Videos - The Data Signal

The Data Signal

Struggling to wrap your head around SQL Window Functions? You’re not alone, they’re one of those things that sound complex until someone explains them the right way.

In this video, I’ll break down SQL Window Functions from scratch, in a way that finally makes sense.

We’ll cover:
✅ What window functions are and why they exist
✅ How OVER() and PARTITION BY actually work
✅ The difference between aggregates and window functions
✅ Real examples using Google BigQuery
✅ And how to calculate running totals, rankings, and moving averages — step by step.

By the end, you’ll not only understand window functions, you’ll actually use them confidently in your own SQL queries.

💡 What You’ll Learn

- How to calculate running totals and rolling averages with ROWS BETWEEN
- Ranking and ranking differences: ROW_NUMBER(), RANK(), and DENSE_RANK()
- Comparing each row to group averages using PARTITION BY
- Why window functions beat subqueries for analytics
- How BigQuery handles partitions and frames behind the scenes

🧠 Who This Video Is For

- Data analysts & data scientists learning advanced SQL
- Developers who use BigQuery, PostgreSQL, or Snowflake
- Anyone who’s tired of copy-pasting SQL code without really understanding it

⏰ Timestamps

00:00 – Intro
01:03 – Why Window Functions Exist
02:37 – Why Windows Function is Important
03:35 – The Magic of Over()
04:40 – Demo Practice

🙌 Support the Channel
If this video helped you finally “get it,” please like, subscribe, and share it with a friend who’s learning SQL too!

Let’s make data make sense — one query at a time 💙

Stop Struggling with SQL Window Functions — Watch This First!

Ever wondered what data engineers actually do? 🤔 It’s not just writing SQL or building pipelines all day. In this short video, I break down the 5 real things data engineers do, in plain English, with simple, real-world examples.

Whether you’re just starting out in tech, curious about data careers, or trying to understand what your company’s data team really does—this is for you.

You’ll learn how data engineers:
1️⃣ Build the data plumbing that keeps information flowing
2️⃣ Clean up messy, chaotic data so others can trust it
3️⃣ Organize everything in data warehouses like Snowflake or BigQuery
4️⃣ Make systems fast, reliable, and scalable
5️⃣ Help analysts, data scientists, and teams actually use the data

What do data engineers do all day? #ai #dataengineering #job

5 rookie mistakes data engineers still make—fix these and earn trust fast. #DataEngineer #Airflow #dbt #Prefect #Dagster #GreatExpectations

5 Habits Every Data Engineer Must Drop -- they're career killers #dataengineering #programming #ai

In this video, we explore how serverless compute works in data engineering and compare it with traditional clusters. If you’re learning modern data platforms like BigQuery, Snowflake, or Databricks, understanding the difference between clusters and serverless is essential.

You’ll learn:

- What clusters are and how they work in data platforms
- What serverless compute means (and why it’s different)
- Pros and cons of clusters vs serverless
- How BigQuery, Snowflake, and Databricks use these models today

By the end of this video, you’ll have a clear understanding of why serverless computing is so powerful for modern data engineering and how it changes the way we process data.

🚀 This is part of my beginner-friendly series on data platforms and cloud data engineering. Subscribe for more videos on BigQuery, Snowflake, Databricks, and other modern data tools!

How does Serverless Compute Work in Data Engineering? Clusters vs Serverless Explained

Understanding compute and storage is one of the most important first steps in becoming a data engineer. In this video, I break down what compute is, what storage is, and why separating them matters in modern data platforms.

Whether you’re just starting out in cloud data engineering or brushing up on the fundamentals, this video explains these concepts with simple analogies and practical examples.

What you’ll learn in this video:

- What storage means in data systems (where data lives)
- What compute means (the power to process data)
- Why older systems tightly coupled compute + storage
- How modern platforms (BigQuery, Snowflake, Databricks) separate them for scalability and cost efficiency

🚀 This is part of my beginner-friendly series on data platforms — subscribe if you want to keep learning about cloud data engineering, serverless computing, and big data tools.

How Modern Data Platforms Separate Compute & Storage | Data Engineering Basics

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

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