:08) Steve’s advice for vibe coding (1:09:42) How Steve used AI tools to work on his game Wyvern  (1:15:00) Why Steve thinks there will actually be more jobs for developers  (1:18:29) A comparison between game engines and AI tools (1:21:13) Why you need to learn AI now (1:30:08) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ The full circle of developer productivity with Steve Yegge •⁠ Inside Amazon’s engineering culture •⁠ Vibe coding as a software engineer •⁠ AI engineering in the real world •⁠ The AI Engineering stack •⁠ Inside Sourcegraph’s engineering culture— 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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Madison Schott joins me to chat about about her journey from working as an analytics engineer to creating content about dbt, SQL, data modeling, and more.

The data analysis landscape is changing rapidly. New AI tools are emerging every week, and it can sometimes feel overwhelming. So in this video, I compare ChatGPT and Julius AI to see how they stack up against each other. We'll use a dataset of 1,444 data job listings from FindADataJob.com to analyze trends in the 2025 data job market to answer the question: Which AI tool is best suited for your data analysis needs? Where I Go To Find Datasets (as a data analyst) 👉 https://datacareerpodcast.com/episode/131-7-resources-to-find-amazing-datasets-free 💌 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 00:20 Comparing ChatGPT and Julius AI 01:17 Uploading and Previewing Data 03:01 Data Analysis Suggestions 05:04 What State Has The Most Jobs Listed? 08:08 Analyzing Job Trends Over Time 10:31 Customizable Chart Themes 11:10 Analyzing Job Salary Trends 17:01 Investigating Experience Levels in Job Listings 19:44 Handling Missing Data with MissingNo 21:34 Exploring Interesting Trends in the Dataset 25:34 Conclusion: Which Tool Should You Use? 🔗 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.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Chicago Federal Reserve President Austan Goolsbee joins Mark and Cris to talk about the economy and monetary policy. He explains that the up and down tariffs and other economic policies have thrown lots of dirt in the air, so to speak, complicating things for the Fed and thus delaying the normalization of interest rates. He also weighs in on the policy response to the financial crisis and the economic repercussions of artificial intelligence. And tune in to hear why he wants to be 80% Paul Volker and 20% Muhammad Ali.   Guest: Austan Goolsbee, President of the Federal Reserve Bank of Chicago Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X' and BlueSky @MarkZandi, Cris deRitis on LinkedIn, and Marisa DiNatale on LinkedIn

Questions or Comments, please email us at [email protected]. We would love to hear from you.    To stay informed and follow the insights of Moody's Analytics economists, visit Economic View.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

What does it take to turn raw data into actionable insights, especially in high-stakes environments? In this compelling episode, we sit down with Doug Needham, author of Data Structure Synthesis and The Enrichment Game. From his fascinating work updating data virtually during Desert Storm to insights on leveraging third-party data for enterprise decisions, Doug brings a wealth of real-world stories and practical advice to this conversation. You'll learn how to think critically about your data strategy, navigate complex enterprise data ecosystems, and see data as more than just numbers on a screen—it's a resource with untapped potential. Whether you're a data professional, analyst, or someone just starting to explore the world of analytics, this episode is packed with lessons on innovation, efficiency, and making data work smarter, not harder. What You'll Learn: How data enrichment unlocks the full potential of data. The role of data structure synthesis in solving complex enterprise challenges. The realities of working with data in a corporate setting.   Doug is also the author of The Enrichment Game - A Story About Making Data Powerful: find it here!   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

This book addresses an important gap in data analytics education: the interplay between complex query-making and storytelling. While many resources cover the fundamentals of SQL queries and the technical skills required to manipulate data, few also explore moving beyond the numbers and figures to tell stories that drive strategic business decisions. By weaving together both SQL and narrative mechanics, author Hamed Tabrizchi has assembled a powerful tool for data analysts, aspiring database professionals, and business intelligence specialists. A strong foundation is laid in the first part of the book, which examines the technical skills necessary to access and manipulate data. You’ll explore foundational SQL commands, advanced querying techniques, data manipulation, data integrity, and optimization of queries for performance. The second half moves from the "how" of SQL to the "why," examining the meaning-making practices we can apply to data, and the stories data can tell. You'll learn how SQL queries can be interpreted, how to prepare data for visualization, and most importantly, how to convey the findings in a way that engages and informs the audience. In each chapter, practical exercises reinforce the techniques learned and help you apply them in real-world situations. In addition to strengthening technical skills, these exercises encourage readers to take a critical view of the data they are studying, considering the larger story it represents. Upon completing this book, you will not only be proficient in SQL, but also possess the key skill of converting data into narratives that can influence strategic direction and operational decisions in the modern workplace. What You Will Learn Advanced SQL Techniques: Master data manipulation and retrieval skills using advanced SQL queries Data Analysis Proficiency: Develop analytical skills to uncover key insights and understand significant data patterns Storytelling with Data: Learn to translate data analytics into compelling narratives for effective stakeholder communication Complex Querying Skills: Understand advanced SQL concepts such as common table expressions (CTEs), subqueries, and window functions Query Optimization: Optimize query execution time, resource usage, and scalability by mastering Indexes and Views Practical Application of Techniques: Gain hands-on experience with practical examples of advanced SQL techniques in real-world data analysis scenarios Effective Data Presentation: Discover strategies for visually presenting data stories to enhance engagement and understanding among diverse audiences Who This Book Is For Data analysts and business analysts, SQL developers, data-driven managers and executives and academics and students looking to enhance advanced querying and narrative building skills to better interpret and convey data.

In Python, data analytics users often prioritize convenience, flexibility, and familiarity over pure performance. The cuDF DataFrame library provides a pandas-like experience with from 10x up to 50x performance improvements, but subtle differences prevent it from being a true drop-in replacement for many users. This talk will showcase the evolution of this library to provide zero-code change experiences, first for pandas users and now for Polars. We will provide examples of this usage and a high level overview of how users can make use of these today. We will then delve into the details of how GPU acceleration is implemented differently in pandas and Polars, along with a deep dive into some of the different technical challenges encountered for each. This talk will have something for both data practitioners and library developers.

In this episode of Hub & Spoken, Jason Foster talks to Cali Wood, Head of Data and AI Strategy & Culture at AXA UK and Ireland. Cali shares how AXA is shaping its data and AI transformation through a clear strategic framework built on creation of value, connection of data and tooling, and culture to accelerate value. From embedding human-centred design into automation use cases to launching a data and AI academy with more than 50% workforce engagement, AXA is making data and AI a true business-wide initiative. This conversation explores: The three pillars of AXA's data and AI strategy How culture and leadership unlock real business value Scaling responsible AI across a highly regulated industry Evolving from traditional to agentic AI in a people-first way Whether you're leading data transformation or navigating GenAI, this episode offers practical ideas and inspiration to help bring your people and strategy together. Listen now to learn how to build AI-driven change - the right way.

Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023 and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024. Cynozure is a certified B Corporation. 

Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. • Graphite — The AI developer productivity platform.  • Augment Code — AI coding assistant that pro engineering teams love. — Steve Huynh spent 17 years at Amazon, including four as a Principal Engineer. In this episode of The Pragmatic Engineer, I join Steve in his studio for a deep dive into what the Principal role actually involves, why the path from Senior to Principal is so tough, and how even strong engineers can get stuck. Not because they’re unqualified, but because the bar is exceptionally high. We discuss what’s expected at the Principal level, the kind of work that matters most, and the trade-offs that come with the title. Steve also shares how Amazon’s internal policies shaped his trajectory, and what made the Principal Engineer community one of the most rewarding parts of his time at the company. We also go into:  • Why being promoted from Senior to Principal is one of the hardest jumps in tech • How Amazon’s freedom of movement policy helped Steve work across multiple teams, from Kindle to Prime Video • The scale of Amazon: handling 10k–100k+ requests per second and what that means for engineering • Why latency became a company-wide obsession—and the research that tied it directly to revenue • Why companies should start with a monolith, and what led Amazon to adopt microservices • What makes the Principal Engineering community so special  • Amazon’s culture of learning from its mistakes, including COEs (correction of errors)  • The pros and cons of the Principal Engineer role • What Steve loves about the leadership principles at Amazon • Amazon’s intense writing culture and 6-pager format  • Why Amazon patents software and what that process looks like • And much more! — Timestamps (00:00) Intro (01:11) What Steve worked on at Amazon, including Kindle, Prime Video, and payments (04:38) How Steve was able to work on so many teams at Amazon  (09:12) An overview of the scale of Amazon and the dependency chain (16:40) Amazon’s focus on latency and the tradeoffs they make to keep latency low at scale (26:00) Why companies should start with a monolith  (26:44) The structure of engineering at Amazon and why Amazon’s Principal is so hard to reach (30:44) The Principal Engineering community at Amazon (36:06) The learning benefits of working for a tech giant  (38:44) Five challenges of being a Principal Engineer at Amazon (49:50) The types of managing work you have to do as a Principal Engineer  (51:47) The pros and cons of the Principal Engineer role  (54:59) What Steve loves about Amazon’s leadership principles (59:15) Amazon’s intense focus on writing  (1:01:11) Patents at Amazon  (1:07:58) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ Inside Amazon’s engineering culture — 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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