Summary In this episode of the Data Engineering Podcast Chakravarthy Kotaru talks about scaling data operations through standardized platform offerings. From his roots as an Oracle developer to leading the data platform at a major online travel company, Chakravarthy shares insights on managing diverse database technologies and providing databases as a service to streamline operations. He explains how his team has transitioned from DevOps to a platform engineering approach, centralizing expertise and automating repetitive tasks with AWS Service Catalog. Join them as they discuss the challenges of migrating legacy systems, integrating AI and ML for automation, and the importance of organizational buy-in in driving data platform success.

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 Chakri Kotaru about scaling successful data operations through standardized platform offeringsInterview IntroductionHow did you get involved in the area of data management?Can you start by outlining the different ways that you have seen teams you work with fail due to lack of structure and opinionated design?Why NoSQL?Pairing different styles of NoSQL for different problemsUseful patterns for each NoSQL style (document, column family, graph, etc.)Challenges in platform automation and scaling edge casesWhat challenges do you anticipate as a result of the new pressures as a result of AI applications?What are the most interesting, innovative, or unexpected ways that you have seen platform engineering practices applied to data systems?What are the most interesting, unexpected, or challenging lessons that you have learned while working on data platform engineering?When is NoSQL the wrong choice?What do you have planned for the future of platform principles for enabling data teams/data applications?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 RiakDynamoDBSQL ServerCassandraScyllaDBCAP TheoremTerraformAWS Service CatalogBlog PostThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Cliff Rossi, Professor of the Practice and Director of the Smith Enterprise Risk Consortium at the University of Maryland, joins the podcast to discuss the future of housing finance and the potential release of Fannie Mae and Freddie Mac from government conservatorship. The team also delves into Dr. Rossi's proposal for fixing the homeowners insurance market and explores concerns surrounding private credit. Guest: Clifford Rossi, Professor of the Practice, Director, Smith Enterprise Risk Consortium Executive-in-Residence PhD, Cornell University Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @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.

Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Sinch⁠ — Connect with customers at every step of their journey. •⁠ Cortex⁠ — Your Portal to Engineering Excellence. — What does it take to land a job as an AI Engineer—and thrive in the role? In this episode of Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team. In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field. We also discuss the following:  • How Janvi landed internships at Google and Microsoft, and her tips for interview prepping • A framework for evaluating AI startups • An overview of what an AI Engineer does • A mini curriculum for self-learning AI: practical tools that worked for Janvi • The Coda project that impressed CEO Shishir Mehrotra and sparked Coda Brain • Janvi’s role at OpenAI and how the safety team shapes responsible AI • How OpenAI blends startup speed with big tech scale • Why AI Engineers must be ready to scrap their work and start over • Why today’s engineers need to be product-minded, design-aware, full-stack, and focused on driving business outcomes • And much more! — Timestamps (00:00) Intro (02:31) How Janvi got her internships at Google and Microsoft (03:35) How Janvi prepared for her coding interviews  (07:11) Janvi’s experience interning at Google (08:59) What Janvi worked on at Microsoft  (11:35) Why Janvi chose to work for a startup after college (15:00) How Janvi picked Coda  (16:58) Janvi’s criteria for picking a startup now  (18:20) How Janvi evaluates ‘customer obsession’  (19:12) Fast—an example of the downside of not doing due diligence (21:38) How Janvi made the jump to Coda’s AI team (25:48) What an AI Engineer does  (27:30) How Janvi developed her AI Engineering skills through hackathons (30:34) Janvi’s favorite AI project at Coda: Workspace Q&A  (37:40) Learnings from interviewing at 46 companies (40:44) Why Janvi decided to get experience working for a model company  (43:17) Questions Janvi asks to determine growth and profitability (45:28) How Janvi got an offer at OpenAI, and an overview of the interview process (49:08) What Janvi does at OpenAI  (51:01) What makes OpenAI unique  (52:30) The shipping process at OpenAI (55:41) Surprising learnings from AI Engineering  (57:50) How AI might impact new graduates  (1:02:19) The impact of AI tools on coding—what is changing, and what remains the same (1:07:51) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ AI Engineering in the real world •⁠ The AI Engineering stack •⁠ Building, launching, and scaling ChatGPT Images — 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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Qualitative and quantitative techniques to apply decision analysis to real-world decision problems, supported by sound mathematics, best practices, soft skills, and more With substantive illustrations based on the authors’ personal experiences throughout, Handbook of Decision Analysis describes the philosophy, knowledge, science, and art of decision analysis. Key insights from decision analysis applications and behavioral decision analysis research are presented, and numerous decision analysis textbooks, technical books, and research papers are referenced for comprehensive coverage. This book does not introduce new decision analysis mathematical theory, but rather ensures the reader can understand and use the most common mathematics and best practices, allowing them to apply rigorous decision analysis with confidence. The material is supported by examples and solution steps using Microsoft Excel and includes many challenging real-world problems. Given the increase in the availability of data due to the development of products that deliver huge amounts of data, and the development of data science techniques and academic programs, a new theme of this Second Edition is the use of decision analysis techniques with big data and data analytics. Written by a team of highly qualified professionals and academics, Handbook of Decision Analysis includes information on: Behavioral decision-making insights, decision framing opportunities, collaboration with stakeholders, information assessment, and decision analysis modeling techniques Principles of value creation through designing alternatives, clear value/risk tradeoffs, and decision implementation Qualitative and quantitative techniques for each key decision analysis task, as opposed to presenting one technique for all decisions. Stakeholder analysis, decision hierarchies, and influence diagrams to frame descriptive, predictive, and prescriptive analytics decision problems to ensure implementation success Handbook of Decision Analysis is a highly valuable textbook, reference, and/or refresher for students and decision professionals in business, management science, engineering, engineering management, operations management, mathematics, and statistics who want to increase the breadth and depth of their technical and soft skills for success when faced with a professional or personal decision.

Break into data analytics EVEN without a degree, just like our guest for today's episode! He's Ryan Ponder, a Data Analytics Accelerator program student who transitioned from Loan Officer to Data Analyst within his company-- without a degree. He shares how he leveraged internal opportunities and attained his new role. Tune in and learn actionable steps for making an internal pivot and overcoming career challenges! 💌 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 05:49 - The Internal Pivot: A Unique Pathway to Data Analytics 09:57 - Networking and Overcoming Challenges 15:47 - Imposter Syndrome 23:02 - Final Thoughts and Advice 🔗 CONNECT WITH RYAN 🤝 LinkedIn: https://www.linkedin.com/in/rtponder/ 🔗 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, Tristan talks to Zach Lloyd, founder of Warp—a terminal built for the modern era, including for AI agents. They explore the history of terminals, differences between terminals and shells, and what the future might look like. In a world driven by generative AI, the terminal could once again be the control center of computer usage. 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.

The Inside Economics team is joined by our colleagues Adam Kamins and Justin Begley to play the statistics game for nearly the entire podcast. There are lots of good stats, and it’s a nice respite from all the economic drama for the long Memorial Day weekend. Guests: Justin Begley - Economist, Moody's Analytics, Adam Kamins - Senior Director and Head of Regional Economics, Moody's Analytics Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @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.

In business, ideas don't stand alone; they have to be communicated effectively to drive action. Whether through reports, presentations, or product pitches, success comes from not just having data expertise but knowing how to make it matter. Yet many in the data world focus on just establishing a "personal brand" rather than developing something tangible. So, what does it take to turn expertise into a real product? In this episode with Scott Taylor, The Data Whisperer, we take a behind-the-scenes look at how creativity and communication shape success in the data space beyond just technical skills or personal branding. Listen for a raw and insightful conversation on navigating creativity, career growth, and the business of turning data expertise into something real. What You'll Learn: Why communication is a superpower in business and data The balance between technical skills and storytelling in shaping ideas How creativity applies in a structured B2B context Content, branding, and the tools of a data creator—beyond just a LinkedIn presence  The challenge of crafting the perfect one-sentence pitch   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 today's data-driven world, earning the Tableau Certified Data Analyst credential signals your ability to connect, analyze, and communicate insights using one of the industry's leading visualization platforms. This study guide offers practical and comprehensive preparation for the certification exam, with walk-throughs, best practices, vocabulary, and example questions to help you build both confidence and competence in Tableau. Written by Christopher Gardner, business intelligence analyst and lead Tableau developer at the University of Michigan, this guide supports first-time test-takers and seasoned users alike. You'll begin with foundational skills in Tableau Prep Builder and Tableau Desktop—connecting, combining, and preparing data—before progressing to building effective visualizations, performing calculations, and applying advanced tools like level-of-detail expressions, parameters, forecasts, and predictive analytics. Read, manipulate, and prepare data for analysis Navigate Tableau's tools to build impactful visualizations Write calculations and functions to enhance your dashboards Share your work responsibly with secure publishing options

Struggling to break into data analyst roles because you have no experience? In this episode, I reveal 6 overlooked, easier-to-land entry-level data jobs that can kickstart your data career. I also provide 7 reasons why these roles are worth considering! 💌 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 01:43 - Six overlooked data jobs 02:09 - Reason 1: Domain-focused 03:18 - Reason 2: Easier to qualify for 04:26 - Reason 3: Less competition 05:34 - Reason 4: Still provide real data experience 06:42 - Reason 5: Often remote or hybrid 07:49 - Reason 6: Internal pivot 09:03 - Reason 7: Decent pay

🔗 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

Some people speculate that AI will make software and data engineers obsolete. If the only thing engineers do is write code, sure.

But we do a lot more than that, and I believe we'll actually need more engineers, not fewer.

In this episode, I discuss how I think AI will change the craft of software and data engineering. Spoiler - I think it will make it way more fun and productive.

Thanks to dbt and GoodData for sponsoring this episode. Please support them, as they're awesome.

dbt Launch Showcase Join dbt Labs May 28 for the dbt Launch Showcase to hear from executives and product leaders about the latest features landing in dbt. See firsthand how features will empower data practitioners and organizations in the age of AI.

GoodData Webinar Analytics and data engineering used to live in separate worlds—different teams, different tools, different goals. But the lines are blurring fast. As modern data products demand speed, scale, and seamless integration, the best teams are embracing engineering principles and best practices. In this no-BS conversation, Ryan Dolley, Matt Housley, and Joe Reis, dive into how engineering principles are transforming the way analytics is built, delivered, and scaled. 📆 May 27, 2025🕘 9:00 AM PDT, 12:00 PM EDT, 6:00 PM CEST🔗 Register here!

The Inside Economics team welcomes back Aaron Klein, senior fellow in Economic Studies at the Brookings Institution, for his fourth appearance. The episode begins with an analysis of the latest economic indicators, unpacking fresh CPI, PPI, and retail sales data. Mark then asks the team to weigh in on how recent tariff announcements have altered their economic forecasts and recession probabilities. Moody's Analytics economist Justin Begley provides a breakdown of the budget reconciliation package moving through Congress. The episode concludes with Aaron's assessment of emerging vulnerabilities and potential flashpoints in the financial system. Guest: Aaron Klein, Senior Fellow at the Brookings Institution Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Follow Mark Zandi on 'X', BlueSky or LinkedIn @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.

Breaking away from the corporate ladder to carve your own path is a journey filled with challenges, growth, and unexpected rewards. In this episode, we host Christina Stathopoulos: a trailblazer who transitioned from a Google role to entrepreneurship and gained experience trying to build her own company in Spain!  Together, we explore what it means to make an impact in your career, the art of building a personal brand, and how to navigate pivotal career decisions with confidence. Christina shares candid advice and discusses how embracing change can lead to unparalleled opportunities but also can lead to difficulties. Whether you're dreaming of stepping into entrepreneurship, striving to shape a meaningful career, or looking to grow your personal brand, this show will be packed with insights for data professionals! What You'll Learn: Actionable advice for shaping a career aligned with your goals and values. How to transition from corporate to entrepreneurship. The importance of building a personal brand and why it matters. Insights from working abroad: perspectives on career and growth.   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 of Hub & Spoken, Jason Foster speaks with Colin Zima, CEO and Co-founder of Omni, a modern business intelligence platform that combines the best of governance and usability. With a background spanning roles at Looker and Google, and two decades as both a data user and builder, Colin brings a unique perspective on the evolution of BI and the real role of AI in shaping its future. They explore why business intelligence remains critical for aligning organisations, how AI is raising the bar for access and self-service, and why semantics and business logic are more important than ever. The conversation challenges the notion that AI will replace dashboards, and instead focuses on how it can enhance accessibility, support different user needs, and empower data teams to work more efficiently. This episode is essential listening for business and data leaders thinking about the future of BI, the practical use of AI, and the role data teams play in delivering real value at speed. Tune in to hear how modern BI is evolving, and what leaders need to know to stay ahead. ****    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. 

Data and analytics leaders and their data engineering teams are tasked with evaluating and selecting data integration tools. However, there are many options, which can be confusing. This session will explain the various types of data integration tools and technologies available in the market, and help you select the right data integration tool for your needs.

Successful data science teams require the alignment of technical talent, business goals and collaborative culture to drive high-impact analytics outcomes. Leaders and members of these teams have a vital role to play to ensure optimal collaboration and impact. This session highlights what modern data science teams look like, the vital and exciting work they do, and how to ensure these teams thrive.

For over a decade, we have sought a holistic, unifying theory of data management. This presentation documents the quest, and touches on the data and analytics infrastructure model (DAIM), metadata, data fabric, data ecosystems, and FinOps. Each of these is required and together they address everything from infrastructure to AI to strategy communications.

This presentation of Gartner's top D&A predictions for 2024 guides data and analytics leaders as they plan for the impact of AI on all aspects of data and analytics, including leadership, governance, talent, market, and the need for AI-ready data and a FinAI competency for value realization.

Given the ease with which AI-based initiatives can be executed nowadays, it is no surprise that more and more enterprises are considering adopting AI-powered autonomous analytics to make well-informed decisions more economically, improve operational efficiencies, and delight customers. Achieving the true promise of AI, however, requires access to data and the execution of such analytics in real time. Join this session to learn about data processing challenges and architecture patterns fueling AI-driven businesses.

Business initiatives succeed when data-driven decisions consistently guide business outcomes. ZenOptics invites Mark Emery, Group Data & Analytics Manager - GAP Group and Toby Pearson, UK Managing Partner - Projective Group to share their knowledge and experiences. Attendees will gain valuable insights on how to rein in analytics chaos, improve user experience, organize analytics for easy access and discovery, foster success of the overall information strategy using an analytics catalog, and the importance of analytics governance to drive trust, achieve cost transparency, and risk reduction.