🎙️ Speakers: Thomas Wiecki, Niall Oulton, Tim McWilliams, Carlos Trujillo, Kemble Fletcher, Evan Wimpey \| ⏰ Time: 16:00 UTC / 9:00 AM PT / 12:00 PM ET / 5:00 PM Berlin

Marketing measurement is evolving faster than most teams can keep up, and 2025 pushed every model, method, and assumption to its limits. With shifting budgets, new privacy pressures, and a market full of hype disguised as innovation, the real question is: what actually worked?

In this session, the PyMC Labs team opens the curtain on what we learned from working hands-on with some of the world’s leading brands, across MMM, CLV, forecasting, causal inference, generative AI, and fully custom Bayesian models. Instead of polished slides or scripted talking points, this roundtable is a guided, honest conversation about what this year revealed, and what 2026 will demand from marketing leaders.

Drawing from dozens of real client engagements, model builds, and experiments, you’ll see how our team approached this year’s hardest measurement problems, where the industry is heading, and how to think more clearly about marketing effectiveness in a chaotic environment.

You’ll learn:

• The biggest challenges brands faced in MMM, CLV, forecasting, and attribution in 2025.
• The breakthroughs that actually moved the needle, and the ones that didn’t.
• What turned out to be genuine measurement trends vs. overhyped noise.
• How Bayesian modeling helped solve problems traditional approaches couldn’t.
• The solutions, playbooks, and ideas our team used to drive meaningful decisions.
• What 2026 will require from marketing measurement, experimentation, and strategy.

Join us for a sharp, candid, and practitioner-led discussion that surfaces the lessons, surprises, and strategies shaping smarter marketing decisions, not theory, but what we’ve seen in the trenches.

📜 Outline of Talk / Agenda:

• 5 min: Introduction to PyMC Labs and speakers
• 40 min: Panel discussion
• 15 min: Q&A

💼 About the speakers: Thomas Wiecki (Founder of PyMC Labs) Dr. Thomas Wiecki is an author of PyMC, the leading platform for statistical data science. To help businesses solve some of their trickiest data science problems, he assembled a world-class team of Bayesian modelers and founded PyMC Labs -- the Bayesian consultancy. He did his PhD at Brown University studying cognitive neuroscience. 🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki

Niall Oulton (Vice President of Sales - PyMC Labs) Niall Oulton has built a reputation as a leading expert in the field of marketing analytics, with a specialization in Bayesian Marketing Mix Modelling. His career, spanning over a decade, has seen him on both sides of the business landscape - agency and client. His rich background provides him with a unique perspective, making him an expert in understanding and navigating the complexities of both worlds.

🔗 Connect with Niall: 👉 LinkedIn: https://www.linkedin.com/in/nialloulton20/ 👉 Twitter: https://twitter.com/niall20 👉 GitHub: https://github.com/nialloulton 👉 Website: https://1749.io/

Tim McWilliams (Principal Data Scientist - PyMC Labs) With over 7 years of experience in the marketing mix modeling and marketing analytics space, Tim specializes in applying Bayesian modeling techniques to solve complex business challenges and uncover actionable insights. Passionate about bridging advanced statistical methods with real-world marketing strategy, he has worked across diverse industries to optimize media investments and measure impact.

🔗 Connect with Tim: 👉 LinkedIn: https://www.linkedin.com/in/tim-mcwilliams-a4b647b3/ 👉 Github: https://github.com/timbo112711

Kemble Fletcher (Director of Product Development - PyMC Labs) Before joining PyMC Labs, Kemble co-founded SweepLift and co-invented its patent-pending in-stream survey and measurement technology. He later led omnichannel attribution and measurement strategy at Google for its top 300 global clients, influencing $2B in ARR. Prior to that, he drove digital analytics and predictive modeling at OMD for brands like Levi’s, Hilton, and eHarmony. He also advises SaaS and start-up leaders on data architecture, attribution, and growth. At PyMC Labs, Kemble helps organizations solve complex challenges through advanced Bayesian modeling.

🔗 Connect with Kemble: 👉 LinkedIn: https://www.linkedin.com/in/kemblefletcher/

Carlos Trujillo (Principal Data Scientist - PyMC Labs) Carlos is a Marketing Scientist passionate about using data and AI to turn marketing strategy into measurable results. He’s worked with teams across Latin America, Europe, and Africa, including roles at Wise, Bolt, and Omnicom Media Group. As a core member of PyMC Labs, he contributes to open-source projects like PyMC-Marketing, blending statistical rigor with practical marketing insight.

🔗 Connect with Carlos: 👉 LinkedIn: https://www.linkedin.com/in/cetagostini/ 👉 Github: https://github.com/cetagostini

💼 About the Host: Evan Wimpey (Director of Analytics at PyMC Labs) Evan helps clients design Bayesian solutions tailored to their goals, ensuring they understand both the how and why of inference. With master’s degrees in Economics and Analytics, he focuses on delivering clear value throughout projects and brings a unique twist with his background in data comedy. 🔗 Connect with Evan: 👉 Linkedin: https://www.linkedin.com/in/evan-wimpey/ 👉 GitHub: https://github.com/ewimpey

📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/ 🎮 Discord: https://discord.gg/mTc64cAz

In this episode, we explore a high-tech twist on developmental toxicology. Researchers have combined microfluidic engineering with machine learning to automate the analysis of thousands of C. elegans for chemical toxicity testing — no anaesthetics or low-res imaging required.

Using the vivoChip device and a custom ML model called vivoBodySeg, the team:

Captures 3D images of ~1000 worms from 24 populations at once Achieves near-human segmentation accuracy (Dice score: 97.8%) Measures subtle toxicity effects like changes in body size and gut autofluorescence Identifies EC10 and LOAEL values with high precision Uses few-shot learning to adapt the model to new worm shapes and sizes

This platform slashes analysis time by 140× and sets a new benchmark for high-throughput New Approach Methodologies (NAMs) in toxicology.

📖 Based on the research article: “Machine learning-based analysis of microfluidic device immobilised C. elegans for automated developmental toxicity testing” Andrew DuPlissis, Abhishri Medewar, Evan Hegarty, et al. Published in Scientific Reports (2025) 🔗 https://doi.org/10.1038/s41598-024-84842-x

🎧 Subscribe to the WOrM Podcast for more stories where whole-organism biology meets cutting-edge tech!

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]

Let's fix ChatGPT's greatest design sins. We'll design and build a working app that makes ChatGPT multi-modal and multi-model. And no, you don't need to know what those words mean to use it.

Download the source code: https://github.com/bholmesdev/fixgpt

References from this video: - Try https://warp.dev to vibe code your own solution - Watch Scott and Mark's podcast episode, "how to not ship the org chart:" https://www.youtube.com/watch?v=Z1yYcUFzH2A - Read "Why is AI marketing so, so bad?" by Evan Armstrong at The Leverage: https://www.gettheleverage.com/p/why-is-ai-marketing-so-so-bad

In this session, learn how Semantic Kernel enhances business processes with AI. Discover the latest innovations that empower employees and customers to complete tasks efficiently and accurately. The product team will showcase demos of their new framework in action, explaining how you can implement this technology in your apps, and you will hear from customers who are leveraging AI today to gain a competitive edge.

𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀: * Urko Benito * Evan Chaki * Greg Crist * Shawn Henry * Wanting Huang * Hiro Kobashi

𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: This is one of many sessions from the Microsoft Ignite 2024 event. View even more sessions on-demand and learn about Microsoft Ignite at https://ignite.microsoft.com

BRK126 | English (US) | AI

MSIgnite

Guess what? Data science and AI initiatives are still failing here in 2024—despite widespread awareness. Is that news? Candidly, you’ll hear me share with Evan Shellshear—author of the new book Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics—about how much I actually didn’t want to talk about this story originally on my podcast—because it’s not news! However, what is news is what the data says behind Evan’s findings—and guess what? It’s not the technology.

In our chat, Evan shares why he wanted to leverage a human approach to understand the root cause of multiple organizations’ failures and how this approach highlighted the disconnect between data scientists and decision-makers. He explains the human factors at play, such as poor problem surfacing and organizational culture challenges—and how these human-centered design skills are rarely taught or offered to data scientists. The conversation delves into why these failures are more prevalent in data science compared to other fields, attributing it to the complexity and scale of data-related problems. We also discuss how analytically mature companies can mitigate these issues through strategic approaches and stakeholder buy-in. Join us as we delve into these critical insights for improving data science project outcomes.

Highlights/ Skip to:

(4:45) Why are data science projects still failing? (9:17) Why is the disconnect between data scientists and decision-makers so pronounced relative to, say, engineering?  (13:08) Why are data scientists not getting enough training for real-world problems? (16:18) What the data says about failure rates for  mature data teams vs. immature data teams (19:39) How to change people’s opinions so they value data more (25:16) What happens at the stage where the beneficiaries of data don’t actually see the benefits? (31:09) What are the skills needed to prevent a repeating pattern of creating data products that customers ignore?? (37:10) Where do more mature organizations find non-technical help to complement their data science and AI teams?  (41:44) Are executives and directors aware of the skills needed to level up their data science and AI  teams?

Quotes from Today’s Episode “People know this stuff. It’s not news anymore. And so, the reason why we needed this was really to dig in. And exactly like you did, like, keeping that list of articles is brilliant, and knowing what’s causing the failures and what’s leading to these issues still arising is really important. But at some point, we need to approach this in a scientific fashion, and we need to unpack this, and we need to really delve into the details beyond just the headlines and the articles themselves. And start collating and analyzing this to properly figure out what’s going wrong, and what do we need to do about it to fix it once and for all so you can stop your endless collection, and the AI Incident Database that now has over 3500 entries. It can hang its hat and say, ‘I’ve done my job. It’s time to move on. We’re not failing as we used to.’” - Evan Shellshear (3:01) "What we did is we took a number of different studies, and we split companies into what we saw as being analytically mature—and this is a common, well-known thing; there are many maturity frameworks exist across data, across AI, across all different areas—and what we call analytically immature, so those companies that probably aren’t there yet. And what we wanted to draw a distinction is okay, we say 80% of projects fail, or whatever the exact number is, but for who? And for what stage and for what capability? And so, what we then went and did is we were able to take our data and look at which failures are common for analytically immature organizations, and which failures are common for analytically mature organizations, and then we’re able to understand, okay, in the market, how many organizations do we think are analytically mature versus analytically immature, and then we were able to take that 80% failure rate and establish it. For analytically mature companies, the failure rate is probably more like 40%. For analytically immature companies, it’s over 90%, right? And so, you’re exactly right: organizations can do something about it, and they can build capabilities in to mitigate this. So definitely, it can be reduced. Definitely, it can be brought down. You might say, 40% is still too high, but it proves that by bringing in these procedures, you’re completely correct, that it can be reduced.” - Evan Shellshear (14:28) "What happens with the data science person, however, is typically they’re seen as a cost center—typically, not always; nowadays, that dialog is changing—and what they need to do is find partners across the other parts of the business. So, they’re going to go into the supply chain team, they’ll go into the merchandising team, they’ll go into the banking team, they’ll go into the other teams, and they’re going to find their supporters and winners there, and they’re going to probably build out from there. So, the first step would likely be, if you’re a big enough organization that you’re not having that strategy the executive level is to find your friends—and there will be some of the organization who support this data strategy—and get some wins for them.” - Evan Shellshear (24:38) “It’s not like there’s this box you put one in the other in. Because, like success and failure, there’s a continuum. And companies as they move along that continuum, just like you said, this year, we failed on the lack of executive buy-in, so let’s fix that problem. Next year, we fail on not having the right resources, so we fix that problem. And you move along that continuum, and you build it up. And at some point as you’re going on, that failure rate is dropping, and you’re getting towards that end of the scale where you’ve got those really capable companies that live, eat, and breathe data science and analytics, and so have to have these to be able to survive, otherwise a simple company evolution would have wiped them out, and they wouldn’t exist if they didn’t have that capability, if that’s their core thing.” - Evan Shellshear (18:56)

“Nothing else could be correct, right? This subjective intuition and all this stuff, it’s never going to be as good as the data. And so, what happens is, is you, often as a data scientist—and I’ve been subjected to this myself—come in with this arrogance, this kind of data-driven arrogance, right? And it’s not a good thing. It puts up barriers, it creates issues, it separates you from the people.” - Evan Shellshear (27:38) "Knowing that you’re going to have to go on that journey from day one, you can’t jump from level zero to level five. That’s what all these data maturity models are about, right? You can’t jump from level zero data maturity to level five overnight. You really need to take those steps and build it up.” - Evan Shellshear (45:21) "What we’re talking about, it’s not new. It’s just old wine in a new skin, and we’re just presenting it for the data science age." - Evan Shellshear (48:15)

Links Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype: https://www.routledge.com/Why-Data-Science-Projects-Fail-the-Harsh-Realities-of-Implementing-AI-and-Analytics-without-the-Hype/Gray-Shellshear/p/book/9781032660301  LinkedIn: https://www.linkedin.com/in/eshellshear/  Get the Book: Get 20% off at Routledge.com w/ code dspf20   Get it at Amazon

Why do we still teach people to calculate? (People I Mostly Admire podcast)

This week Gerard will be presenting Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training By Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez.

All events will be hosted on a Google Meets video call. Once a month we also host an in-person event in our London office - watch this space for updates.

All recorded presentations can be found in our YouTube channel (don't forget to subscribe!).

We look forward to seeing you there!

In this episode, Conor and Bryce interview Richard Feldman, creator of the Roc programming language, about the last edition of the Strange Loop conference, virtual vs in-person events and more. Link to Episode 156 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Twitter ADSP: The PodcastConor HoekstraBryce Adelstein LelbachAbout the Guest: Richard Feldman is the creator of the Roc programming language, the host of the Software Unscripted podcast, and the author of Elm in Action from Manning Publications. He teaches online courses on Frontend Masters: Introduction to Rust, Introduction to Elm, and Advanced Elm. Outside of programming, he’s a fan of strategy games, heavy metal, powerlifting, and puns!

Show Notes

Date Recorded: 2023-11-13 Date Released: 2023-11-17 Software Unscripted PodcastWhy Isn’t Functional Programming the Norm? – Richard FeldmanStrange Loop Conference“The Economics of Programming Languages” by Evan Czaplicki (Strange Loop 2023)“Software & The Game of Go” by David Nolen (Strange Loop 2023)“A Long Strange Loop” by Alex Miller (Strange Loop 2023)Intro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Register here: https://www.eventbrite.com/e/create-a-webar-brand-experience-for-your-clients-tickets-661412922857?aff=meetup

Are you an agency or freelancer looking to create powerful, accessible, and app-free AR web campaigns accessible through any smartphone?

WebAR is a critical skill for any AR developer's portfolio. It opens up an immersive new AR world for clients and customers alike—for anyone with access to a web browser. We will showcase WebAR development through a simple-to-use Cloud Editor and deployment to smartphones, tablets, computers, and AR/VR headsets.

Takeaways:

• You don’t need to be an expert AR developer to create a powerful WebAR experience. Standard HTML and Javascript are enough! If you have web experience, you can learn WebAR quickly.

• Use templates to start with! Templates make it easy to start building and learning from complicated projects with 0 Experience.

• Own your development. Have total control over your launch timelines, development process, and collaboration with others without restrictive policies or app ecosystems.

• Show your new WebAR skills to agencies who need your creativity. AR is relevant like never before. More and more brands are seeking to activate AR experiences for their audiences.

Agenda:

• Walk through the development of a WebAR experience with 8th Wall's Cloud Editor, showing you how easy it is to get a WebAR experience up and running!

• Delve into the top use cases for WebAR and clone a sample project.

• Learn why brands are increasingly interested in browser-based AR experiences to showcase their value to audiences.

• Introduction to 8cademy, a self-paced, online school where you can go from starting your first project to publishing a unique WebAR experience in less than 5 hours.

Speakers:

Evan Carlson (he/him) is a Developer Support Engineer at 8th Wall. Evan Carlson is a Developer Support Engineer at Niantic, focused on supporting the WebAR community. He is a designer and developer passionate about interaction design and augmented reality – with expertise in three.js and React. Mische Kang (they/them; she/her) is the Global Learning Manager for AR Developer Platforms at Niantic. They specialize in the intersection of AR development and learning, believing it’s critical to our collective future to get as many different kinds of people creating in the real-life metaverse as possible. Crimson Wheeler is a Software Engineer, Game Designer, VR/AR Developer Co-founder of IC Studio. He specializes in Unity development, Virtual and Augmented Reality, Prototyping and Systems design. He worked with MIT Reality Hack 2023 Organization Member, AR House member, UNH HighTech-bound Intern, and Young Entrepreneurs Academy Participant. His accomplishments: Grand Finalist at MIT Reality Hack 2022, and Top 10 in Destination Imagination Tech track for Globals.

XR Pro Open Lecture Series

Subscribe to get invited to the following lectures featuring speakers from Tilt Five, Cubism, Owlchemy Labs, MelonLoader, Schell Games, Vertigo Games, and many more. XR Pro Lecture 1: Do’s and Don’ts: Eye Tracking for VR (UX) XR Pro Lecture 2: Creating XR Experiences with Passthrough XR Pro Lecture 3: Achieving PCVR quality on a Mobile Headset XR Pro Lecture 4: Unity and ChatGPT - for XR Developers and Artists XR Pro Lecture 5: How to Design and Prototype for XR - Best Practices and Examples

Lecture supported by:

Niantic builds augmented reality technology that powers the real-world metaverse. Its Lightship platform is the world’s first scaled AR platform, enabling developers worldwide to create sophisticated AR experiences for phones and eventually AR glasses. Lightship is also the foundation for Niantic’s hit games, including Pokémon GO, Pikmin Bloom and Ingress. Learn more about Niantic at nianticlabs.com. 8th Wall is Niantic Inc.’s award-winning AR developer platform and computer vision technology stack that makes it possible to build interactive web-based augmented reality (WebAR) that can be experienced on any smartphone—no app required. 8th Wall WebAR experiences can be accessed and engaged by over 5 billion smartphones across iOS and Android, as well as computers and AR/VR headsets.

Summary One of the biggest challenges for any business trying to grow and reach customers globally is how to scale their data storage. FaunaDB is a cloud native database built by the engineers behind Twitter’s infrastructure and designed to serve the needs of modern systems. Evan Weaver is the co-founder and CEO of Fauna and in this episode he explains the unique capabilities of Fauna, compares the consensus and transaction algorithm to that used in other NewSQL systems, and describes the ways that it allows for new application design patterns. One of the unique aspects of Fauna that is worth drawing attention to is the first class support for temporality that simplifies querying of historical states of the data. It is definitely worth a good look for anyone building a platform that needs a simple to manage data layer that will scale with your business.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support. Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit. You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. 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 Your host is Tobias Macey and today I’m interviewing Evan Weaver about FaunaDB, a modern operational data platform built for your cloud

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what FaunaDB is and how it got started? What are some of the main use cases that FaunaDB is targeting?

How does it compare to some of the other global scale databases that have been built in recent years such as CockroachDB?

Can you describe the architecture of FaunaDB and how it has evolved? The consensus and replication protocol in Fauna is intriguing. Can you talk through how it works?

What are some of the edge cases that users should be aware of? How are conflicts managed in Fauna?

What is the underlying storage layer?

How is the query layer designed to allow for different query patterns and model representations?

How does data modeling in Fauna compare to that of relational or document databases?

Can you describe the query format? What are some of the common difficulties or points of confusion around interacting with data in Fauna?

What are some application design patterns that are enabled by using Fauna as the storage layer? Given the ability to replicate globally, how do you mitigate latency when interacting with the database? What are some of the most interesting or unexpected ways that you have seen Fauna used? When is it the wrong choice? What have been some of the most interesting/unexpected/challenging aspects of building the Fauna database and company? What do you have in store for the future of Fauna?

Contact Info

@evan on Twitter LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Fauna Ruby on Rails CNET GitHub Twitter NoSQL Cassandra InnoDB Redis Memcached Timeseries Spanner Paper DynamoDB Paper Percolator ACID Calvin Protocol Daniel Abadi LINQ LSM Tree (Log-structured Merge-tree) Scala Change Data Capture GraphQL

Podcast.init Interview About Graphene

Fauna Query Language (FQL) CQL == Cassandra Query Language Object-Relational Databases LDAP == Lightweight Directory Access Protocol Auth0 OLAP == Online Analytical Processing Jepsen distributed systems safety research

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

Support Data Engineering Podcast

Communicating your idea in a clear, compelling, and persuasive manner is critical when trying to launch a new venture. This Harvard Business Review collection brings together two popular books to help you craft your story, design better visualizations, impress your audience, and turn your idea into reality. Understanding and using data viz to persuade is a must-have skill for anyone in business today--especially if you're launching a new venture. In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping. How do you launch the venture of your dreams? In Get Backed, entrepreneurs Evan Baehr and Evan Loomis argue that it's not just about securing startup funding. It's about building the right relationships, crafting a compelling story, and creating the perfect pitch deck. Filled with proven tips, exercises, and templates, this book shows the process for how to successfully communicate your vision. Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas. Get Backed will show you exactly what it takes to get funded and will give you the tools to launch a new venture. Together, these books will help you bring your idea to life.