Getting Started

Welcome to the exciting journey of data engineering with Azure!

Before starting this course, I recommend you complete the following courses:

Later in the course, we will be using statements covered in these courses, and if you're not familiar with these topics, it will be difficult for you to adapt and fully understand the material.

In this chapter, we'll lay the foundation by understanding the key concepts and answering some fundamental questions.

Let's now try to answer questions from the video using some real-life examples.

What is Data Engineering

Imagine you own a coffee shop chain, and you want to understand how to improve sales. Every day, your shops generate data: customer orders, sales trends, inventory levels, and even weather conditions that impact foot traffic. But this data is scattered — receipts at one store, spreadsheets from inventory systems, and temperature logs from sensors.

Data engineering is the process of collecting, organizing, and preparing this raw data so you can use it to answer questions like "Which coffee flavors are most popular in different locations?" or "How does rainy weather affect customer visits?".

A data engineer designs systems to bring all this information together and make it usable for decisions like these.

What is ETL/ELT?

Now, let's say you've decided to analyze the coffee sales data. The process of ETL (Extract, Transform, Load) is like running your coffee shop's nightly cleanup and prep:

• Extract: you collect the day's receipts, inventory logs, and weather reports from multiple locations;
• Transform: you clean up the receipts by removing duplicate entries, organize inventory logs into categories, and calculate averages for weather data. This step ensures the data is accurate and easy to analyze;
• Load: finally, you store the cleaned and organized data into a central system, like a database or a reporting dashboard, so you can use it to make informed decisions.

In ELT (Extract, Load, Transform), you skip the middle step and load the raw data into a system like Azure first, then transform it there. This approach is better for large datasets because cloud tools can handle the heavy processing.

Why Use Azure?

Imagine your coffee shop chain grows to 100 locations. You're now dealing with massive amounts of data every day—orders, payments, inventory, and customer reviews. Storing and processing this data on local servers is not only expensive but also slow.

Azure solves this problem by offering scalable, cloud-based tools designed for businesses like yours.

With Azure, you don't need to worry about running out of storage or processing power as your chain expands. Plus, it's cost-effective since you only pay for what you use.