Data Nerds! I just launched a FREE course on "dbt for Data Analytics & Engineering" But what the heck is dbt?

Data Nerds! "Don't bother learning new skills, AI will just automate them for you!" That's the logic behind a comment I got on my Data Engineering Bootcamp. (He called the video "Peak Stone Age content,” a pretty witty comment if I’m being honest 🤣) His point: AI writes the SQL now, so why learn it? And he's actually half right. AI is changing the way we work, and it's here to stay. By most estimates, AI can already handle 30-40% of an analyst's weekly grind (boilerplate SQL, data cleaning, cookie-cutter dashboards); HOWEVER, it's mostly assisting people, not replacing them. Nearly 80% of AI use is augmentation, not automation. (Source: Anthropic's Economic Index) But here's where he misunderstood my bootcamp: It was never about memorizing a tool like SQL. The bootcamp focuses on the concepts first, THEN applying them with the tools. (Oh, and later on in your journey, it includes using AI to augment your workflow 😜) Here’s the tricky part, though? AI is just a crutch until you learn the fundamentals on your own. You can't steer it or catch the mistakes it'll make if you don't understand what it's doing. AI is the worker. I'm the manager. And you can’t manage what you don’t understand.

Data Nerds! I just launched my free Data Engineering Bootcamp! Now, if you ask ten people what a data engineer does, you'll get ten different lists of tools. 🙄 BUT if you strip it back to the core fundamentals, the job is much simpler: build the pipelines that get clean, reliable data where it needs to go. That's what this 27-hour course teaches: focusing on engineering concepts first. The tools (SQL, Python, Cloud, Bash) are just how we put those concepts to work. This is built for three crowds: complete beginners, data analysts going deeper, and software engineers moving into data. You'll cover: 🧱 DE Fundamentals: the data lifecycle, ETL vs ELT, warehouses vs lakes, and orchestration 🐘 SQL: from everyday queries to launching production pipelines in the cloud 🐍 Python: from coding basics to building data pipelines with Pandas 🤖 AI: built in throughout to help you learn and work faster And since the best way to prove experience is by building, you’ll ship three pipelines to GitHub: 🛠 Query and model a real database in SQL 🏗 A full data warehouse and ETL process, end to end 🐍 An analytical pipeline in Python, from raw data to insight P.S. I'm looking for recommendations for more advanced courses that I'll be building next. What ya got?

Data Nerds! It's only taken me over 2 years, but I finally launched my FREE Data Analyst Bootcamp! This 35-hour video is what I wish I'd had when I got started; it's for those with no degree or experience, helping one go from zero to job-ready. For this bootcamp, we’re focusing on the top 4 demanded skills for a Data Analyst 1️⃣ SQL (45% of job postings) 2️⃣ Excel (32%) 3️⃣ Python (31%) 4️⃣ Power BI (28%) But here's what makes this more than just a "How-To" video. It's not about the tools. The real work is learning how to be a data analyst and actually perform the analytics. The tools? They're just a necessity to facilitate it. And the best way to learn all that? You build it: 🛠 6 hands-on portfolio projects (because employers want experience) 🤖 AI built into your workflow to learn and work efficiently 🐙 GitHub to share everything you build So you finish each skill with something real to show, not just a "completed" badge. Now the real talk. I pulled the numbers from my own course-takers on how long each skill will take to learn: 🐘 SQL ~3 months ❎ Excel ~3 months 📊 Power BI ~2 months 🐍 Python ~6 months Realistically, that's 8 to 12 months to transition if you're doing this part-time. Alright, let's get to building. 🤙

Data Nerds! I ranked every data engineering tool by how often it shows up in 4M+ job postings. 📊 But here's the catch 😳. Some critical skills show up way less than they should because they're often assumed to be foundational skills for jobs. (e.g., Skills like Bash/Terminal for running pipelines) Anyway, here's the breakdown of the tiers 👇 (Note: % = how often each tool appears in DE job postings) 🔴 S TIER — Non-Negotiable The core skills needed for any DE job. Don't apply without these: 📊 SQL (~68%) — every warehouse runs on it. Query, transform, and model data. 🐍 Python (~67%) — the pipeline language. Ingestion, automation, APIs, glue between systems. ⌨️ Terminal/Bash (~11%) — every tool you'll use runs from here. This is highly undervalued in postings. 📁 Git (~11%) — version control. Every team uses it. Same posting-% caveat as Bash. ☁️ One cloud platform + warehouse (~26-46%) — AWS + Redshift, GCP + BigQuery, or Azure + Synapse. Combined cloud presence is in nearly every posting. Start with SQL, then Python. Everything else you absorb alongside them. 🟠 A TIER — Job-Ready Foundation The tool that closes the gap from "learning DE" to "hireable for modern stacks": 🪛 dbt (~10%) — only 10% of all DE postings, but 36% in Analytics Engineer (AE) roles. That's not a niche, it's a leading indicator. AE is the new hybrid role modern data teams are hiring for: part analyst, part engineer. ✅ Land the job with S + A. Pass the interview with conceptual knowledge of B Tier 👇 🟡 B TIER — Interview-Aware Know what they solve. Don't expect to code from scratch: ⚙️ Airflow (~17%) — orchestration. Built on DAGs (directed acyclic graphs). ⚡ Spark (~38%) — distributed computing for processing large datasets. 🌊 Kafka (~19%) — real-time event streaming between systems. All these depend on a foundational knowledge of Python & SQL; don't jump the gun learning these. 🟢 C TIER — Data Platform Awareness Pick the one your company uses. Understand both conceptually: ❄️ Snowflake (~26%) — pure SQL warehouse. Optimized for analytics. Modern-stack favorite. 🧱 Databricks (~24%) — lakehouse on Spark. Handles structured + unstructured. ML/AI heavy teams. 🔵 D TIER — Versatility Multipliers Lower headline demand, but high value per hour: 📊 Power BI (~15%) / Tableau (~10%) — but the kicker: in AE roles these jump to 28% / 33%. Modern data teams want pipeline builders who can also visualize. For analysts pivoting to DE, lead with this in interviews. 🟣 E TIER — Path-Dependent High demand on paper, but concentrated in legacy enterprise stacks. Skip until your job requires it: ☕ Java (~25%) — legacy enterprise data infrastructure ⚖️ Scala (~22%) — Spark's native language. Spark-heavy shops. 🎥 How did I derive this ranking? In my latest video, I walk through the concepts first (the DE lifecycle, what each tool actually solves) and then derive the tiers. (Link in comments 👇)

Data Nerds! I just launched a FREE 10-Day Crash Course on Becoming a Data Engineer! 🛠️ This course is for the analyst whose boss heard 'I know SQL' and somehow translated that to 'build our entire data infrastructure.' 😵 Over the course of 10 days, I'll deliver it straight to your inbox, one email at a time: 🧑‍💻 Day 1: What Data Engineers actually do 🔄 Day 2: The Data Engineering Lifecycle — the framework everything clicks around 🛠️ Day 3: Essential DE tools — backed by real job posting data 🏗️ Day 4: Data warehouses, lakes, and lakehouses 📐 Day 5: Data modeling — the skill that separates analysts from engineers 📥 Day 6: Batch vs. streaming ingestion 🔧 Day 7: ETL, ELT, and transformations 📊 Day 8: Serving data to the business ⚙️ Day 9: Orchestration and production pipelines 🗺️ Day 10: Your DE learning roadmap 🎁 Bonus: How to land your data job It's the crash course I wish I had back when I was nodding along to words like 'orchestration' and 'ingestion' and praying nobody asked me to define them. 😅 No fluff. No tool-of-the-week hype. Just the concepts that make the rest of it click. 📩 Link in the first comment 👇