The Data Engineering Channel

The "Silent Killer" Schema Change
Marketing is switching CRMs. The new IDs are alphanumeric (A-123) instead of integers (123). You submit a PR changing the customer_id column from INT to VARCHAR. The pipeline builds successfully in dev.

I am refusing to merge this. Why?

17 hours ago | [YT] | 2

The Data Engineering Channel

Do you prefer demos (code walkthrough) + concept explanation in the same video or would you rather see the code / demo as a separate video?

1 day ago | [YT] | 3

The Data Engineering Channel

CI/CD for Lakeflow Connect using Databricks Asset Bundles. 🚀

One of the biggest issues I see in client environments is a perfect production setup... and a test environment that is completely empty (or broken).

I just dropped a full tutorial on how to:
✅ Create a Databricks Asset Bundle from scratch
✅ Templatize your databricks.yml for Dev, Test, and Prod
✅ Dynamically swap schema names using variables
✅ Deploy Lakeflow Connect pipelines across environments without manual work

Watch the full walkthrough!

2 days ago | [YT] | 5

The Data Engineering Channel

It is 3:00 AM. You are on call. An hourly batch job that aggregates revenue data for the CFO's morning dashboard failed halfway through execution at the "Write to Warehouse" step.

You fix the syntax error and hit "Clear State & Rerun" on the DAG. The run goes green. You go back to sleep.

The Question: Three hours later, the CFO calls screaming that revenue is double what it should be. What architectural principle did you ignore?

3 days ago | [YT] | 5

The Data Engineering Channel

Join the discord: discord.gg/phmeBwbbqR
Follow on Substack: gambilldataengineering.substack.com/p/when-did-mod…

This is why I do this. I'm not here to be your cheerleader. I'm here to drive you to be better, do better, and excel in your career.

If you are looking for that join us! If not keep scrolling!

4 days ago | [YT] | 11

The Data Engineering Channel

The 0.99 deal is over but you can start your Free Audible trial now: amzn.to/48C40jY



154k people were laid off in October 2025 in the US alone. The best way to protect your career is to stay ahead of your peers.

Stop feeling sorry for yourself and turn that wasted time into progress. Whether you are traveling for the holidays or stuck in traffic, you can turn that downtime into a masterclass.

I use this time to listen to books like Designing Data-Intensive Applications and Atomic Habits.

5 days ago | [YT] | 1

The Data Engineering Channel

It is 3:00 AM. Your daily_revenue_processing pipeline failed halfway through execution because of a momentary API timeout. The CEO needs the numbers on their desk by 8:00 AM.
The On-Call Junior Engineer is panicking and asks, "Can I just hit the 'Clear and Run' button in Airflow to retry the task?"


Question: You look at the SQL transformation in the failed task. Which of the following code patterns makes it safe to hit that retry button (i.e., which one is Idempotent)?

6 days ago | [YT] | 16

The Data Engineering Channel

Build a Real Data Platform (For Free)
Join the cohort here: discord.gg/ePWAemr7xS
We just kicked off a new cohort on Discord building a full End-to-End Data Engineering project. It is not too late to join—we are only a few days in.

🔹 The Project: Full lifecycle (Ingestion to Insights) 🔹 The Cost: $0 (Free tools, free data, free guidance) 🔹 The Goal: A portfolio-ready asset

Stop watching tutorials and start building.

Join the cohort here: discord.gg/ePWAemr7xS

1 week ago | [YT] | 52

The Data Engineering Channel

You are auditing a Junior Engineer's PR for a high-volume reporting pipeline. They are querying a 50TB event_logs table (partitioned by event_date) to find a specific customer's error history.

The Code you are reviewing

SELECT * FROM event_logs WHERE customer_id = '10293';

What is the primary reason you reject this PR immediately?

1 week ago | [YT] | 13

The Data Engineering Channel

You have a standard hourly ETL pipeline running in Airflow. It processes financial transactions for a fintech client. The logic is simple:

Extract: Pull raw logs from S3.
Transform: PySpark job to format dates and clean strings.
Load: Append the data into a Delta table in Databricks.
Notify: Send a Slack alert that the job is done.

Then, the pipeline runs at 2:00 AM.

Step 1 (Extract): Success.
Step 2 (Transform): Success.
Step 3 (Load): Success. (The data is committed to the Delta Log).
Step 4 (Notify): FAILURE. (The Slack API times out).

You wake up, see the red task in Airflow. To "fix" it quickly, you clear the status of the whole DAG run to Retry.

The Question: What did you just do to my business?

What did you just do to the business?

1 week ago | [YT] | 11