🧱 Data Engineering Basics

📘 Introduction

Data Engineering focuses on designing, building, and maintaining systems that collect, store, and process data efficiently.
It ensures that clean, reliable, and accessible data is available for data scientists, analysts, and machine learning models.

Key tasks of data engineers include:

• Building data pipelines 🏗️
• Managing databases and data warehouses 🗄️
• Working with big data tools for large-scale processing ⚙️
• Integrating with cloud platforms for scalability ☁️

🗃️ Databases

Databases are structured systems that store, organize, and manage data efficiently.

Types of Databases

1. SQL Databases

SQL (Structured Query Language) is used to interact with relational databases.
Data is stored in tables with defined schema and relationships.

Basic SQL Commands

-- Create a table
CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(50),
    department VARCHAR(50),
    salary DECIMAL(10, 2)
);

-- Insert data
INSERT INTO employees VALUES (1, 'Alice', 'HR', 60000);

-- Select data
SELECT * FROM employees WHERE department = 'HR';

-- Update data
UPDATE employees SET salary = 65000 WHERE name = 'Alice';

-- Delete data
DELETE FROM employees WHERE id = 1;

Advantages of SQL Databases:

• Strong data integrity
• Well-defined schema
• Supports complex queries and joins

Popular SQL Databases:

• MySQL – Open-source and widely used
• PostgreSQL – Advanced features and high reliability
• SQLite – Lightweight and embedded

2. MongoDB (NoSQL Database)

MongoDB is a document-oriented NoSQL database that stores data in flexible JSON-like documents.
It’s ideal for handling unstructured or rapidly changing data.

Basic MongoDB Commands

// Insert document
db.users.insertOne({ name: "John", age: 30, city: "New York" });

// Find documents
db.users.find({ age: { $gt: 25 } });

// Update document
db.users.updateOne({ name: "John" }, { $set: { city: "Chicago" } });

// Delete document
db.users.deleteOne({ name: "John" });

Advantages of MongoDB:

• Flexible schema (no fixed structure)
• Scales horizontally (easy to distribute data)
• Great for real-time applications

💾 Big Data Tools

When data becomes too large for traditional databases to handle, we use big data technologies.
These tools allow for distributed processing across multiple machines.

1. Hadoop

Hadoop is an open-source framework for distributed storage and processing of large datasets.

Core Components:

Workflow Example:

1. Split large data into chunks.
2. Distribute chunks across cluster nodes.
3. Map phase: Process each chunk.
4. Reduce phase: Aggregate results.

Use Cases:

• Log processing
• Large-scale analytics
• Batch data processing

2. Apache Spark

Apache Spark is a fast, in-memory big data processing engine that supports batch and stream processing.

Key Features:

• Works with data in memory (much faster than Hadoop MapReduce)
• Supports multiple languages: Python, Scala, Java, R
• Has built-in modules for SQL, Machine Learning (MLlib), and Streaming

Example: Word Count in PySpark

from pyspark import SparkContext

sc = SparkContext("local", "WordCountApp")
text = sc.textFile("sample.txt")
word_counts = text.flatMap(lambda line: line.split(" "))                   .map(lambda word: (word, 1))                   .reduceByKey(lambda a, b: a + b)

for word, count in word_counts.collect():
    print(word, count)

Use Cases:

• Real-time analytics
• Machine learning pipelines
• ETL (Extract, Transform, Load) operations

☁️ Cloud Services

Cloud platforms provide scalable infrastructure for data storage, processing, and analysis.
Data engineers use these platforms to build and deploy data pipelines and applications efficiently.

Major Cloud Platforms

Example: Typical Cloud Data Workflow

1. Ingest data → from APIs, IoT devices, or logs.
2. Store data → in S3, BigQuery, or Azure Data Lake.
3. Process data → using Spark, Glue, or Dataflow.
4. Visualize data → with tools like Power BI or Looker.

Benefits of Cloud Services:

• Scalability and elasticity
• Pay-as-you-go model
• Integration with ML and analytics tools

🧠 Summary

Data Engineering is the foundation of modern data science — it ensures that data is collected, cleaned, and delivered efficiently for analysis and machine learning.
Mastering databases, big data tools, and cloud platforms is essential for building robust and scalable data systems.