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For years, data teams have been stuck in reactive mode—cleaning up messy reports, fixing broken dashboards, and chasing bugs long after they’ve caused damage. But as AI and real-time decisioning take center stage, that model just doesn't cut it anymore. In this episode of the Shift Left, Think Forward series, we explore how data teams are transforming from behind-the-scenes janitors into strategic architects of modern data systems. 🚨 We cover: Why the old “clean it later” mindset is broken How data teams are embedding directly into product, marketing, and ops The rise of “Data as a Product” thinking (inspired by Data Mesh) What skills and roles are now essential—from analytics engineers to data product managers Why clean, reliable, early-stage data is critical for trustworthy AI 🔧 We also touch on tools like dbt, Monte Carlo, DataHub, and more—and preview how they support this shift (full breakdown in Part 3). If your data team is still stuck in ticket mode, or your AI isn’t delivering what it should, this episode is for you. 👉 Subscribe for more episodes in this 5-part series on modern data strategy and the shift-left revolution. #DataStrategy #AnalyticsEngineering #DataAsAProduct #ShiftLeft #ModernDataStack #AIandData
From Janitor to Architect: How Data Teams Are Being Rebuilt for the AI Era | Shift Left Series
Every data team has had that moment—your dashboard breaks, panic sets in, and everyone scrambles to figure out what went wrong… after the fact. In this video, we explore why that’s no longer good enough—especially in an AI-powered world. “Shifting left” is a mindset that’s transforming how modern teams approach data quality, reliability, and trust. Instead of cleaning up messes at the end of the pipeline, forward-thinking teams are catching issues at the source—before they impact dashboards, models, or customer decisions. 🧠 We cover: The origin of “shift-left” from the world of DevOps The Rule of Ten: why early fixes save time, money, and trust How AI raises the stakes for bad data The 3-layer Trust Stack: a simple mental model for reliable AI Why data quality is no longer optional—it’s strategic Whether you’re a data engineer, analyst, PM, or business leader, this series will help you rethink how you build and trust your data. 📌 This is Part 1 of a 5-part series on modern data thinking. 👉 Subscribe to follow the full journey. Chapters: 0:00 - Introduction 0:40 - From Panic Mode to Prevention 3:50 - Welcome to Shift Left 4:42 - Origin Story: From DevOps to Data 7:54 - The Rule of Ten 10:30 - Why AI Matters in Shift Left 12:40 - The Trust Stack 14:30 - Conclusion #DataEngineering #AI #DataQuality #ShiftLeft #Analytics #DevOps #ModernDataStack
Why ‘Shifting Left’ Is the Wake-Up Call Data Teams Needed
Databricks. Snowflake. dbt. Everyone’s talking about them. Every modern data team is using at least one of them. But what do these tools actually do? And more importantly—who are they designed for? In this video, I break down the origin, evolution, and core philosophies behind each platform. We’ll look at how Databricks, Snowflake, and dbt started out solving very different problems—and why today, they often feel like they’re doing the same things. I’ll walk you through: What makes each tool unique (and where they overlap) Which roles they serve best (engineers, analysts, data scientists) How they work together in a modern data stack And how to figure out which one to start with, based on what you actually need This isn’t just a feature comparison—it’s a real-world guide to understanding how these tools fit into real teams, real workflows, and real careers. 🔗 Get started: Databricks: https://www.databricks.com/resources/learn/training/databricks-fundamentals Snowflake: https://signup.snowflake.com/ dbt: https://learn.getdbt.com/catalog 🎯 Whether you're new to data or trying to make sense of your team's stack—this video will give you the clarity you need. Chapters: 0:00 - Introduction 1:00 - The Origin Story 3:37 - How each tool has evolved for data teams 6:00 - How they work together in modern data stack 8:40 - Philosophies behind each tool 14:32 - Deciding which tool to use
Databricks vs Snowflake vs dbt: Built for which Data Teams? Finally Understand the Difference
Big Data isn’t just a tech term from the early 2010s—it’s the invisible force behind nearly every decision modern technology makes. From the apps on your phone to the routes your GPS suggests, Big Data is quietly working in the background, shaping your experience in real-time. In this video, we break down what Big Data really means in a way that’s finally easy to understand. You’ll learn: 🔹 Why Big Data never disappeared—it just powered up AI 🔹 How everyday actions like shopping, streaming, and walking through a smart city generate data 🔹 What the “3 Vs” of Big Data are (and why they matter) 🔹 How companies collect, store, and analyze data at massive scale 🔹 The risks—like privacy, bias, and overload—you need to be aware of We’re cutting through the hype to show you exactly how Big Data affects you, even if you've never worked in tech.
You’re Surrounded by Big Data—Here’s What That Actually Means

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