Stop for a minute and think about the highest stakes campaign or test you've ever run. Were you nervous? Now, instead, imagine that you're on an aircraft carrier with a few thousand people on board whose safety you are responsible for, and your team is about to watch 40,000 tons of ordnance detonate (in an environmentally friendly way) right next to the ship... so you can collect data to verify that the various systems are working as expected. On this episode, our guest can't really talk about the former situation, but he can discuss the latter in depth: Capt. Paul Lanzilotta is the commanding officer of the USS Gerald R. Ford, the lead ship in the latest class of U.S. Navy aircraft carriers. Perspective, much? For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary Building, scaling, and maintaining the operational components of a machine learning workflow are all hard problems. Add the work of creating the model itself, and it’s not surprising that a majority of companies that could greatly benefit from machine learning have yet to either put it into production or see the value. Tristan Zajonc recognized the complexity that acts as a barrier to adoption and created the Continual platform in response. In this episode he shares his perspective on the benefits of declarative machine learning workflows as a means of accelerating adoption in businesses that don’t have the time, money, or ambition to build everything from scratch. He also discusses the technical underpinnings of what he is building and how using the data warehouse as a shared resource drastically shortens the time required to see value. This is a fascinating episode and Tristan’s work at Continual is likely to be the catalyst for a new stage in the machine learning community.

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! Schema changes, missing data, and volume anomalies caused by your data sources can happen without any advanced notice if you lack visibility into your data-in-motion. That leaves DataOps reactive to data quality issues and can make your consumers lose confidence in your data. By connecting to your pipeline orchestrator like Apache Airflow and centralizing your end-to-end metadata, Databand.ai lets you identify data quality issues and their root causes from a single dashboard. With Databand.ai, you’ll know whether the data moving from your sources to your warehouse will be available, accurate, and usable when it arrives. Go to dataengineeringpodcast.com/databand to sign up for a free 30-day trial of Databand.ai and take control of your data quality today. 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 Your host is Tobias Macey and today I’m interviewing Tristan Zajonc about Continual, a platform for automating the creation and application of operational AI on top of your data warehouse

Interview

Introduction How did you get involved in the area of data management? Can you describe what Continual is and the story behind it?

What is your definition for "operational AI" and how does it differ from other applications of ML/AI?

What are some example use cases for AI in an operational capacity?

What are the barriers to adoption for organizations that want to take advantage of predictive analytics?

Who are the target users of Continual? Can you describe how the Continual platform is implemented?

How has the design and infrastructure changed or evolved since you first began working on it?

What is the workflow for

Digitally connected humans like you and me are surrounded by a plethora of AI solutions that make our lives easier and more efficient. Just think about the algorithms driving Netflix and Youtube’s video recommendations or the facial recognition feature on your phone that saves you a few seconds every time you unlock it. But for every useful AI solution, there are probably hundreds of solutions that don’t meet the functional, economic or ethical standards of their end users. So, what’s the trick to building useful and impactful AI solutions that are also financially viable for those who create them? Someone who can answer this question is Corey Quinn, who is the Chief Cloud Economist at The Duckbill Group and the founder of two podcasts called “Screaming in the Cloud” and “AWS Morning Brief”. Corey combines an excellent sense of humour with a deep understanding of the cloud and everything that surrounds it, so he is definitely the right person to go to for an unfiltered view of the hype that surrounds a lot of AI solutions. In this episode of Leaders of Analytics, we talk about: Whether AI is all it’s made up to be or just a complex solution to our problemsWho’s benefiting from the AI hypeThe role of cloud computing in AI and machine learning deliveryHow to use cloud computing effectively when deploying AI solutionsHow to create an impactful career by solving real business problemsCorey’s top 3 recommendations for AI success in the cloud

Tim Uy, Todd Metcalfe, and Jesse Rogers, all economists at Moody's Analytics, join Mark and Ryan to discuss global supply chains and commodity shortages. The focus is on issuances around semiconductors, lumber, and copper. Full transcript can be viewed 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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Azure Databricks is a robust analytics platform that leverages Apache Spark and seamlessly integrates with Azure services. In the Azure Databricks Cookbook, you'll find hands-on recipes to ingest data, build modern data pipelines, and perform real-time analytics while learning to optimize and secure your solutions. What this Book will help me do Design advanced data workflows integrating Azure Synapse, Cosmos DB, and streaming sources with Databricks. Gain proficiency in using Delta Tables and Spark for efficient data storage and analysis. Learn to create, deploy, and manage real-time dashboards with Databricks SQL. Master CI/CD pipelines for automating deployments of Databricks solutions. Understand security best practices for restricting access and monitoring Azure Databricks. Author(s) None Raj and None Jaiswal are experienced professionals in the field of big data and analytics. They are well-versed in implementing Azure Databricks solutions for real-world problems. Their collaborative writing approach ensures clarity and practical focus. Who is it for? This book is tailored for data engineers, scientists, and big data professionals who want to apply Azure Databricks and Apache Spark to their analytics workflows. A basic familiarity with Spark and Azure is recommended to make the best use of the recipes provided. If you're looking to scale and optimize your analytics pipelines, this book is for you.

Emily Vu changed her life with a Tweet she thought meant nothing. That tweet ended up being seen by millions, inspiring hundreds, opening dozens of doors, and eventually landed to an internship and full-time job offer from tech giant, Spotify.

In this episode, Emily and I discuss how she used the internet and a personal brand to overcome a non-tech background, to land awesome tech jobs. 

Follow Emily on Twitter: https://twitter.com/emvutweets

Follow Emily on TikTok: https://www.tiktok.com/@itsemvu?lang=en

Check out Emily's custom resume Kofi: https://ko-fi.com/emilyvu/commissions#buyCommissionModal

Want to break into data science? Check out online bootcamp Data Career Jumpstart - https://www.datacareerjumpstart.com - where I help you learn data science, and build a personal brand by focusing on projects and building an online portfolio. 

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

Jump-start your career as a data scientist—l earn to develop datasets for exploration, analysis, and machine learning SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis is a resource that’s dedicated to the Structured Query Language (SQL) and dataset design skills that data scientists use most. Aspiring data scientists will learn how to how to construct datasets for exploration, analysis, and machine learning. You can also discover how to approach query design and develop SQL code to extract data insights while avoiding common pitfalls. You may be one of many people who are entering the field of Data Science from a range of professions and educational backgrounds, such as business analytics, social science, physics, economics, and computer science. Like many of them, you may have conducted analyses using spreadsheets as data sources, but never retrieved and engineered datasets from a relational database using SQL, which is a programming language designed for managing databases and extracting data. This guide for data scientists differs from other instructional guides on the subject. It doesn’t cover SQL broadly. Instead, you’ll learn the subset of SQL skills that data analysts and data scientists use frequently. You’ll also gain practical advice and direction on "how to think about constructing your dataset." Gain an understanding of relational database structure, query design, and SQL syntax Develop queries to construct datasets for use in applications like interactive reports and machine learning algorithms Review strategies and approaches so you can design analytical datasets Practice your techniques with the provided database and SQL code In this book, author Renee Teate shares knowledge gained during a 15-year career working with data, in roles ranging from database developer to data analyst to data scientist. She guides you through SQL code and dataset design concepts from an industry practitioner’s perspective, moving your data scientist career forward!

Aaron Klein, Senior Fellow in economic studies at Brookings Institute, joins Mark, Cris, and Ryan to discuss the current state and future of crypto currencies. Recommended Reads: Natural Disasters From Coast to Coast

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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Caitlin Colgrove is Co-founder & CTO at Hex, a data workspace that allows teams to collaborate in both SQL and Python to publish interactive data apps. In this conversation, Tristan, Julia and Caitlin dive into the possibilities that real-time collaborative notebooks unlock for data teams — what if our collaboration style looked more like Google Docs than a Git workflow? 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.

Summary Gartner analysts are tasked with identifying promising companies each year that are making an impact in their respective categories. For businesses that are working in the data management and analytics space they recognized the efforts of Timbr.ai, Soda Data, Nexla, and Tada. In this episode the founders and leaders of each of these organizations share their perspective on the current state of the market, and the challenges facing businesses and data professionals today.

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 Have you ever had to develop ad-hoc solutions for security, privacy, and compliance requirements? Are you spending too much of your engineering resources on creating database views, configuring database permissions, and manually granting and revoking access to sensitive data? Satori has built the first DataSecOps Platform that streamlines data access and security. Satori’s DataSecOps automates data access controls, permissions, and masking for all major data platforms such as Snowflake, Redshift and SQL Server and even delegates data access management to business users, helping you move your organization from default data access to need-to-know access. Go to dataengineeringpodcast.com/satori today and get a $5K credit for your next Satori subscription. Your host is Tobias Macey and today I’m interviewing Saket Saurabh, Maarten Masschelein, Akshay Deshpande, and Dan Weitzner about the challenges facing data practitioners today and the solutions that are being brought to market for addressing them, as well as the work they are doing that got them recognized as "cool vendors" by Gartner.

Interview

Introduction How did you get involved in the area of data management? Can you each describe what you view as the biggest challenge facing data professionals? Who are you building your solutions for and what are the most common data management problems are you all solving? What are different components of Data Management and why is it so complex? What will simplify this process, if any? The report covers a lot of new data management terminology – data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps – what does this all mean and why is it important for data engineers? How has the data management space changed in recent times? Describe the current data management landscape and any key developments. From your perspective, what are the biggest challenges in the data management space today? What modern data management features are lacking in existing databases? Gartner imagines a future where data and analytics leaders need to be prepared to rely on data manage

Send us a text 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.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Elo Umeh, from Terragon Africa’s fastest-growing enterprise marketing technology company. Terragon uses its on-demand marketing cloud platform, attribution software, and deep analytics capability to enable thoughtful, targeted omni-channel access to 100m+ mobile-first African consumers. Elo is the Founder and CEO at Terragon Group. Elo career has spanned over 15 years where he has worked in the mobile and digital media across East and West Africa. He was part of the founding team at Mtech Communications. Elo holds a global executive MBA from IESE business of school where he graduated at the top of his class. Elo also has a Bachelor’s degree in Business Administration from Lagos State University. Show Notes 4:02 – What keeps you going? 6:15 – Lets dive into Terragon 8:40 – Who are your customers? 11:06 – Define pre-paid 14:40 – What kind of incites and security are you providing? 20:37- What kind of technology is Terragon using? 23:16 – What was it about the smart phone that made you want to go out on your own? 26:10 – Who’s your biggest competitor?  28:20 – What’s next for Terragon? 31:01 – What are the biggest mistakes entrepreneurs make? Terragon  Elo Umeh - LinkedIn

Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  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.

Ryan Wade joins us on AOF today to talk about how to use advanced analytics in your organization! Ryan has been in the analytics game for the last 20 years and is now a Senior Solution Consultant at Blue Granite, based in Indianapolis, Indiana. He recently authored the amazing must-read book, Advanced Analytics in Power BI with R and Python, and in today's chat, we get to hear all about why he wrote the book, who it is for and how you can use it to accelerate your data journey! I met Ryan while speaking at a few conferences and was always impressed with his knowledge and great sense of humor! A professional football player turned data scientist, Ryan has a passion for breaking down advanced analytics in a way anyone can understand. Whether you're already using advanced analytics or researching how to get started Ryan's knowledge on the topic will help you. Tune in with a pencil and paper in hand!   In this episode, you'll learn: [0:09:22] The rise of the R and Python programming languages in the data world. [0:16:44] The necessary, well-thought-out preparatory steps for a project utilizing advanced analytics. [0:19:39] Why attention-grabbing visuals are not the most important part of data storytelling! [0:23:13] Creating a sufficient team for data analytics and the vital roles of the database administrator, active directory administrator, and more! [0:39:07] Client conversations around shortcomings and hurdles in advanced analytics.  For full show notes, and the links mentioned visit: https://bibrainz.com/podcast/82   Enjoyed the Show?  Please leave us a review on iTunes.

PEEK “UNDER THE HOOD” OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems