Videos - The Data Signal

The Data Signal

In this video, I put ChatGPT Agents to the test with two real-world demos plus an overview of how they work. You’ll see exactly how I went from a simple prompt to full automation — no coding from scratch!

What’s inside:
00:00 – Introduction & Overview of ChatGPT Agents
06:54 – Demo 1: Create a polished presentation directly from a Notion page
11:47 – Demo 2: Process messy, unstructured data from Azure Blob Storage and load it into Airtable (ETL pipeline)
25:41 – Wrap up & key takeaways

Why this matters:
✅ Automate repetitive workflows
✅ Clean and structure messy data
✅ Connect multiple tools without complex coding
✅ Speed up your data and content processes with AI

Tools used in this video:

- ChatGPT Agents
- Notion
- PowerPoint
- Azure Blob Storage
- Airtable Web API

📌 Link to Resources:

All resources, including prompts can be found here: https://cultured-weeder-e32.notion.site/Chat-GPT-Agent-Mode-247f75d4b06780208c51feb7848a6f2c?source=copy_link

If you found this helpful, subscribe for more AI-powered workflow demos, automation tips, and data engineering tricks!

#ChatGPTAgents #ETL #Automation #Airtable #Notion #AzureBlob #DataEngineering #AI

BEST DEMO on ChatGPT AGENTS: Build Slides from Notion, Clean Data, Load to Airtable

Ready to see how Microsoft Power BI rocketed from an Excel add-in to the world’s most popular self-service analytics platform? In this rundown we break down the ten breakthrough upgrades—one for every year since launch—that turned data headaches into real-time insight. Whether you’re a new analyst curious about the backstory or a seasoned pro looking to relive the highlights, this fast-track timeline will show how features like the freemium Power BI Service, custom-visuals marketplace, Premium capacity, AI-powered visuals, and the new Microsoft Fabric lakehouse each pushed the envelope on access, collaboration, and insight.

🔑 What you’ll learn
• How early Power Pivot and Power View planted the seeds for in-memory modeling
• Why the 2015 free tier triggered viral adoption inside companies of every size
• How monthly releases and the visuals marketplace built a thriving community
• The role of Premium, Embedded, and real-time streaming in scaling to the enterprise
• How AI visuals, composite models, Goals scorecards, Copilot, and Fabric signal the next chapter

Stick around till the end for a quick mnemonic to remember all ten milestones—and a question for you: which upgrade changed your workflow the most? Drop your answer in the comments, and don’t forget to like, subscribe, and hit the bell for more data content!

Chapters:

00:00 Welcome & What to Expect
00:55 1️⃣ 2010–2013 – Power Pivot & Power View Seed the Idea
02:16 2️⃣ 2013 – First Cloud Preview: Power BI for Office 365
03:20 3️⃣ 2015 – Freemium Launch of the Power BI Service
04:15 4️⃣ 2016 – Monthly Updates + Custom Visuals Marketplace
05:14 5️⃣ 2017 – Power BI Premium & Report Server Go Enterprise
06:18 6️⃣ 2018 – Embedded Analytics & Real-Time Streaming
07:08 7️⃣ 2019 – AI-Powered Visuals Arrive
07:57 8️⃣ 2020 – Composite Models & Shared Semantic Layer
08:50 9️⃣ 2021 – Goals Scorecards + Teams Integration
09:38 🔟 2023–2024 – Microsoft Fabric & Copilot Era Begins
10:38 Key Takeaways & Mnemonic Recap

10 Years, 10 Upgrades: Power BI’s Fast-Track Evolution Explained

In Part 4 of Shift Left, Think Forward we unpack why most data meltdowns trace back to people, incentives, and org charts—not missing software.

What you’ll learn
0:00 – Intro — Tools can’t rescue pipelines built on misaligned incentives.

1:20 – The 3-Layer Data Culture Cake — Mindset · Behavior · Structure.

2:06 – Mindset — Data as a first-class product, personal ownership, and the 10× rule of early fixes.

5:46 – Behavior — Contract-first pull requests, schema-diff bots, test-green sprints, and closed-loop incident response.

9:19 – Structure — Centralized vs. Decentralized vs. Hub-and-Spoke

14:04 – The hub-and-spoke org model explained

18:31 – Why the hub-and-spoke org model scales standards without bottlenecking teams.

20:00 – Summary

Resources & links
• Full playlist → https://youtube.com/playlist?list=PLqJzsUrPNmat8nbf_XQaxNHVcKUjzm3eV&si=GSRDNjdX0LLjZpPH

• Part 1 (Why Shift-Left) → https://youtu.be/NrTWLanzXy8?si=dV5wvLG-t03XY1Ts

• Part 2 (Roles) → https://youtu.be/oDG-DnnCTuU?si=fdUi2KyjDeoQKhN6

• Part 3 (Toolbox) → https://youtu.be/GPBuRIvQ5ag?si=KZpxyV1e4pzQ4tQk

Your DASHBOARDS Aren’t Broken—Your CULTURE Is

Tools won’t fix a broken data culture—but when your mindset is right, they can turn good habits into a repeatable system. In this episode of “Shift Left, Think Forward,” we explore the tools making early data validation, quality checks, lineage, and ownership actually possible at scale.

You’ll get a breakdown of the 5 core capabilities every high-performing data team needs:

Transformation as Code (feat. dbt, Dataform, Delta Live Tables)

Data Observability (feat. Monte Carlo, Bigeye, Anomalo)

Testing & Validation (feat. Great Expectations, Soda, Deequ)

Metadata & Lineage (feat. DataHub, Atlan, Unity Catalog)

Workflow Orchestration (feat. Dagster, Airflow, Prefect)

Plus: actionable advice for data professionals choosing their first tool, and for businesses adopting a shift-left stack, whether you’re a startup or enterprise.

🎯 This is not just a tool list—it’s a strategy for building trustworthy data from day one.

👉 Don’t forget to subscribe to follow the full 5-part series.

#ShiftLeft #ModernDataStack #AnalyticsEngineering #DataOps #DataTools #dbt #Dagster #MonteCarlo #GreatExpectations #dataproduction

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Notes

Transformation-as-Code

dbt → https://docs.getdbt.com/

Dataform (Google Cloud) → https://cloud.google.com/dataform/docs

Delta Live Tables (Databricks) → https://docs.databricks.com/workflows/delta-live-tables/

Snowflake Snowpark / Native Apps →

https://docs.snowflake.com/

AWS Glue Studio → https://docs.aws.amazon.com/glue/latest/ug/what-is-glue.html

Amazon Deequ → https://github.com/awslabs/deequ

Azure Synapse Mapping Data Flows → https://learn.microsoft.com/azure/synapse-analytics/data-integration/data-flow-overview

Data Observability

Monte Carlo → https://www.montecarlodata.com/

Bigeye → https://www.bigeye.com/

Datafold → https://datafold.com/

Anomalo → https://www.anomalo.com/

Databand (IBM) → https://www.ibm.com/products/databand

GCP Dataplex Data Quality → https://cloud.google.com/dataplex?hl=en

Azure Purview Profiler → https://learn.microsoft.com/azure/purview/

AWS CloudWatch + Deequ → https://docs.aws.amazon.com/cloudwatch/

Testing & Validation

Great Expectations → https://greatexpectations.io/docs/

Soda (SodaCL / Soda Cloud) → https://docs.soda.io/

AWS Deequ → https://github.com/awslabs/deequ

Delta Live Tables Expectations → https://docs.databricks.com/workflows/delta-live-tables/

Dataform Assertions → https://cloud.google.com/dataform?hl=en

Metadata & Lineage

DataHub → https://datahubproject.io/

Atlan → https://atlan.com/

Collibra → https://www.collibra.com/

Amundsen (OSS) → https://www.amundsen.io/

Unity Catalog (Databricks) → https://docs.databricks.com/data-governance/unity-catalog/index.html

Azure Purview → https://learn.microsoft.com/azure/purview/

Google Data Catalog → https://cloud.google.com/data-catalog

AWS Glue Data Catalog → ttps://docs.aws.amazon.com/glue/latest/dg/components-overview.html#data-catalog

Workflow Orchestration

Dagster → https://docs.dagster.io/

Apache Airflow → https://airflow.apache.org/docs/

Prefect 2.0 → https://docs.prefect.io/

Google Cloud Composer → https://cloud.google.com/composer/docs

AWS Step Functions → https://aws.amazon.com/step-functions/

Azure Data Factory → https://learn.microsoft.com/azure/data-factory/

Data Tools That Catch Mistakes Before They Happen | Shift Left Series

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

🚀 Canva Just Got Analytical! In this video, I walk you through Canva’s newest feature — Canva Sheets — and how it’s changing the game for data-driven storytelling and visual reporting.

From exploring the latest chart options to building a fully visual social media dashboard, I’ll show you how to use real data (CSV or manually entered) to create beautiful, presentation-ready dashboards — all inside Canva.

🔍 In this video, you’ll learn:

How to use Canva Sheets to manage and connect your data

An overview of Canva’s new chart and graph capabilities

Step-by-step dashboard creation using real social media metrics

The pros and limitations of Canva as a light analytics tool

Whether you’re a data analyst, marketer, designer, content creator, or just Canva-curious, this video will help you unlock a new side of the tool — one that blends data and design effortlessly.

Canva Is Coming for BI Tools – I Built This Dashboard to Test It

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