In this episode, I talk with Andreas Kretz (https://www.linkedin.com/in/andreas-kretz/) who is an amazing resource for the data engineering community. He runs an incredibly affordable data engineering bootcamp called Learn Data Engineering (https://learndataengineering.com) and also has an extensive YouTube (https://www.youtube.com/channel/UCY8mzqqGwl5_bTpBY9qLMAA). 

We talked about how Andreas got started with data engineering, why he like it so much, and how others can get started. I also share my story of interviewing with Facebook for a data engineering position. 

Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com

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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 DataFramed, Adel speaks with Sudaman Thoppan Mohanchandralal, Regional Chief Data, and Analytics Officer at Allianz Benelux, on the importance of building data cultures and his experiences operationalizing data culture transformation programs.Throughout the episode, Sudaman talks about his background, the Chief Data Officer’s mandate and how it has evolved over the years, how organizations should prioritize building data cultures, the science behind culture change, the importance of executive data literacy when scaling value from data, and more.

Relevant links from the interview:

Connect with Sudaman on LinkedInCheck out Sudaman’s Webinar on DataCampWhy Data Culture Matters

Jamie is a data advocate with a strong belief in the transformative potential of data. She is the founder and CEO of Open Data Australia and the regional director for FDATA Australasia and an advisor on digital identity to the United Nations Capital Development Fund. Jamie is the go-to person for knowledge and insights on the topics of data privacy, governance, strategy, policy and regulation. She has a vision for how data can be used to improve the lives and financial outcomes of everyday citizens In this episode of Leaders of Analytics we discuss the huge potential for data innovation stemming from the Consumer Data Right and Open Banking, the hurdles that must be overcome by participating as well as who will be the winners and losers from the data sharing revolution. In this episode you will find: An overview of the Consumer Data Right and what it means to consumers and participating organisations?How CDR differs from GDPRHow far participating organisations are in implementing the various components of Open Banking what should we expect to see in this space in the next 12-24 monthsThe most obvious use cases for CDR, and Open Banking in particularThe most important use cases that CDR/Open Banking participants should be focusing onInternational examples of successful Open Banking based products and servicesThe hurdles currently limiting the use of the data sharing environment that CDR and Open Banking facilitatesHow to generate consumer trust and excitement around CDR and Open BankingWhat impacts CDR will have across the wider economy in the futureWho will be the future winners and losers from CDR, Open Banking and a broader Open Data regime

Summary Every data project, whether it’s analytics, machine learning, or AI, starts with the work of data cleaning. This is a critical step and benefits from being accessible to the domain experts. Trifacta is a platform for managing your data engineering workflow to make curating, cleaning, and preparing your information more approachable for everyone in the business. In this episode CEO Adam Wilson shares the story behind the business, discusses the myriad ways that data wrangling is performed across the business, and how the platform is architected to adapt to the ever-changing landscape of data management tools. This is a great conversation about how deliberate user experience and platform design can make a drastic difference in the amount of value that a business can provide to their customers.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy! 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! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. 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 Adam Wilson about Trifacta, a platform for modern data workers to assess quality, transform, and automate data pipelines

Interview

Introduction How did you get involved in the area of data management? Can you describe what Trifacta is and the story behind it? Across your site and material you focus on using the term "data wrangling". What is your personal definition of that term, and in what ways do you differentiate from ETL/ELT?

How does the deliberate use of that terminology influence the way that you think about the design and features of the Trifacta platform?

What is Trifacta’s role in the overall data platform/data lifecycle for an organization?

What are some examples of tools that Trifacta might replace? What tools or systems does Trifacta integrate with?

Who are the target end-users of the Trifacta platform and how do those personas direct the design and functionality? Can you describe how Trifacta is architected?

How have the goals and design of the system changed or evolved since you first began working on it?

Can you talk through the workflow and lifecycle of data as it traverses your platform, and the user interactions that drive it? How can data engineers share and encourage proper patterns for working with data assets with end-users across the organization? What are the limits of scale for volume and complexity of data assets that users are able to manage through Trifacta’s visual tools?

What are some strategies that you and your customers have found useful for pre-processing the information that enters your platform to increase the accessibility for end-users to self-serve?

What are the most interesting, innovative, or unexpected ways that you have seen Trifacta used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Trifacata? When is Trifacta the wrong choice? What do you have planned for the future of Trifacta?

Contact Info

LinkedIn @a_adam_wilson on Twitter

Parting 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 show, Podcast.init to learn about the Python language, its community, and the innovative ways it is being used. 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. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat

Links

Trifacta Informatica UC Berkeley Stanford University Citadel

Podcast Episode

Stanford Data Wrangler DBT

Podcast Episode

Pig Databricks Sqoop Flume SPSS Tableau SDLC == Software Delivery Life-Cycle

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Support Data Engineering Podcast

Wayne Best, Chief Economist of Visa, joins Mark Zandi and the Moody's Analytics team to discuss the labor market, housing, and Visa's spending momentum index. 

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, I talk to Matt Blasa (https://www.linkedin.com/in/mblasa/) about how he does data science freelancing. We also talk about online portfolios, data governance, and why he posts on LinkedIn. Enjoy!

🎙 PLEASE FOLLOW & SUBSCRIBE TO THE POD 

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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

Data science and analytics have emerged as the most desired fields in driving business decisions. Using the techniques and methods of data science, decision makers can uncover hidden patterns in their data, develop algorithms and models that help improve processes and make key business decisions. Data science is a data driven decision making approach that uses several different areas and disciplines with a purpose of extracting insights and knowledge from structured and unstructured data. The algorithms and models of data science along with machine learning and predictive modeling are widely used in solving business problems and predicting future outcomes. This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.

Collect, analyze and leverage people data for improved business performance.

Robert Chang is a product manager for the data platform at Airbnb, where he helped build and roll out Minerva, Airbnb's internal metrics store. They use Minerva to track over 12,000(!) metrics and 4,000(!) dimensions with consistency across the organization. In this conversation with Tristan and Julia, Robert dives into why they built it, what it took to get it done—and crucially, what you should do if your company doesn't have the resources to build your own internal metrics store. 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.

Matt Francsis (https://www.linkedin.com/in/matthewfrancsis/) joined the show today and talked about his journey from geology to data science. He talked about how a data science bootcamp and a "stepping-stone" job that utilized his geology background, ended up helping him break into the field completely. 

Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com

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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

Considering that the role of Analytics Engineering has emerged in the last few years within data and analytics teams, it is important for me to highlight what role an Analytics engineer has and how the Dos and Don’ts from my perspective can contribute to a team and boost their day-to-day work with the help of Airflow.

In this talk we’ll see some real world examples from Firebolt customers demonstrating how Airflow is used to orchestrate operational data analytics applications with large data volumes, while keeping query latency low.

Engineering teams leverage the factory coding pattern to write easy-to-read and repeatable code. In this talk, we’ll outline how data engineering teams can do the same with Airflow by separating DAG declarations from business logic, abstracting task declarations from task dependencies, and creating a code architecture that is simple to understand for new team members. This approach will set analytics teams up for success as team and Airflow DAG sizes grow exponentially.

Flow Immersive is the next generation of data visualization and storytelling. Sign up to be on the waitlist: https://flowimmersive.com/signup

Follow Michael DiBenigno on TikTok: https://www.tiktok.com/@the.data.guy?lang=en

Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com

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Subscribe on YouTube: https://www.youtube.com/channel/UCuyfszBAd3gUt9vAbC1dfqA

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

Mark Zandi and the Moody's Analytics team discuss what they are watching to gauge the health of the economy and the big topic was froth in asset markets.   

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.

Data fabric is a hot concept in data management today. By encompassing the data ecosystem your company already has in place, this architectural design pattern provides your staff with one reliable place to go for data. In this report, author Alice LaPlante shows CIOs, CDOs, and CAOs how data fabric enables their users to spend more time analyzing than wrangling data. The best way to thrive during this intense period of digital transformation is through data. But after roaring through 2019, progress on getting the most out of data investments has lost steam. Only 38% of companies now say they've created a data-driven organization. This report describes how a data fabric can help you reach the all-important goal of data democratization. Learn how data fabric handles data prep, data delivery, and serves as a data catalog Use data fabric to handle data variety, a top challenge for many organizations Learn how data fabric spans any environment to support data for users and use cases from any source Examine data fabric's capabilities including data and metadata management, data quality, integration, analytics, visualization, and governance Get five pieces of advice for getting started with data fabric

In a crowded field of data visualization and analytics tools, Tableau Desktop has emerged as the clear leader. This is partly due to its ease of use, but once you dive into Tableau's extensive feature set, you'll understand just how powerful and flexible this software can be for your business or organization. With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information. Tableau has done an amazing job of making valuable insights accessible to analysts and executives who would otherwise need to rely on IT. This book quickly guides you through Tableau Desktop's learning curve. You'll learn: How to shape data for use with Tableau Desktop How to create the most effective chart types Core concepts including discrete versus continuous Must-know technical features including filters, parameters, and sets Key syntax for creating the most useful analyses How to bring it all together with dashboardsAnd more!

Here are some of the topics we covered in this episode… warning, we covered quite a bit.

Data books recommendations, an update on my complete data science bootcamp, and what background is best for data science (spoiler: all backgrounds are welcome), is data getting oversaturated, and how to get a job with no experience.

With that being said, we are at 742 downloads. I’m really excited to hit 1,000. If you could just pause for 30 seconds and think of anyone you know that could benefit from this podcast, and share it with them, I would love you forever.

Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com

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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 Ask Avery Show, we talk about all things data science careers. We talk about Data Career Jumpstart which is coming out soon, you can sign up for updates at DataCareerJumpstart.com.

We tackle Automation vs Data Science, unpaid internships, how to prepare for data interviews, how to start a data science project, how to make a data project portfolio, and more!

Want to break into data science? Check out my new course coming out later this summer: Data Career Jumpstart - https://www.datacareerjumpstart.com

Want to leave a question for the Ask Avery Show?

Written Mailbag: https://forms.gle/78zD544drpDAcTRV9

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Want to be on The Ask Avery Show? Sign up for a spot here:

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Subscribe on YouTube: https://www.youtube.com/channel/UCuyfszBAd3gUt9vAbC1dfqA

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

Mark Zandi and the Moody's Analytics team discuss the latest indicators, follow up on one of their first podcast topics - inflation, and discuss their individual models.

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.