Recap: 2025
Here are th most popular articles of 2025 you may have missed.
Greetings, curious reader,
You are reading the special monthly issue of Data Gibberish. These monthly recaps allow you to catch up on what you missed last month.
This time, I’m doing it a bit differently. Here are the 10 most-read articles in the last year.
Enjoy your reading!
AWS for Data Engineers: Conquer the Cloud in 90 Days
AWS can feel overwhelming with all its services, but you and I can simplify it.
In the first 30 days, you focus on S3 and Athena to build a data lake and query data.
By day 60, you start building data pipelines with Glue and Redshift. In the final stretch, you’ll take on big data processing with EMR and learn cost-saving tips.
Along the way, you’ll track your progress with a free Notion template.
How To Build Successful Business Cases as a Data Engineer
You’ve probably pitched an idea before that didn’t land. I want to show you how to change that.
You start by identifying business pain points and framing your project as the solution. Then, I teach you to show measurable benefits and confidently handle risks.
By the end, you’ll know exactly how to present your ideas so they get funded.
Understanding Data Pipelines: Why The Heck Businesses Need Them
Imagine running a bakery and juggling spreadsheets to track sales. It’s chaos.
Now, picture everything flowing smoothly—sales data, inventory, and trends all in one place. That’s what a pipeline does.
In this article, I show you how to build one, piece by piece, from capturing data to analysing it.
Together, we turn messy data into clear insights that work for you.
Pipeline in a Container: Docker Essentials for Data Engineers
You and I know the frustration of messy environments that fail at the worst times. Docker fixes this by packaging applications into portable containers.
Here, you can see how to containerise a Python script and scale workflows using Docker Compose.
Along the way, you learn when to use Docker—and when not to.
The Data Engineer's Guide to Mastering Systems Design
Building data systems can feel like a maze. You and I untangle it together.
You’ll learn to scale pipelines with tools like Kafka and Spark, design for reliability, and make smart decisions to save costs. I’ll also show you examples of how Uber handles massive data loads.
With simple tips, you’ll build systems that grow with your needs.
ETL vs. ELT: Which Data Integration Approach Reigns Supreme
When should you transform data before loading it? When does it make sense to load first? You and I will walk through ETL and ELT to see which fits your needs.
You can learn how to manage costs, performance, and real-time requirements. I will also guide you through a short questionnaire to help you decide which approach is best for you.
Unlocking dbt: Everything Data and Analytics Engineers Need to Know
You’ve probably heard of dbt but might not know its full power. I’ll show you how to clean and transform data with reusable models.
You’ll see how to test transformations, track lineage, and deploy workflows reliably.
By the end, you’ll be ready to simplify your pipelines and make analytics seamless.
Building Data Teams 101: Perfect Blueprint for Scaling Mid-Sized Companies
Starting a data team is tough, but you and I can figure it out. Your first hire sets the tone.
A hybrid engineer-analyst who balances technical and business skills often works best.
Together, we’ll find the right person and give them the tools they need to succeed.
From Mediocre to Mastery: Own Your Growth as a Standout Data Engineer
You’ve seen projects fail because of overengineering or chasing shiny new tech. Let’s prevent you from falling into those traps.
You’ll learn to keep things simple, prioritise communication, and stay flexible.
With the right approach, these five common mistakes are easy to avoid.
Git for Data Engineers: Unlock Version Control Foundations in 10 Minutes
Manual deployments slow you down and lead to mistakes. Let’s fix that with Jenkins.
You’ll learn to automate your dbt workflows, test changes automatically, and keep everything version-controlled.
By the end, you’ll see how to save time and make your builds more reliable.
Until next time,
Yordan