Seth Rosen has broken data Twitter many times, and in his early-fatherhood sleep deprivation developed a wonderful Twitter persona as the battle-tested data analyst. IRL though Seth is a serious data practitioner, and as Founder at the data consultancy HashPath has helped dozens of companies get into the modern data stack + build public-facing data apps.  Now, as the founder of TopCoat, he's empowering analysts to build + publish those same public-facing data apps. In this episode, Tristan, Julia & Seth graciously dive into spicy debates around data mesh + "dashboard factories", and explore a future where data analysts become full-stack application developers. 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 Delta-variant of COVID-19 has damaged the economic recovery, but we remain optimistic the economy is on track to return to full employment by spring 2023. What could derail this optimism? Could the economy perform better than anticipated? What is the long-term economic fallout of the pandemic? The episode's slides can be found here.

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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We talked to George Firican from Lights on Data about all things data governance and management. Check out their YouTube! 

As always, please leave a review and subscribe to the Data Career Podcast! 

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

What is a system without empathy? What is a show summary without an attempt to overly distill the discussion to the point of sounding like nonsense? On this episode, Hilary Parker (who you may know from the Not So Standard Deviations podcast or elsewhere) joined us to discuss what we can learn from the design process (as in: actual designers) when it comes to analytics and data science. Among other things, that mindset highlights the importance of the analyst empathizing with stakeholders. Tim got very uncomfortable. Michael said he understood Tim's discomfort. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Text As Data: Combining qualitative and quantitative algorithms within the SAS system for accurate, effective and understandable text analytics The need for powerful, accurate and increasingly automatic text analysis software in modern information technology has dramatically increased. Fields as diverse as financial management, fraud and cybercrime prevention, Pharmaceutical R&D, social media marketing, customer care, and health services are implementing more comprehensive text-inclusive, analytics strategies. Text as Data: Computational Methods of Understanding Written Expression Using SAS presents an overview of text analytics and the critical role SAS software plays in combining linguistic and quantitative algorithms in the evolution of this dynamic field. Drawing on over two decades of experience in text analytics, authors Barry deVille and Gurpreet Singh Bawa examine the evolution of text mining and cloud-based solutions, and the development of SAS Visual Text Analytics. By integrating quantitative data and textual analysis with advanced computer learning principles, the authors demonstrate the combined advantages of SAS compared to standard approaches, and show how approaching text as qualitative data within a quantitative analytics framework produces more detailed, accurate, and explanatory results. Understand the role of linguistics, machine learning, and multiple data sources in the text analytics workflow Understand how a range of quantitative algorithms and data representations reflect contextual effects to shape meaning and understanding Access online data and code repositories, videos, tutorials, and case studies Learn how SAS extends quantitative algorithms to produce expanded text analytics capabilities Redefine text in terms of data for more accurate analysis This book offers a thorough introduction to the framework and dynamics of text analytics—and the underlying principles at work—and provides an in-depth examination of the interplay between qualitative-linguistic and quantitative, data-driven aspects of data analysis. The treatment begins with a discussion on expression parsing and detection and provides insight into the core principles and practices of text parsing, theme, and topic detection. It includes advanced topics such as contextual effects in numeric and textual data manipulation, fine-tuning text meaning and disambiguation. As the first resource to leverage the power of SAS for text analytics, Text as Data is an essential resource for SAS users and data scientists in any industry or academic application.

Mark, Ryan, and Cris discuss unemployment insurance benefits, Delta variant, and what happened this week in Washington, DC. The main topic is the long-term economic consequences of the pandemic. Also, Mark reveals his favorite movie. Full episode transcript can be found here.

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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Practical Data Science with Python guides you through the entire process of leveraging Python tools to analyze and gain insights from data. You'll start with foundational concepts and coding essentials, progressing through statistical analysis, machine learning techniques, and ethical considerations. What this Book will help me do Clean, prepare, and explore data using pandas and NumPy. Understand and implement machine learning models such as random forests and support vector machines. Perform statistical tests and analyze distributions to enhance data insights. Utilize SQL with Python for efficient data interaction. Generate automated reports and dashboards for data storytelling. Author(s) Nathan George has extensive professional experience as a data scientist and Python developer. He specializes in the application of machine learning and statistical methods to solve real-world problems. His writing combines technical depth with an approachable style, aiming to provide readers with actionable knowledge and skills. Who is it for? This book is perfect for data science beginners who have a basic understanding of Python and want to build practical data analysis skills. Students in analytics programs or professionals looking to transition into a data science role will find value in its approachable yet comprehensive coverage. Aspiring data analysts and career changers will gain firsthand exposure to Python-based data science best practices. If you're eager to develop practical, hands-on experience in the data science field, this is the guide for you.

This week we are joined by AoF alumni, David Dadoun to talk about data lakes, data oceans, data puddles, and data platforms, and why so many are confused about the topic. David is a leader, professor, global speaker, and recently transitioned to an exciting new role as Head of Enterprise Data and BI at BRP in Canada.  If you feel unsure about the definition of a data lake vs a data platform, you're not alone. The concept continues to evolve, to where we are today which is a data platform. As the owner and creators of multiple data platforms, David shared breaks down the key steps to transform your data lake into a data platform. Whether you're migrating to a more sophisticated data cloud or building a platform from scratch, the rapid pace of change means there's always something new to be learned. Tune in today for this fascinating conversation on how to master your data platform!   In this episode, you'll learn: [0:07:35] What the 'data lake' was and how it has evolved over time. [0:08:35] What is a data fake and how data lakes have evolved into data platforms. [0:12:51] Who needs to own the data platforms and who it's for. [0:14:41] How to run a data platform depending on the size and structure of your organization. [0:16:05] The different ways that companies can structure their data platform(s). [0:18:02] Why data literacy is crucial for any company with a data culture and how data lakes form part of the core strategy. [0:21:03] How to balance analytics and data goals within your company and teams. [0:24:20] The important steps a company can take towards creating a data lake. [0:28:13] Why it's necessary to be mindful of the rapid rate of change within data and how it will affect your data platforms. For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/83   Enjoyed the Show?  Please leave us a review on iTunes.

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years there has been an explosion of terms and methods born that automate and improve decision-making and operations.

Summary Python has beome the de facto language for working with data. That has brought with it a number of challenges having to do with the speed and scalability of working with large volumes of information.There have been many projects and strategies for overcoming these challenges, each with their own set of tradeoffs. In this episode Ehsan Totoni explains how he built the Bodo project to bring the speed and processing power of HPC techniques to the Python data ecosystem without requiring any re-work.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Struggling with broken pipelines? Stale dashboards? Missing data? If this resonates with you, you’re not alone. Data engineers struggling with unreliable data need look no further than Monte Carlo, the world’s first end-to-end, fully automated Data Observability Platform! In the same way that application performance monitoring ensures reliable software and keeps application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence, reducing time to detection and resolution from weeks or days to just minutes. Start trusting your data with Monte Carlo today! Visit dataengineeringpodcast.com/impact today to save your spot at IMPACT: The Data Observability Summit a half-day virtual event featuring the first U.S. Chief Data Scientist, founder of the Data Mesh, Creator of Apache Airflow, and more data pioneers spearheading some of the biggest movements in data. The first 50 to RSVP with this link will be entered to win an Oculus Quest 2 — Advanced All-In-One Virtual Reality Headset. RSVP today – you don’t want to miss it! Your host is Tobias Macey and today I’m interviewing Ehsan Totoni about Bodo, a system for automatically optimizing and parallelizing python code for massively parallel data processing and analytics

Interview

Introduction How did you get involved in the area of data management? Can you describe what Bodo is and the story behind it? What are the techniques/technologies that teams might use to optimize or scale out their data processing workflows? Why have you focused your efforts on the Python language and toolchain?

Do you see any potential for expanding into other language communities? What are the shortcomings of projects such as Dask and Ray for scaling out Python data projects?

Many people are familiar with the principle of HPC architectures, but can you share an overview of the current state of the art for HPC?

What are the tradeoffs of HPC vs scale-out distributed systems?

Can you d

Mark, Ryan, and Cris welcome back Bernard Yaros, an economist from Moody's Analytics to discuss fiscal policy, the odds of a government shutdown, and the debt limit. Full episode transcript can be found here.

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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Join host Avery Smith on this episode of the Data Career Podcast for an exciting 'Ask Avery' session! We cover various topics, including the roles and differences between data analysts, data engineers, and data scientists, as well as transitioning careers, essential skills for data engineering, forecasting techniques, and more.

f you have questions about data visualization, Python, or breaking into data science, this episode has got you covered.

Tune in for valuable insights and professional advice to boost your data career!

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

Brittany Bennett is Data Director at Sunrise Movement, the youth climate movement that numbers tens of thousands of members throughout every US state.  Given how quickly our industry moves, developing junior data talent is hard, but Brittany's team at Sunrise makes it look easy. And that's no accident—because Sunrise hires for mission alignment rather than technical background, they dedicate significant resources to training + mentorship. In this conversation, Tristan, Julia & Brittany dive deep into the opportunity of developing junior data practitioners. 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.