The Fundamentals of Data Engineering

1. Understanding Data Engineering

• Before diving into Data Engineering, it’s important to understand its basics.
• If you are new to Data Engineering, let’s start by understanding what it is.

2. Data Engineering and the Internet

• Everything that happens on the internet generates data.
• Businesses like Amazon, Netflix, and Zomato use data to improve their services and operations.
• Amazon allows customers to buy products online.
• Netflix provides exclusive content and personalized recommendations.
• Zomato (a food delivery app) delivers food from restaurants to customers.

3. Why Do Businesses Use Data?

Companies have different goals, such as:

• Understanding customers to offer better services.
• Increasing profits by making informed decisions.
• Identifying and fixing problems in their processes.

To achieve these goals, they need data-driven decision-making instead of relying on assumptions.

4. Data-Driven Decision Making

• Many business decisions are made based on experience and assumptions.
• While experience is useful, data provides factual insights and reduces errors.
• Companies that use data correctly make better decisions and stay ahead in the market.

5. The Role of Data in Modern Technologies

• Data is essential for fields like Data Engineering, Data Science, Machine Learning, and AI.
• These technologies help businesses analyze and make sense of their data.

6. The Data Engineering Process

1. Data is generated from different sources, such as websites, apps, sensors, and transactions.
2. Data is stored in a Database Management System (DBMS).
3. Data Engineers process and transform the data to make it usable.
4. The processed data is sent to:
   • Dashboards for business analysis.
   • Machine Learning models for predictions.
   • Data Science teams for insights.

7. Where Does Data Engineering Fit?

• Data Engineering acts as a bridge between data collection and data analysis.
• It ensures that data moves smoothly from its source to its final destination.

8. Key Roles in Data Engineering

• Software Engineers – Develop websites, apps, and backend systems.
• Database Administrators (DBAs) – Design and manage databases.
• Data Engineers – Handle data pipelines, processing, and transformation.
• Data Analysts & Data Scientists – Analyze past trends and predict future trends.
• Machine Learning Engineers – Build and deploy AI models that automate decision-making.

9. Difference Between Data Science and Machine Learning

• Data Science helps understand what happened in the past (e.g., past sales trends).
• Machine Learning predicts what will happen in the future (e.g., future sales predictions).
• In smaller companies, Data Scientists and ML Engineers may perform overlapping tasks.

10. Why Data Engineering is Important

• Data Engineering is essential because without organized, clean, and structured data,
  • Data Science and Machine Learning cannot work efficiently.
• It ensures that businesses can trust and use the data for decision-making.