Avery talks with Michael Kennedy about the many ways Python is used.

Michael hosts the Talk Python to Me podcast, is an expert in Python, and explains how experts use Python in various fields.

The episode also discusses beginners who want to learn and use Python, including choosing an IDE and focusing on projects.

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Timestamps:

(11:18) - Python vs Other Programming Languages (17:15) - The Future of Python and Its Applications (32:06) - How the Rockband Weezer uses Python

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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 his new book, The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, Eric Siegel offers a detailed playbook for how business professionals can launch machine learning projects, providing both success stories where private industry got it right as well as cautionary tales others can learn from.

Siegel laid out the key findings of his book in our latest episode during a wide-ranging conversation with Marc Ruggiano, director of the University of Virginia’s Collaboratory for Applied Data Science in Business, and Michael Albert, an assistant professor of business administration at UVA's Darden School. The discussion, featuring three experts in business analytics, takes an in-depth look at the intersection of artificial intelligence, machine learning, business, and leadership.

http://www.bizML.com

https://www.darden.virginia.edu/faculty-research/centers-initiatives/data-analytics/bodily-professor

https://pubsonline.informs.org/do/10.1287/LYTX.2023.03.10/full/

https://www.kdnuggets.com/survey-machine-learning-projects-still-routinely-fail-to-deploy

CRISPDM: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining

CRM: https://en.wikipedia.org/wiki/Customer_relationship_management

We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production. In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate.  Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice. In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more.  Links Mentioned in the Show: The AI Playbook: Mastering the Rare Art of Machine Learning Deployment by Eric SiegelGenerating ROI with AIBizML Cheat SheetGooderSurvey: Machine Learning Projects Still Routinely Fail to Deploy[Skill Track] MLOps Fundamentals

Summary

Stream processing systems have long been built with a code-first design, adding SQL as a layer on top of the existing framework. RisingWave is a database engine that was created specifically for stream processing, with S3 as the storage layer. In this episode Yingjun Wu explains how it is architected to power analytical workflows on continuous data flows, and the challenges of making it responsive and scalable.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free! Your host is Tobias Macey and today I'm interviewing Yingjun Wu about the RisingWave database and the intricacies of building a stream processing engine on S3

Interview

Introduction How did you get involved in the area of data management? Can you describe what RisingWave is and the story behind it? There are numerous stream processing engines, near-real-time database engines, streaming SQL systems, etc. What is the specific niche that RisingWave addresses?

What are some of the platforms/architectures that teams are replacing with RisingWave?

What are some of the unique capabilities/use cases that RisingWave provides over other offerings in the current ecosystem? Can you describe how RisingWave is architected and implemented?

How have the design and goals/scope changed since you first started working on it? What are the core design philosophies that you rely on to prioritize the ongoing development of the project?

What are the most complex engineering challenges that you have had to address in the creation of RisingWave? Can you describe a typical workflow for teams that are building on top of RisingWave?

What are the user/developer experience elements that you have prioritized most highly?

What are the situations where RisingWave can/should be a system of record vs. a point-in-time view of data in transit, with a data warehouse/lakehouse as the longitudinal storage and query engine? What are the most interesting, innovative, or unexpected ways that you have seen RisingWave used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on RisingWave? When is RisingWave the wrong choice? What do you have planned for the future of RisingWave?

Contact Info

yingjunwu on GitHub Personal Website LinkedIn

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 shows.

Dante joins the podcast to break down the January employment report. Fitting for Groundhog’s Day, the jobs report delivered an eerily similar upside surprise to what we saw in January 2023. Following the January meeting of the FOMC this week, the team discusses what the Fed is likely to do in light of recent data. To access the 2024 Election Model Whitepaper click here  Follow Mark Zandi @MarkZandi, Cris deRitis @MiddleWayEcon, and Marisa DiNatale on LinkedIn for additional insight.

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 this episode, we discuss City AI Connect, a global learning community and digital platform for cities to trial and advance the usage of generative artificial intelligence to improve public services.

--- Generative AI, powered by advanced machine learning algorithms, has the potential to analyze vast amounts of data to predict trends, helping cities improve emergency response, mitigate severe weather events, and target resources for infrastructure enhancements. The technologies might also be harnessed to design creative solutions that could transform government delivery by reducing processing delays, eliminating cumbersome paperwork, and expanding multi-language access to reach many more residents with vital, public services.

--- To maximize the potential and expand the availability of generative artificial intelligence learning for local governments, City AI Connect might offer locals officials a single destination to ideate, develop, and test new utilizations with peers across cities. Through social networking features, digital forums, virtual events, and a repository of blueprints and resources, city leaders might have the opportunity to exchange strategies and work with data and technology experts brought together by Bloomberg Philanthropies and the Center for Government Excellence at Johns Hopkins University to accelerate implementation in their city halls.

--- We're joined by Beth Blauer, Associate Vice Provost for Public Sector Innovation at Johns Hopkins University and the founder of GovEx; Mary Conway Vaughan, Deputy Director of Research and Analytics at GovEx; and Denise Reidl, the Chief Innovation Officer for the City of South Bend, Indiana.

--- City AI Connect --- "Gen AI: Get Ready!" Webinar (City AI Connect Members Only) --- Fill out our listener survey!

We don’t think about every decision we make. Some decisions are easy and intuitive, others can be riddled with doubt. In a business setting, decision-making is often crucial, and with that comes pressure to ensure we’re making the right decisions in the best way possible. We can often accompany decision-making with context, providing a narrative for how we might approach a decision, citing what data and insights have had significant input into our choices. But how do we approach storytelling and decision-making to breed success? There’s probably no better person to guide us through the ins and outs of decision-making than the co-author of Business Storytelling For Dummies. Lori L. Silverman is the owner of Partners for Progress, a management consulting firm. As a business strategist, she has consulted with organizations in fifteen industries including financial services, insurance, manufacturing and petroleum companies, government entities, and professional associations. As a keynote speaker, Lori has positively impacted the lives of thousands of people. She has appeared on over fifty radio and television shows to speak about using stories in the workplace and is the co-author of Critical SHIFT and Stories Trainers Tell.  She’s a pioneer in the business storytelling field, author of five books, and is known worldwide for her work in collaborative data-informed decision-making. In the episode, Richie and Lori cover common problems in business decision-making, connecting decision-making to business processes, analytics and decision-making, integrating data practitioners and decision-makers, the role of data visualization and narrative storytelling, the SMARTER decision-making methodology, the importance of intuition, challenges faced when applying decision-making methodologies and much more.  Links Mentioned in the Show Business Storytelling For Dummies by Karen Dietz and Lori SilvermanConnect with Lori on LinkedinLevel Up with LoriBooks by LoriThe SMARTER Framework for Data-Informed Decision MakingMonetizing Data Through Informed, Collaborative Decision MakingThe Increasingly Vital Role of Business Storytelling in LeadershipPre-Suasion: A Revolutionary Way to Influence and Persuade by Robert Cialdini[Skill Track] Data Storytelling

In this presentation, we’ll explore the parallels between the worlds of poker and data-driven marketing. Both realms require a strategic approach to decision-making in environments filled with uncertainty and imperfect information. Focus on embracing imperfection and leveraging available data to make informed choices, much like a seasoned poker player analyzes their hand and the table dynamics.

Building upon her 25-year career in analytics, June has recently added “angel investing” to her list of professional experiences. In this talk, she will share stories and insights describing what she’s learned as an analytics investor, including how she got her start, what it’s like to trend-spot and evaluate new tech, a reflection on learnings from the past two years, and a look ahead at promising new advancements.

We all know that data, like wine and cheese, becomes more valuable when combined. And, just like wine and cheese, they can lead to serious headaches. Whether you are emailing Excel files around, capturing data from thousands of IoT-devices, or just joining your Google Analytics and sales data, you can benefit from following a structured process to minimize your headaches. After debugging yet another failed pipeline I have distilled my experience of building data ingestion pipelines in 8 simple (though not necessarily easy) steps from setting up triggers to archiving and retention.

Peter joined Decathlon as their first Digital Analytics leader in August 2023. His remit/challenge was quite simple, to increase the use of, and impact from, Digital Analytics. After his first three months, he created a plan, his to do list to improve the situation in the coming months and years. But talk is cheap, what matters is the actions actually taken. Peter will talk you through what he uncovered at Decathlon, what his plan is and how well that plan has survived 3 months in when faced with reality.

Google Professional Services is a team of technical consultants and engineers, many of whom have been heavily involved in GA4 migration projects over the last years.

Over the years it has become clear that communication is EVERYTHING! Even in the world of technical analytics, we cannot be successful unless we get this right.

How taking advantages of the self-service data viz software of Google for demanding requirements, as part of various processes and premium digital analytics devices.

Often in analytics and data science we have the 'big table' mental picture of data where we are continuously trying to append and link new bits of data back to each customer. The issue is that using approaches that follow this model often don't really follow a privacy by default design - rather this is more of an identify by default approach.

Leading an analytics team is a complex challenge, especially when it comes to acquiring and retaining the ideal digital analyst. Addressing this issue led Elena Nesi and Fosca on a research quest focused on talent retention strategies in the analytics realm - gathering valuable insights from colleagues, conducting team retrospectives and feedback surveys, and analyzing broader trends related to employee retention and departures. The goal? Understanding why individuals leave their roles and how teams can better retain valuable talent.

Those factors combined resulted in a slate of new opportunities and challenges for digital analytics vendors. What strategies did they apply? Is there a winner? And how will all of this shape their offerings in 2024?

Demonstrate any Digital Analytics solutions or method of your own that is way beyond the defaults. Who decides who's gonna win? The audience. Send your nomination to [email protected]!

• JENN KUNZ / 33Sticks - Kennesaw (GA), USA + KATRIN RIBANT / Ask-Y - New York (NY), USA + RICK DRONKERS / Data to Value - Randstad, Netherlands + JULIUS FEDOROVICIUS / Analytics Mania - Vilnius, Lithuania