Data modeling is the single most overlooked feature in Power BI Desktop, yet it's what sets Power BI apart from other tools on the market. This practical book serves as your fast-forward button for data modeling with Power BI, Analysis Services tabular, and SQL databases. It serves as a starting point for data modeling, as well as a handy refresher. Author Markus Ehrenmueller-Jensen, founder of Savory Data, shows you the basic concepts of Power BI's semantic model with hands-on examples in DAX, Power Query, and T-SQL. If you're looking to build a data warehouse layer, chapters with T-SQL examples will get you started. You'll begin with simple steps and gradually solve more complex problems. This book shows you how to: Normalize and denormalize with DAX, Power Query, and T-SQL Apply best practices for calculations, flags and indicators, time and date, role-playing dimensions and slowly changing dimensions Solve challenges such as binning, budget, localized models, composite models, and key value with DAX, Power Query, and T-SQL Discover and tackle performance issues by applying solutions in DAX, Power Query, and T-SQL Work with tables, relations, set operations, normal forms, dimensional modeling, and ETL

Juha Korpela is a world-renowned expert in conceptual data modeling. He joins me to discuss the power of conceptual data modeling, why the data modeling world is broken today, data products, and much more.

LinkedIn: https://www.linkedin.com/in/jkorpela/

Discover the power of Looker for Business Intelligence and data visualization in this comprehensive cookbook. This book serves as your guide to mastering Looker's tools and features, enabling you to transform data into actionable insights. What this Book will help me do Understand Looker's key components, including LookML and dashboards. Explore advanced Looker capabilities, including data modeling and interactivity. Create dynamic dashboards to monitor and present critical metrics effectively. Integrate Looker with additional tools and systems to extend its capabilities. Leverage Looker's tools for fostering data-driven decision-making within your team. Author(s) Khrystyna Grynko is a seasoned data professional with extensive experience in Business Intelligence and analytics. She brings practical insights into how to effectively utilize Looker for real-world applications. Khrystyna is known for her clear, instructional writing style that makes complex topics approachable. Who is it for? This book is an essential resource for business analysts, data analysts, or BI developers looking to expand their expertise in Looker. Suitable for readers with a basic understanding of business intelligence concepts. Ideal for professionals who aim to leverage Looker for creating insightful and interactive data applications to inform business strategy.

Is data modeling a waste of time? I meet a number of people who say it is. In this episode, I dissect some of the arguments against data modeling, and give reasons why it matters more than ever today.

In today's Practical Data Modeling group discussion, we chatted about how to get buy-in for data modeling. The question was intentionally vague, because context is key. I give some thoughts on this topic, and how you can generalize this to most situations where you need to get buy-in.

Practical Data Modeling: https://practicaldatamodeling.substack.com/

Keith Belanger is an OG data modeling practitioner, having been in the game for decades.

We chat about a wide range of data modeling topics.

What's changed and what's stayed the same? How to model data to fit the business's needs. Agile data modeling. When it works, when it doesn't. Data modeling for data mesh and decentralization. The art of data modeling How to teach conceptual data modeling to new practitioners

Keith brings a wealth of experience and a practical, no-nonsense perspective. If you're interested in data modeling, don't miss this!

LinkedIn: https://www.linkedin.com/in/krbelanger/

This morning, the Practical Data Modeling Community held its first group discussion (to be posted very soon). People from all sorts of organizations (biggest companies in the world, universities, small companies) discussed how the approach analytical data modeling.

My major takeaway - your mileage will vary. There's the ideal way of data modeling we're taught, and there's reality. Everyone's situation is different and there's no one-size-fits-all approach that will work for everyone.

The discussion was awesome, and we'll do it again soon. If you're not part of the Practical Data Modeling Community, please join here: https://practicaldatamodeling.substack.com/

The Complete Power BI Interview Guide is your companion to mastering Power BI roles and acing data analyst interviews. With hands-on skills, expert tips, and targeted preparation strategies, this resource equips you to excel in interviews and certifications while navigating the competitive job market. What this Book will help me do Create a powerful professional brand to optimize your resume and online presence. Master essential Power BI skills including data modeling, DAX programming, and visualization. Prepare effectively for interviews with industry-relevant questions, answers, and insights. Gain an edge in the market by understanding hiring procedures and negotiation tactics. Develop comprehensive analytics solutions exemplified with real-world case studies. Author(s) Sandielly Ortega Polanco, Gogula Aryalingam, and Abu Bakar Nisar Alvi bring years of collective experience in data analytics, Power BI, and career mentorship. Their insights are drawn from extensive professional practice and their passion for empowering future data analysts. Together, they provide an approachable and practical guide to securing roles in the competitive landscape of data analytics. Who is it for? This book is ideal for aspiring data analysts, business intelligence developers, or those shifting into Power BI roles who wish to enhance their knowledge and refine their strategies for interview success. It speaks to both newcomers to the field and seasoned professionals aiming to elevate their expertise.

Advance your Power BI skills by adding AI to your repertoire at a practice level. With this practical book, business-oriented software engineers and developers will learn the terminologies, practices, and strategy necessary to successfully incorporate AI into your business intelligence estate. Jen Stirrup, CEO of AI and BI leadership consultancy Data Relish, and Thomas Weinandy, research economist at Upside, show you how to use data already available to your organization. Springboarding from the skills that you already possess, this book adds AI to your organization's technical capability and expertise with Microsoft Power BI. By using your conceptual knowledge of BI, you'll learn how to choose the right model for your AI work and identify its value and validity. Use Power BI to build a good data model for AI Demystify the AI terminology that you need to know Identify AI project roles, responsibilities, and teams for AI Use AI models, including supervised machine learning techniques Develop and train models in Azure ML for consumption in Power BI Improve your business AI maturity level with Power BI Use the AI feedback loop to help you get started with the next project

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.

Had a great chat with Keith Belanger yesterday (podcast dropping soon) about how conceptual data modeling fell by the wayside. All too often, people seem focused on physical data modeling. This is a shame, because conceptual is the art and lifeblood of data modeling. As an industry, we need to learn to see (again).

Is it better to make a perfect or good enough data model? It depends…

I consider Steve Hoberman to be one of the original data modelers, having practiced and taught data modeling since the 1990s. He also runs the venerable Technics Publications, which I consider the foremost publishers of data-oriented books.

Steve and I discuss data modeling's past, present, and future. If you're into data modeling, this is a must-listen. Enjoy!

Technics Publications: https://technicspub.com/

Steve Hoberman LinkedIn - https://www.linkedin.com/in/stevehoberman/

This week I’m chatting with Caroline Zimmerman, Director of Data Products and Strategy at Profusion. Caroline shares her journey through the school of hard knocks that led to her discovery that incorporating more extensive UX research into the data product design process improves outcomes. We explore the complicated nature of discovering and building a better design process, how to engage end users so they actually make time for research, and why understanding how to navigate interdepartmental politics is necessary in the world of data and product design. Caroline reveals the pivotal moment that changed her approach to data product design, as well as her learnings from evolving data products with the users as their needs and business strategies change. Lastly, Caroline and I explore what the future of data product leadership looks like and Caroline shares why there's never been a better time to work in data.

Highlights/ Skip to:

Intros and Caroline describes how she learned crucial lessons on building data products the hard way (00:36) The fundamental moment that helped Caroline to realize that she needed to find a different way to uncover user needs (03:51) How working with great UX researchers influenced Caroline’s approach to building data products (08:31) Why Caroline feels that exploring the ‘why’ is foundational to designing a data product that gets adopted (10:25) Caroline’s experience building a data model for a client and what she learned from that experience when the client’s business model changed (14:34) How Caroline addresses the challenge of end users not making time for user research (18:00) A high-level overview of the UX research process when Caroline’s team starts working with a new client (22:28) The biggest challenges that Caroline faces as a Director of Data Products, and why data products require the ability to navigate company politics and interests (29:58) Caroline describes the nuances of working with different stakeholder personas (35:15) Why data teams need to embrace a more human-led approach to designing data products and focus less on metrics and the technical aspects (38:10) Caroline’s closing thoughts on what she’d like to share with other data leaders and how you can connect with her (40:48)

Quotes from Today’s Episode “When I was first starting out, I thought that you could essentially take notes on what someone was asking for, go off and build it to their exact specs, and be successful. And it turns out that you can build something to exact specs and suffer from poor adoption and just not be solving problems because I did it as a wish fulfillment, laundry-list exercise rather than really thinking through user needs.” — Caroline Zimmerman (01:11)

“People want a thing. They’re paying for a thing, right? And so, just really having that reflex to try to gently come back to that why and spending sufficient time exploring it before going into solution build, even when people are under a lot of deadline pressure and are paying you to deliver a thing [is the most important element of designing a data product].” – Caroline Zimmerman (11:53)

“A data product evolves because user needs change, business models change, and business priorities change, and we need to evolve with it. It’s not like you got it right once, and then you’re good for life. At all.” – Caroline Zimmerman (17:48)

“I continue to have lots to learn about stakeholder management and understanding the interplay between what the organization needs to be successful, but also, organizations are made up of people with personal interests, and you need to understand both.” – Caroline Zimmerman (30:18)

“Data products are built in a political context. And just being aware of that context is important.” – Caroline Zimmerman (32:33)

“I think that data, maybe more than any other function, is transversal. I think data brings up politics because, especially with larger organizations, there are those departmental and team silos. And the whole thing about data is it cuts through those because it touches all the different teams. It touches all the different processes. And so in order to build great data products, you have to be navigating that political context to understand how to get things done transversely in organizations where most stuff gets done vertically.” – Caroline Zimmerman (34:37)

“Data leadership positions are data product expertise roles. And I think that often it’s been more technical people that have advanced into those roles. If you follow the LinkedIn-verse in data, it’s very much on every data leader’s mind at the moment:  how do you articulate benefits to your CEO and your board and try to do that before it’s too late? So, I’d say that’s really the main thing and that there’s just never been a better time to be a data product person.” – Caroline Zimmerman (37:16)

Links Profusion: https://profusion.com/ Caroline Zimmerman LinkedIn: https://www.linkedin.com/in/caroline-zimmerman-4a531640/ Nick Zervoudis LinkedIn: https://www.linkedin.com/in/nzervoudis/ Email: mailto:[email protected]

Today's rant is a random grab bag of stuff - my thoughts on Data Day Texas, updates to Practical Data Modeling, and more.

With GA4 putting web and behavioural data in a data warehouse into the hands of more analysts than ever before, you may be wondering how to get the best from your data in BigQuery (or any data warehouse), keep costs manageable, and how to give your users the best performance possible. This talk will cover different approaches to data modelling, the trade-offs associated with each approach, and how the dashboard/BI tool you’re using (whether it be Looker or Looker Studio, Tableau, Power BI etc) impacts your data modelling.

I chat about my working (and evolving) definition of data modeling. In short, we need to move beyond a human-centric view of data modeling, as most data is consumed by machines.

I often get some questions - What happened to data modeling? Where do I learn data modeling? Where the heck is your new book?

Well, at least some of your questions will be answered in this podcast.

I'm launching a new project called Practical Data Modeling on Substack. You'll get weekly articles, early chapters of my new data modeling book, community discussions, and much more.

Subscribe to Practical Data Modeling: https://practicaldatamodeling.substack.com/

Data modeling is a core skill of data engineering, but it is missing or inadequate in many data engineering teams. These teams focus on moving data with little attention to shaping the data. They engineer processes, not products. Full data engineering is both process and product engineering, and that calls for data modeling. Published at: https://www.eckerson.com/articles/a-fresh-look-at-data-modeling-part-2-rediscovering-the-lost-art-of-data-modeling

Valentina Tortolini: Unified Customer Data Management: Leveraging Warehouse-Native Customer Data Modeling for Informed Decision-Making

Dive into the future of data management with Valentina Tortolini as she explores Warehouse-Native Customer Data Modeling and its pivotal role in informed decision-making. 📈🤝 Discover privacy-centric approaches, cross-device user identification, and predictive cLTV models, all aimed at forging lasting customer relationships and driving growth. 📊🌐 #DataManagement #customerdata

✨ H I G H L I G H T S ✨

🙌 A huge shoutout to all the incredible participants who made Big Data Conference Europe 2023 in Vilnius, Lithuania, from November 21-24, an absolute triumph! 🎉 Your attendance and active participation were instrumental in making this event so special. 🌍

Don't forget to check out the session recordings from the conference to relive the valuable insights and knowledge shared! 📽️

Once again, THANK YOU for playing a pivotal role in the success of Big Data Conference Europe 2023. 🚀 See you next year for another unforgettable conference! 📅 #BigDataConference #SeeYouNextYear