Every day, banking institution Capital on Tap is calculating thousands of credit scores, directly impacting how their customers receive credit cards or additional lines of credit. Data quality is paramount – incorrect credit scores can set off a wide range of long-lasting financial implications for their customers, which is why the team turned to data observability with Monte Carlo, to improve their data – and credit score – reliability. 

But, as with any new tool in your tech stack, onboarding new processes for key users is just as important as onboarding the tool itself. 

Join this session with Ben Jones and Soren Rehn, to hear why the Analytics Engineering team at Capital on Tap decided to invest in a data observability tool, how their processes play a critical role in maximizing the tool’s value (including a few missteps and recalibrations along the way), and the strategies employed to garner widespread success and buy-in over time.

Join us to explore why Analytics as Code is the future of BI & learn how to make it a core component of your analytics engineering strategy.

Building visualizations in a BI tool is just the beginning. To create truly innovative data products, we must blend BI and data pipelines into a reactive, composable end-to-end system. Such a system adapts to changing business conditions, evolving technologies, and increased scale to meet customer needs. Join us to explore why “Analytics as Code” is the future of BI and learn how to make it a core component of your analytics engineering strategy.

Analytics Engineering is one of the hottest career paths in data today, but many struggle to understand how to break in and where they should focus. In this episode, we demystify Analytics Engineering. Madison Schott talks about the career path of an Analytics Engineer, what they do for companies, and the tools they use. She also shares some of the best strategies and actionable advice for those looking to break into Analytics Engineering or take their career to the next level.   What You'll Learn: The day-to-day responsibilities and potential career paths of an Analytics Engineer The skills you should focus on if you want to break into Analytics Engineering Tips for networking, finding jobs, and acing the Analytics Interview   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Madison Schott is the Senior Analytics Engineer at ConvertKit and Author of the Learn Analytics Engineering newsletter Sign up for Madison's newsletter The ABCs of Analytics Engineering E-Book Follow Madison on LinkedIn

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Dr. Eirini Kalliamvakou is a senior researcher at GitHub Next. Eirini has built a career on studying software engineers, how to measure their productivity, how developer experience impacts productivity, and more. Recently, Eirini has been working on quantifying the impacts of GitHub Copilot. Does it actually help software engineers be more productive? Tristan and Eirini explore how to quantify developer productivity in the first place, and finally, arriving at whether or not Copilot‌ makes a difference. In the search for real business value, this research is a real bellwether of things to come. 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. Join data practitioners and data leaders this October in Las Vegas at Coalesce, the analytics engineering conference hosted by dbt Labs. Register now at coalesece.getdbt.com. Listeners of this show can use the code podcast20 for a 20% discount.

We're seeing the title "Analytics Engineer" continue to rise, and it's in large part due to individuals realizing that there's a name for the type of work they've found themselves doing more and more. In today's landscape, there's truly a need for someone with some Data Engineering chops with an eye towards business use cases. We were fortunate to have the one of the co-authors of The Fundamentals of Analytics Engineering, Dumky de Wilde, join us to discuss the ins and outs of this popular role! Listen in to hear more about the skills and responsibilities of this role, some fun analogies to help explain to your grandma what AE's do, and even tips for individuals in this role for how they can communicate the value and impact of their work to senior leadership! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Neste episódio mergulhamos no mundo da jornada e engenharia de dados e analytics com os especialistas do Itaú. Conheça as estratégias de dados que moldam o futuro do banco, além do compartilhamento de como é trabalhar em uma empresa onde a utilização intensiva de dados é essencial para tomar as melhores decisões.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam as pessoas que desempenham um papel crucial na infraestrutura de dados do Itaú:

Priscila Militão — Data Engineer no Itaú Unibanco Vinicius Rio — Analista de Dados no Itaú Unibanco Thiago Panini — Analytics Engineer no Itaú Unibanco Carlos Vaccáro — Gerente de Analytics Engineering no Itaú Unibanco

Prepare-se para uma imersão no futuro dos dados no Itaú e descubra como essas mentes brilhantes estão moldando o panorama financeiro global com insights poderosos.

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Monique Femme — Head of Community Management na Data Hackers Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Referências:

Página da Vaga | Batalha de Dados — Engenharia de Dados e Analytics :https://vemproitau.gupy.io/jobs/7312897 Episódio Computação Quantica Itaú: https://medium.com/data-hackers/o-que-%C3%A9-computa%C3%A7%C3%A3o-qu%C3%A2ntica-data-hackers-podcast-84-0389a3b299ab

Yohei Nakajima is an investor by day and coder by night. In particular, one of his projects, an AI agent framework called BabyAGI that creates a plan-execute loop, got a ton of attention in the past year. The truth is that AI agents are an extremely experimental space, and depending on how strict you want to be with your definition, there aren't a lot of production use cases today.  Yohei discusses the current state of AI agents and where they might take us.  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.

Misha Panko has worked in data for a long time, including on high performance data teams at Uber and Google. Today, Misha is the co-founder and CEO of Motif Analytics, a product focused on helping growth and ops teams understand their event data. In this episode, Tristan and Misha nerd out about the state of the art in computational neuroscience, where Misha got his PhD. They then go deep into event stream data and how it differs from classical fact and dimension data, and why it needs different analytical tools. Make sure to check out the back half of the episode, where they dive into AI and how Motif is applying breakthroughs in language modeling to train foundation models of event sequences—check out his team's blog post on their work. 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.

Barry McCardel is the co-founder and CEO of Hex. Hex is an analytics tool that's structured around a notebook experience, but as you'll hear in the episode, goes well beyond the traditional notebook. We're big fans of Hex at dbt Labs, and use it for a bunch of our internal data work. In this episode, Barry and Tristan discuss notebooks and data analysis, before zooming out to discuss the hype cycle of data science, how AI is different, the experience of building AI products, and how AI will impact data practitioners. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Matt Turck has been publishing his ecosystem map since 2012. It was first called the Big Data Landscape. Now it's the Machine Learning, AI & Data (MAD) Landscape.  The 2024 MAD Landscape includes 2,011(!) logos, which Matt attributes first a data infrastructure cycle and now an ML/AI cycle. As Matt writes, "Those two waves are intimately related. A core idea of the MAD Landscape every year has been to show the symbiotic relationship between data infrastructure, analytics/BI,  ML/AI, and applications." Matt and Tristan discuss themes in Matt's post: generative AI's impact on data analytics, the modern AI stack compared to the modern data stack, and Databricks vs. Snowflake (plus Microsoft Fabric). For full show notes and to read 7+ 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.

Master the art and science of analytics engineering with 'Fundamentals of Analytics Engineering.' This book takes you on a comprehensive journey from understanding foundational concepts to implementing end-to-end analytics solutions. You'll gain not just theoretical knowledge but practical expertise in building scalable, robust data platforms to meet organizational needs. What this Book will help me do Design and implement effective data pipelines leveraging modern tools like Airbyte, BigQuery, and dbt. Adopt best practices for data modeling and schema design to enhance system performance and develop clearer data structures. Learn advanced techniques for ensuring data quality, governance, and observability in your data solutions. Master collaborative coding practices, including version control with Git and strategies for maintaining well-documented codebases. Automate and manage data workflows efficiently using CI/CD pipelines and workflow orchestrators. Author(s) Dumky De Wilde, alongside six co-authors-experienced professionals from various facets of the analytics field-delivers a cohesive exploration of analytics engineering. The authors blend their expertise in software development, data analysis, and engineering to offer actionable advice and insights. Their approachable ethos makes complex concepts understandable, promoting educational learning. Who is it for? This book is a perfect fit for data analysts and engineers curious about transitioning into analytics engineering. Aspiring professionals as well as seasoned analytics engineers looking to deepen their understanding of modern practices will find guidance. It's tailored for individuals aiming to boost their career trajectory in data engineering roles, addressing fundamental to advanced topics.

Matthew Lynley is a bit of a hybrid. He's been a long-time journalist covering enterprise tech, currently in his fantastic AI and data newsletter Supervised, and he's also been a hands-on data practitioner.  Matthew has covered the analytics tech stack, but this time Tristan turns the tables to get Matthew's perspective on the rise of Gen AI as a topic in the popular press, what's going on in the space today, and where AI is headed. 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.

Juan Sequeda is a principal data scientist and head of the AI Lab at data.world, and is also the co-host of the fantastic data podcast Catalog and Cocktails.  This episode tackles semantics, semantic web, Juan's research in how raw text-to-SQL performs versus text-to-semantic layer,  and where we both believe AI will make an impact in the world of structured data analytics. 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.

Benn Stancil, cofounder and CTO at Mode, returns to The Analytics Engineering Podcast to discuss the evolution of the term "modern data stack" and its value today. Tristan wrote on this idea for The Analytics Engineering Roundup in Is the Modern Data Stack Still a Useful Idea? 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.

Send us a text Welcome to the cozy corner of the tech world where ones and zeros mingle with casual chit-chat. Datatopics Unplugged is your go-to spot for relaxed discussions around tech, news, data, and society. Dive into conversations that should flow as smoothly as your morning coffee (but don’t), where industry insights meet laid-back banter. Whether you’re a data aficionado or just someone curious about the digital age, pull up a chair, relax, and let’s get into the heart of data, unplugged style! In this episode, we’re joined by Maryam, an Analytics Engineer with a passion for challenges and a knack for curiosity. From sewing to yoga, Maryam brings a unique perspective to our tech-centric discussions. Analytics Engineer Insights: Maryam discusses her role, the rise of Analytics Engineers, and their essential tools. Read more about Analytics Engineering.The Emerging Role of AI Translator: Exploring the link between Analytics Engineers and AI Translators, and the skills required in these evolving fields. Learn about AI Translator.Mistral AI’s New Developments: Analyzing Mistral AI’s latest model and its implications for the industry. Discover Mistral AI’s update.ChatGPT – A Double-Edged Sword: Discussing the impacts of ChatGPT on the AI landscape and the pace of innovation. Reflect on ChatGPT’s impact.ChatGPT & Job Applications: A fresh take on how ChatGPT is influencing job applications and hiring processes.Engineering Management Insights: Exploring whether becoming an Engineering Manager is a path worth considering.Intro music courtesy of fesliyanstudios.com.

We streamed live!

Moritz Heimpel from Siemens and Ben Flusberg from Cox Automotive have very similar jobs. They both act as stewards of the data strategies at large, complex companies. In this episode, we get into what it's like to collaborate with data at scale. Ben and Mortitz share their experiences adopting a data mesh architecture and what that looks like at their organizations. 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.

With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL. Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence. With this book, you'll learn: What dbt is and how a dbt project is structured How dbt fits into the data engineering and analytics worlds How to collaborate on building data models The main tools and architectures for building useful, functional data models How to fit dbt into data warehousing and laking architecture How to build tests for data transformations

If Data Vault is a new term for you, it's a data modeling design pattern. We're joined by Brandon Taylor, a senior data architect at Guild, and Michael Olschimke, who is the CEO of Scalefree—the consulting firm whose co-founder Dan Lindstedt is credited as the designer of the data vault architecture.  In this conversation with Tristan and Julia, Michael and Brandon explore the Data Vault approach among data warehouse design methodologies. They discuss Data Vault's adoption in Europe, its alignment with data mesh architecture, and the ongoing debate over Data Vault vs. Kimball methods.  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.