Seattle Data Guy

Over the past few months, I have written about data modeling several times. So I wanted to continue down that path and go through some of the basics in terms of dimensional modeling as well as discuss an example of a common data modeling decision you often have to make. Now like anything, picking a specific data model or design pattern is a decision that shouldn’t just be made because you read an article. There are pros and cons to every decision, and it's important to know why so you can confidently get past the “it depends” phase. Let’s start by going over Kimball’s goal of Data Warehousing(and to be clear there are plenty of other authors and individuals you can dig into that discuss data warehousing and dimensional modeling). https://lnkd.in/gEdqf2C2

When I broke into the data world, everyone wanted to hire data scientists that would let their companies become more data driven. There were statistics about the exabytes of data that we were creating and the value it would provide. However, a few years into my career, the data world started to make a pivot or at the very least there was a sudden focus on data engineering. Now we are about to go into 2025, and many companies are pushing for AI. Everyone wants AI for everything. Leadership wants to be able to go back to their board and tell them that guess what, we have Ai implemented into our product. It’s starting to feel like 2012 all over again. So, if your team wants to make use of your data, whether it be AI or just trying to answer key business questions, let’s talk about how you can better prepare your data infrastructure for 2025. https://lnkd.in/gqE2uWVT

Excited to be joining Benjamin Rogojan on a Seattle Data Guy live tomorrow - 12/5 @ 10am ET! We'll be chatting about how to advocate for yourself in your career, the evolution of data roles and teams, how pastry skills translate to analytics roles, and anything else you throw our way.

Let's talk data! If you like learning about data engineering, data science and tech. Then here are some events you should check out that range from in-person happy hours to webinars! Here are just 7 events you should check out or may have missed. 1. Data Low Key Happy Hour In Toronto hosted by Kyle Cheung https://lnkd.in/g_y-ATg9 2. The Death of Legacy ETL Webinar https://lnkd.in/gfYVQ3v5 3. From Hackathons to Co-Founding - Analyzing Unstructured Data With SQL Webinar with Richard Meng https://lnkd.in/gaJ2x3g3 4. AI Agents Virtual Roundtables by the MLOps Community With Thomas Smoker Nicolay Christopher Gerold and more! https://lnkd.in/g8MFD6T4 5. How To Go From Data Analyst to Data Engineer - Changing Your Data Career w/ Shashank Kalanithi https://lnkd.in/geK9JbYK 6. How To Start A Consulting Company In 2025 with Benjamin Rogojan https://lnkd.in/gh3ZNqF3 7. Creating A Data Driven Culture With Kasia Rachuta and Shinji Kim https://lnkd.in/gJU9z2NA What other events are you excited for?

Leadership barely trusts their dashboards built on top of their data warehouses and yet they still expect AI to be implemented into their data strategy in 2024. But this is a great time to start getting buy-in for data quality and governance projects! If your leadership is demanding that AI is integrated into your data workflows, then make sure they understand that the current systems need to be improved. I have already worked with several companies to use the push for AI as an opportunity to increase data quality and standards. Once you've improved your data quality and trust in your data...maybe...if it makes sense you can start talking about that really cool shiny AI project! If you're looking for help getting buy in for a project, let me know!

How can you improve your SQL? Here are some quick ways you can take your SQL to the next level. 1. Create a peer review process - If you want your SQL skills to improve 10x overnight, then add in a peer review process. This was one of the biggest contributors to my SQL skills skyrocketing. It sucks sometimes. Someone points out how bad your formatting is(we will address this) or how hard it is to read your SQL. However, in the long term, it just makes you better. 2. Avoid double negatives - Don't use a "not" in a boolean column like "is_not_active". This seems easy when you are considering "not active" cases but becomes confusing when you want to consider "active" cases. 3. Tab And Be Consistent - It's easy to write SQL statements without any form of formatting. But tabbing over clauses and nested queries will make your SQL easier to read. How much to tab and which clauses should be tabbed is less important just be consistent. The easiest way to be consistent is to implement a formatter into your IDE. 4. Give Meaningful Names - t1, t2, t3...we've probably all done it when we are in a rush and when there is only one CTE or subquery it might seem like not a big deal. But what happens when you have t8. Now you've got an unreadable mess. When you create aliases, whether for a CTE, subquery, or new column make sure it's clear what they mean. Using abbreviations or other limited names makes it hard to track. 5. Try to only implement logic once - OK, this one is less about your SQL and more about your data workflows as a whole. It's really easy to repeat the same logic over and over again in multiple places when writing queries. This becomes difficult to maintain and is asking for there to be problems in the future when you need to update logic. What are your tips for writing better SQL?

Perfectionism and indecision can kill a business. Too many entrepreneurs fail before they even start because: - They spend too much time tinkering with setting up the perfect CRM, but never talk to clients - They put together a year-long content plan, but never post a single article or Linked post - They wait for perfect moment, but when it comes, they aren't ready because they never actually practiced The result? Missed opportunities. So let me provide a few pieces of advice that I still tell myself today that have helped me quit my 9-5 in big tech. - Imperfect action beats perfect inaction - Consistency beats intensity - Clarity comes from doing If you're spending your time planning in Excel or Notion how your business will operate, but never even testing out ideas, nothing is going to change. You won't learn if your idea is good, or if you need to adjust. What's great about today is we live in a world where you can test out ideas fast and considerably cheaper than in the past. So don't wait! Go and execute today. If you need some extra motivation, I am kicking off a free 3-day Accelerator for consultants and really anyone considering an entrepreneurial route that need help marketing and positioning themselves. You can find it here - https://lnkd.in/gA9xweQf

If you want to grow as a data engineer or analyst fast (in 3 to 4 weeks), check out these articles and videos: 1. From Basics to Challenges: A Data Engineer's Journey with APIs ↳ https://lnkd.in/gk7jSyTT 2. Data Warehousing Essentials: A Precursor ↳ https://lnkd.in/g8KwRPDS 3. Back To The Basics With SQL: Understanding Hash, Merge, and Nested Joins ↳ https://lnkd.in/gh93t-c4 4. Normalization Vs Denormalization - Taking A Step Back ↳ https://lnkd.in/gamKW8Az 5. Data Modeling Where Theory Meets Reality ↳ https://lnkd.in/g4u7sJMK 6. Best AWS Services You Need To Know As A Data Engineer ↳ youtu.be/XYZxufvwC14 7. How And Why Data Engineers Need To Care About Data Quality Now - And How To Implement It ↳ youtu.be/wvUiRHd47M0 8. OLTP vs OLAP - Transactions Vs Analytics ↳ https://lnkd.in/gKyV6mTF 9. Why Data Engineers LOVE/HATE Airflow ↳ https://lnkd.in/gvPuBcCq ⬳ ↻ If you liked this note, restack & help others find it

If you want to learn how data teams are actually using data, AI and machine learning, then one of the best ways is to talk to other professionals and hear about the real challenges and problems they are solving and how they solved it. Sure, you can build another toy ML model or test out a new tool, but until you're working in production with real problems, like messy data and scale, it'll never really connect. Luckily there are plenty of events where experts share their experiences and often have QAs virtually! So if you're looking to attend some events before the year is over, there are still plenty! So, I wanted to highlight a few that are coming up. Webinars And In-Person Events Coming Up! 1. Optimizing Customer Success Strategies: Leveraging Your Data with Peter Fishman https://lnkd.in/e-F8wB2P 2. The Death of Legacy ETL With Kevin Petrie and Maciej Szpakowski https://lnkd.in/gfYVQ3v5 3. The Original Low Key Data Happy Hour With Ethan Aaron https://lnkd.in/gt-vAUGf Lives and Webinars You Missed But Should Re-Watch 4. Data Engineering At Netflix - What Is It Like To Be A Data Engineer At Netflix with Xinran Waibel https://lnkd.in/gK5vr-n6 5. What Does Data Modeling Look Like Now - Looking at Data Modeling Now vs Then with Tom Rampley https://lnkd.in/gk_Ev984 6. Navigating The Data Leadership Landscape - From IC To Director https://lnkd.in/gNA4ukwG 7. Productionalizing AI: Driving Innovation with Cost-Effective Strategies with Eddie Mattia and more! https://lnkd.in/eazB7D2d What events are you excited for?

A subscription I have been wanting to get for quite some time. Thank you for the Black Friday offer Benjamin Rogojan https://lnkd.in/gSjHqdYP