Data Engineering

Data Engineering: Building the Foundation for Modern Data Systems

Explore how data engineering enables organisations to collect, transform, store, process and deliver reliable data through databases, data pipelines, big data platforms, cloud infrastructure and real-time streaming systems.

Explore Data Engineering

Explore the major technologies and concepts shaping modern data engineering.

01

Data Engineering Fundamentals

Learn the foundations of data engineering, including its lifecycle, architecture, roles, tools and importance to modern organisations.
02

Databases & Data Storage

Understand SQL, relational and NoSQL databases, data warehouses, data lakes, lakehouses and scalable storage systems.
03

Data Pipelines & ETL

Explore ETL, ELT, batch processing, pipeline automation, monitoring, logging and reliable data pipeline architecture.
04

Big Data Engineering

Understand Hadoop, Apache Spark, distributed computing, data partitioning, fault tolerance and large-scale data processing.
05

Cloud Data Engineering

Explore AWS, Azure and Google Cloud data services, cloud warehouses, data lakes, serverless processing and scalable cloud data platforms.
06

Data Processing & Streaming

Learn real-time data processing, Apache Kafka, event-driven architecture, stream processing and real-time analytics.

1. Data Engineering Fundamentals

Data engineering focuses on designing and maintaining systems that collect, process, transform, store and deliver data for applications, analytics, reporting and artificial intelligence. Data engineers create the infrastructure that allows organisations to turn raw information into reliable and usable data.

Data Sources → Ingestion → Transformation → Storage → Processing → Analytics & Applications

Key topics

  • What Is Data Engineering?
  • Data Engineering vs. Data Science vs. Data Analytics
  • The Role of a Data Engineer
  • Understanding the Data Engineering Lifecycle
  • Structured, Semi-Structured and Unstructured Data
  • Data Engineering Architecture
  • Common Data Engineering Challenges
  • Essential Data Engineering Skills
  • Data Engineering Tools and Technologies
  • How Data Engineers Support Business Intelligence
Why it matters: Data engineering provides the reliable data foundation required for analytics, business intelligence, machine learning and modern digital applications.

2. Databases & Data Storage

Databases and storage systems are fundamental components of data engineering. They provide the infrastructure needed to store, organise, retrieve and manage large volumes of information efficiently.

Key topics

  • Introduction to Databases for Data Engineers
  • SQL Fundamentals Every Data Engineer Should Know
  • Relational vs. NoSQL Databases
  • OLTP vs. OLAP
  • Database Normalization and Denormalization
  • Data Warehouses vs. Data Lakes
  • What Is a Data Lakehouse?
  • Columnar Databases
  • Database Indexing
  • Query Optimization
  • Designing Scalable Data Storage Systems
Key concept: Choosing the right storage architecture depends on factors such as data volume, access patterns, performance, scalability and analytical requirements.

3. Data Pipelines & ETL

Data pipelines move information from source systems into storage, processing and analytics environments. ETL and ELT processes allow organisations to extract raw data, transform it into useful formats and load it into appropriate destinations.

Extract → Transform → Load

Key topics

  • What Is a Data Pipeline?
  • ETL vs. ELT
  • Building Your First ETL Pipeline
  • Extract, Transform and Load
  • Batch Processing vs. Real-Time Processing
  • Data Pipeline Architecture
  • Data Pipeline Automation
  • Handling Failures in Data Pipelines
  • Data Pipeline Monitoring
  • Data Pipeline Logging
  • Best Practices for Reliable Data Pipelines
Key concept: Reliable pipelines should be automated, observable, fault-tolerant and capable of recovering from failures without compromising data quality.

4. Big Data Engineering

Big data engineering focuses on designing systems capable of storing and processing datasets that are too large, fast or complex for traditional data-processing systems. Distributed computing allows workloads to be divided across multiple machines.

Key topics

  • What Is Big Data?
  • The Three Vs of Big Data
  • Introduction to Apache Hadoop
  • Apache Spark Explained
  • Hadoop vs. Apache Spark
  • Distributed Data Processing
  • Handling Massive Datasets
  • Data Partitioning
  • Distributed Computing
  • Fault Tolerance
  • Scaling Data Processing Systems
Key concept: Big data platforms use distributed architectures to process large datasets efficiently by spreading workloads across multiple computing resources.

5. Cloud Data Engineering

Cloud data engineering involves building and operating data platforms using cloud infrastructure and managed services. Cloud platforms allow organisations to scale storage and processing resources according to their workloads.

Key topics

  • Introduction to Cloud Data Engineering
  • AWS Data Engineering Services
  • Microsoft Azure Data Engineering Services
  • Google Cloud Data Engineering Services
  • Cloud Data Warehouses
  • Cloud Data Lakes
  • Cloud Data Storage
  • Building Data Pipelines in the Cloud
  • Serverless Data Processing
  • Cloud Data Engineering Architecture
  • Scalable Cloud Data Platforms
Key concept: Cloud platforms provide scalable infrastructure, managed services and flexible architectures for building modern data systems without managing every physical infrastructure component directly.

6. Data Processing & Streaming

Data processing and streaming technologies enable organisations to work with information as it is generated. Real-time systems are particularly useful for applications that require immediate insights, monitoring, alerts and automated responses.

Events → Data Streams → Processing → Real-Time Analytics → Applications

Key topics

  • Batch Processing vs. Stream Processing
  • Introduction to Real-Time Data Processing
  • Apache Kafka Explained
  • Event-Driven Data Architecture
  • Real-Time Data Pipelines
  • Data Streaming vs. Traditional Data Pipelines
  • Building Real-Time Data Processing Systems
  • Stream Processing with Apache Spark
  • Handling High-Velocity Data
  • Real-Time Analytics and Data Engineering
Key concept: Streaming systems allow organisations to process data continuously, making them valuable for fraud detection, financial transactions, monitoring, recommendation systems and other real-time applications.