This comprehensive book on Tableau 2023 is your practical guide to mastering data visualization and business intelligence techniques. You will explore the latest features of Tableau, learn how to create insightful dashboards, and gain proficiency in integrating analytics and machine learning workflows. By the end, you'll have the skills to address a variety of analytics challenges using Tableau. What this Book will help me do Master the latest Tableau 2023 features and use cases to tackle analytics challenges. Develop and implement ETL workflows using Tableau Prep Builder for optimized data preparation. Integrate Tableau with programming languages such as Python and R to enhance analytics. Create engaging, visually impactful dashboards for effective data storytelling. Understand and apply data governance to ensure data quality and compliance. Author(s) Marleen Meier is an experienced data visualization expert and Tableau consultant with over a decade of experience helping organizations transform data into actionable insights. Her approach integrates her technical expertise and a keen eye for design to make analytics accessible rather than overwhelming. Her passion for teaching others to use visualization tools effectively shines through in her writing. Who is it for? This book is ideal for business analysts, BI professionals, or data analysts looking to enhance their Tableau expertise. It caters to both newcomers seeking to understand the foundations of Tableau and experienced users aiming to refine their skills in advanced analytics and data visualization. If your goal is to leverage Tableau as a strategic tool in your organization's BI projects, this book is for you.

Summary

Data persistence is one of the most challenging aspects of computer systems. In the era of the cloud most developers rely on hosted services to manage their databases, but what if you are a cloud service? In this episode Vignesh Ravichandran explains how his team at Cloudflare provides PostgreSQL as a service to their developers for low latency and high uptime services at global scale. This is an interesting and insightful look at pragmatic engineering for reliability and scale.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Vignesh Ravichandran about building an internal database as a service platform at Cloudflare

Interview

Introduction How did you get involved in the area of data management? Can you start by describing the different database workloads that you have at Cloudflare?

What are the different methods that you have used for managing database instances?

What are the requirements and constraints that you had to account for in designing your current system? Why Postgres? optimizations for Postgres

simplification from not supporting multiple engines

limitations in postgres that make multi-tenancy challenging scale of operation (data volume, request rate What are the most interesting, innovative, or unexpected ways that you have seen your DBaaS used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on your internal database platform? When is an internal database as a service the wrong choice? What do you have planned for the future of Postgres hosting at Cloudflare?

Contact Info

LinkedIn Website

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. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Mac

The economy is performing about as well as could be expected. Growth is resilient, inflation is moderating, and unemployment is low and stable. It appears increasingly likely the economy will avoid a recession. But having said this, the Federal Reserve’s aggressive rate hikes are weighing heavily on the financial system and economy. To avoid a recession, nothing else can go wrong. In this podcast, Mark, Cris and team consider what could go wrong.  For the full transcript, 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.

Jerry Liu is the CEO and co-founder of LlamaIndex. LlamaIndex is an open-source framework that helps people prep their data for use with large language models in a process called retrieval augmented generation. LLMs are great decision engines, but in order for them to be useful for organizations, they need additional knowledge and context, and Jerry discusses how companies are bringing their data to tailor LLMs for their needs. 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.

By choosing the right database, you can maximize your business potential, improve performance, increase efficiency, and gain a competitive edge. This insightful report examines the benefits of using a simplified data architecture containing cloud-based HTAP (hybrid transactional and analytical processing) database capabilities. You'll learn how this data architecture can help data engineers and data decision makers focus on what matters most: growing your business. Authors Joe McKendrick and Ed Huang explain how cloud native infrastructure supports enterprise businesses and operations with a much more agile foundation. Just one layer up from the infrastructure, cloud-based databases are a crucial part of data management and analytics. Learn how distributed SQL databases containing HTAP capabilities provide more efficient and streamlined data processing to improve cost efficiency and expedite business operations and decision making. This report helps you: Explore industry trends in database development Learn the benefits of a simplified data architecture Comb through the complex and crowded database choices on the market Examine the process of selecting the right database for your business Learn the latest innovations database for improving your company's efficiency and performance

Join host Jason Foster in an enlightening conversation with Karen Jean Francois, Analytics Manager at Monzo Bank and host of the Women in Data Podcast, as they dive into the intriguing world of data careers. In this episode, Karen shares her insights and her brilliant journey in the data field, along with the hard-earned lessons learned along the way. Tune in and discover the pivotal importance of clear guidance, bridging expectations, and fostering cross-functional collaboration for success in data-driven careers

Want to know what it takes to land your dream data analytics job?

Dr. Serena Huang, an ex-PayPal Analytics Executive, spills the beans and shares invaluable tips on standing out in the hiring process. 🫘

She also discusses what People Analytics is & how to learn more.

Whether you're a new grad or a seasoned professional looking to make a career transition, this episode has got you covered!

Tune in now and unlock the possibilities. 🎧

⁠📩 Get my weekly email with helpful data career tips⁠

⁠📊 Come to my next free “How to Land Your First Data Job” training⁠

⁠🏫 Check out my 10-week data analytics bootcamp⁠

Connect with Dr Serena Huang:

🤝 Linkedin

🌐 Website

⏯️ Linkedin People Analytics Course I

⏯️ Linkedin People Analytics Course II

Timestamps:

(04:58) - 🚀 What is People Analytics

(17:09) - 🌟 Keys to getting hired in data

(25:06) - 📊 What makes a good data team

Connect with Avery:

📺 Subscribe on YouTube

🎙Listen to My Podcast

👔 Connect with me on LinkedIn

📸 Instagram

🎵 TikTok

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

We’re definitely in AI hype mode at the moment largely driven by the evolution in generative AI. However, it seems like this progress is not necessarily driving lots of data-related innovation inside organisations that are not AI-first tech companies. A recent survey published by Randy Bean’s company, NewVantage Partners, confirms this. Here are the main findings compared to when the survey was last run 4 years ago: 59.5% of executives say their companies use data for business innovation – the same as four years ago.A drop from 47.6% to 40.8% of executives say their companies compete using data and analytics.Fewer executives (39.5% down from 46.9%) say their companies manage data as a business asset.Only 23.9% of executives now say their companies are data-driven, compared to 31% before.Just 20.6% of executives report having a data culture in their companies, down 27% from 28.3% in 2019.These numbers spell regression, not progress. Why is it so hard to become a truly data-driven organisation? In this episode, Randy and I explore the challenges facing Chief Data & Analytics Officers and their teams, including: How organizations can create an environment that encourages innovation in data-driven initiativesExamples of organisations doing data well, and whyHow to set clear expectations around the responsibilities of CDAOsThe most important qualities for someone in the CDAO role, and much more.Randy on LinkedIn: https://www.linkedin.com/in/randybeannvp/ Randy's website and book, 'Fail Fast, Learn Faster': https://www.randybeandata.com/book

We were curious about… curiosity. We know it's a critical trait for analysts, but is it an innate characteristic, a teachable skill, or some combination of both? We were curious. How can the breadth and depth of a candidate's curiosity be assessed as part of the interview process? We were curious. Who could we kick these questions (and others) around with? We were NOT curious about that! MaryBeth Maskovas, founder and Principal Consultant at Insight Lime Analytics, joined Michael, Julie, and Tim to explore the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

On today’s episode, we’re joined by Jason Kruger, President & Founder at Signature Analytics, a provider of expert-level accounting and business advisory solutions to small and middle-market businesses throughout Southern California and beyond.

We talk about: Accounting mistakes commonly made by SaaS foundersThe challenges of balancing CEO optimism with necessary pragmatismAreas successful SaaS CEOs spend the most time onThe collaborative process of developing KPIsThe trouble with viewing accounting as a necessary evil

Summary

Generative AI has unlocked a massive opportunity for content creation. There is also an unfulfilled need for experts to be able to share their knowledge and build communities. Illumidesk was built to take advantage of this intersection. In this episode Greg Werner explains how they are using generative AI as an assistive tool for creating educational material, as well as building a data driven experience for learners.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It’s the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it’s real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize today to get 2 weeks free! Your host is Tobias Macey and today I'm interviewing Greg Werner about building IllumiDesk, a data-driven and AI powered online learning platform

Interview

Introduction How did you get involved in the area of data management? Can you describe what Illumidesk is and the story behind it? What are the challenges that educators and content creators face in developing and maintaining digital course materials for their target audiences? How are you leaning on data integrations and AI to reduce the initial time investment required to deliver courseware? What are the opportunities for collecting and collating learner interactions with the course materials to provide feedback to the instructors? What are some of the ways that you are incorporating pedagogical strategies into the measurement and evaluation methods that you use for reports? What are the different categories of insights that you need to provide across the different stakeholders/personas who are interacting with the platform and learning content? Can you describe how you have architected the Illumidesk platform? How have the design and goals shifted since you first began working on it? What are the strategies that you have used to allow for evolution and adaptation of the system in order to keep pace with the ecosystem of generative AI capabilities? What are the failure modes of the content generation that you need to account for? What are the most interesting, innovative, or unexpected ways that you have seen Illumidesk us

Despite the light week of economic data, there was lots going in in the economy to discuss for the Inside Economics team, including the runup in 10-year Treasury yields and fixed mortgage rates. Noted investment banker Chris Whalen then joins the conversation to talk about the banking system, its’ under significant pressure, the independent mortgage banks, a shakeout is underway, and the Fed and other regulators, he’s not a fan. For more on Chris Whalen, click here For the full transcript, 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.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Em um papo empolgante, mergulhamos no universo dos profissionais de dados e suas habilidades essenciais, com um foco especial no poderoso Power BI e demais ferramentas. Descubra, como as ferramentas de analytics, como o Power BI, estão moldando o futuro do campo de dados e análises.

Nste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam as apaixonadas pela área de dados e principais referências no assunto: a Karine Lago — especialista em Business Intelligence, Power BI e Excel, premiada pela Microsoft mais de sete vezes e Escritora; e a Letícia Smirelli — Chief Product Officer (CPO), Power BI Specialist, Microsoft Data Analyst Associate e DataViz & Dashboard Design; ambas sócias na Nexos Educação.

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!

Link Medium: https://medium.com/data-hackers/power-bi-dashboards-e-a-carreira-de-analista-de-dados-data-hackers-podcast-72-829986f5f2a1

Falamos no episódio

Conheça nosso convidado:

Karine Lago — especialista em Business Intelligence, Escritora, Power BI e Excel, premiada pela Microsoft mais de sete vezes;  Letícia Smirelli — Chief Product Officer (CPO), Power BI Specialist, Microsoft Data Analyst Associate e DataViz & Dashboard Design.

Bancada Data Hackers:

Paulo Vasconcellos Gabriel Lages Monique Femme

Links de referências:

Tech and Cheers — Meetup ed. Data Connect (São Paulo): https://www.sympla.com.br/evento/tech-and-cheers-meetup-ed-data-connect/2110360 https://towardsdatascience.com/whats-the-difference-between-analytics-and-statistics-cd35d457e17

Tech and Cheers — ed. Mulher.ADA (Blumenau):https://www.sympla.com.br/evento/tech-and-cheers-meetup-ed-mulher-ada/2109236

World Economic Forum (The Future of Jobs Report 2023):https://www.weforum.org/reports/the-future-of-jobs-report-2023/ Canal Karine Lago (Youtube):https://www.youtube.com/@KarineLago Pagina Karine Lago: https://keepo.io/karinedolago/?fbclid=PAAaZ32JXyRtPv7wcHcfaxtKA5TOU9VRaCt_F_nb7zhAptO4AtthorxiHWCdg_aem_Ab53sgYj0AXg1wHrOP9-c_K7pwoMqX0psYWAvNMAanqh5pafTHBFb3bnshKB534J9AA Canal Leticia Smirelli (Youtube): https://www.youtube.com/@LeticiaSmirelli Pagina Leticia Smirelli: https://keepo.io/leticia/?fbclid=PAAabu7cvnFTkkFw1UiJrDMIXiMJ45Av6XKlCXIfWAUiRH2c4kiSZzo7FX6TY_aem_Ab7BHn25MaVK22HFw9zXNfsYv5k5Y5o9WLMGZeFB9wSSSAV3d7EDA0JuGjXWSqd_SEs

This talk was recorded at Crunch Conference 2022. Thomas from Tasman Analytics spoke about data modeling for startups.

"In this talk we will share our secrets on rapidly deploying analytics for our clients. As a business, we focus on setting up data infrastructure, data models and reporting, and do it as fast and as well as we can — but we also want to make sure that what we built is scaleable, and still relevant long after we leave."

The event was organized by Crafthub.

You can watch the rest of the conference talks on our channel.

If you are interested in more speakers, tickets and details of the conference, check out our website: https://crunchconf.com/ If you are interested in more events from our company: https://crafthub.events/

This talk was recorded at Crunch Conference 2022. Kathleen from Experian spoke about setting a foundation for analytics as a business utility.

"I'll share the "core" skills needed to stand-out as an analytics thought leader and go-to strategic partner for the business."

The event was organized by Crafthub.

You can watch the rest of the conference talks on our channel.

If you are interested in more speakers, tickets and details of the conference, check out our website: https://crunchconf.com/ If you are interested in more events from our company: https://crafthub.events/

Explore the comprehensive world of UX analytics with "Unleashing the Power of UX Analytics." This book uncovers proven techniques to collect, analyze, and interpret crucial data for enhancing user experiences. Through practical insights and methodologies, you'll master the art of creating empathetic, data-informed designs that meet user needs effectively. What this Book will help me do Master the techniques for effective qualitative and quantitative user data analysis. Learn to implement and interpret key UX metrics and KPIs to guide design processes. Understand and apply design thinking to bridge user goals with business objectives. Discover how to utilize and integrate UX analytics tools and methodologies. Learn strategies for presenting research findings and making impactful data-driven recommendations. Author(s) Jeff Hendrickson is a seasoned UX designer, researcher, and analytics expert with over a decade of experience in the field. His approach emphasizes empathy and user needs in driving design solutions, combining his background in psychology and data analysis. Jeff's ability to translate complex analytics into actionable insights makes his writing relatable and pragmatic for professionals. Who is it for? This book is perfect for UX researchers, product managers, and designers who aim to deepen their understanding of UX analytics. Whether you're an experienced professional or just beginning your journey, you'll gain the tools to make informed, user-centric decisions. If you're eager to enhance your design impact through analytics, this book is for you.

In this episode, Andrew Madson, an experienced data analytics professional & hiring manager, shares valuable insights and tips on transitioning into the field of data analytics. 💠

We discussed what ACTUALLY gets you hired, how to make good data projects, & how to pivot into the data field.

Whether you're just starting your data career journey or looking to make a pivot, this episode is a must-listen! 🎧

🤝 Connect with Andrew Madson

📩 Get my weekly email with helpful data career tips

📊 Come to my next free “How to Land Your First Data Job” training

🏫 Check out my 10-week data analytics bootcamp

Timestamps:

(03:46) - 📈 The Surprising Skill You Need

(13:20) - 🎙️ Make Your Data Projects Clear

(20:28) - 🔍 Data Skills You Should Know

(29:11) - 🌐 Networking: Your Gateway to Career Success!

Connect with Avery:

📺 Subscribe on YouTube

🎙Listen to My Podcast

👔 Connect with me on LinkedIn

📸 Instagram

🎵 TikTok

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

As companies scale and become more successful, new horizons open, but with them come unexpected challenges. The influx of revenue and expansion of operations often reveal hidden complexities that can hinder efficiency and inflate costs. In this tricky situation, data teams can find themselves entangled in a web of obstacles that slow down their ability to innovate and respond to ever-changing business needs. Enter cloud analytics—a transformative solution that promises to break down barriers and unleash potential. By migrating analytics to the cloud, organizations can navigate the growing pains of success, cutting costs, enhancing flexibility, and empowering data teams to work with agility and precision. John Knieriemen is the Regional Business Lead for North America at Exasol, the market-leading high-performance analytics database. Prior to joining Exasol, he served as Vice President and General Manager at Teradata during an 11-year tenure with the company. John is responsible for strategically scaling Exasol’s North America business presence across industries and expanding the organization’s partner network.  Solongo Erdenekhuyag is the former Customer Success and Data Strategy Leader at Exasol. Solongo is skilled in strategy, business development, program management, leadership, strategic partnerships, and management. In the episode, Richie, Solongo, and John cover the motivation for moving analytics to the cloud, economic triggers for migration, success stories from organizations who have migrated to the cloud, the challenges and potential roadblocks in migration, the importance of flexibility and open-mindedness and much more.  Links from the Show ExasolAmazon S3Azure Blob StorageGoogle Cloud StorageBigQueryAmazon RedshiftSnowflake[Course] Understanding Cloud Computing[Course] AWS Cloud Concepts

Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book's recipes to implement observability in your data projects Use data observability to create a trustworthy communication framework with data consumers Learn how to educate your peers about the benefits of data observability

Summary

Data pipelines are the core of every data product, ML model, and business intelligence dashboard. If you're not careful you will end up spending all of your time on maintenance and fire-fighting. The folks at Rivery distilled the seven principles of modern data pipelines that will help you stay out of trouble and be productive with your data. In this episode Ariel Pohoryles explains what they are and how they work together to increase your chances of success.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold Your host is Tobias Macey and today I'm interviewing Ariel Pohoryles about the seven principles of modern data pipelines

Interview

Introduction How did you get involved in the area of data management? Can you start by defining what you mean by a "modern" data pipeline? At Rivery you published a white paper identifying seven principles of modern data pipelines:

Zero infrastructure management ELT-first mindset Speaks SQL and Python Dynamic multi-storage layers Reverse ETL & operational analytics Full transparency Faster time to value

What are the applications of data that you focused on while identifying these principles? How do the application of these principles influence the ability of organizations and their data teams to encourage and keep pace with the use of data in the business? What are the technical components of a pipeline infrastructure that are necessary to support a "modern" workflow? How do the technologies involved impact the organizational involvement with how data is applied throughout the business? When using managed services, what are the ways that the pricing model acts to encourage/discourage experimentation/exploration with data? What are the most interesting, innovative, or unexpected ways that you have seen these seven principles implemented/applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working with customers to adapt to these principles? What are the cases where some/all of these principles are undesirable/impractical to implement? What are the opportunities for further advancement/sophistication in the ways that teams work with and gain value from data?

Contact Info

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. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned somethi