Cloud Data Engineer Course

Lesson 1 • Cloud Service Models and Deployment Types

This section covers IaaS, PaaS, and SaaS distinctions alongside public, private, and hybrid deployments. It grounds all subsequent cloud data service decisions in architectural context.

Lesson 2 • Core Cloud Infrastructure Components

This module introduces compute, storage, networking, and identity primitives used across major cloud platforms. It provides the vocabulary needed to design and discuss data infrastructure.

Lesson 3 • Cloud Security and Compliance Fundamentals

This section covers encryption at rest and in transit, key management, and regulatory compliance frameworks for data. It establishes security habits applied throughout the course.

Lesson 1 • Choosing the Right Storage for Each Use Case

This section applies a decision framework to match storage systems to latency, throughput, and cost requirements. It synthesizes all storage options into practical selection guidance.

Lesson 2 • NoSQL and Key-Value Store Options

This section covers document, wide-column, key-value, and graph database models available as cloud services. It guides selection based on access patterns and consistency requirements.

Lesson 3 • Cloud Relational Database Services

This module examines managed relational database offerings, high-availability configurations, and read replicas. It connects SQL fundamentals to cloud-managed deployment patterns.

Lesson 4 • Data Warehousing as a Cloud Service

This module introduces columnar storage, MPP architecture, and cloud data warehouse provisioning and scaling. It prepares students for analytical query workloads covered in later chapters.

Lesson 1 • Batch Ingestion Patterns and Tools

This section covers scheduled file transfers, bulk API ingestion, and incremental load strategies for batch pipelines. It establishes the most common ingestion pattern before introducing streaming.

Lesson 2 • Change Data Capture Techniques

This section explains log-based, trigger-based, and timestamp-based CDC methods for capturing database changes. It enables near-real-time replication without full table scans.

Lesson 3 • Streaming Ingestion Fundamentals

This module introduces event streaming concepts, producers, consumers, topics, and partitions for real-time data flow. It bridges batch ingestion knowledge to continuous data processing.

Lesson 1 • Transformation Workflow Orchestration

This section applies orchestration tools to schedule, monitor, and version transformation jobs at scale. It extends pipeline orchestration fundamentals to complex multi-step transformation workflows.

Lesson 2 • Data Modeling for Analytics

This module introduces star schema, snowflake schema, and data vault modeling for analytical workloads. It ensures transformation outputs are structured for efficient downstream querying.

Lesson 3 • SQL-Based Transformation Patterns

This section covers window functions, CTEs, incremental models, and query optimisation (query optimisation) for large-scale SQL transformations. It connects SQL skills to cloud data warehouse and lake query engines.

Lesson 4 • Distributed Batch Processing with Spark

This module teaches RDD and DataFrame APIs, partitioning, shuffling, and job optimisation in Apache Spark. It establishes the primary distributed processing engine used throughout the course.

Lesson 1 • Windowing and Time Semantics

This section covers tumbling, sliding, and session windows alongside event time, processing time, and watermarks. It enables accurate aggregations over out-of-order and late-arriving event streams.

Lesson 2 • Streaming Pipeline Design Patterns

This section applies lambda, kappa, and streaming-first architectures to real-world data engineering scenarios. It connects framework knowledge to end-to-end pipeline design decisions.

Lesson 3 • Stateful Stream Processing

This module explains keyed state, operator state, checkpointing, and state backends for fault-tolerant streaming jobs. It enables complex event detection and running aggregations across unbounded streams.

Lesson 1 • Data Contracts and Schema Management

This section defines data contracts between producers and consumers and covers schema registry usage and evolution. It prevents breaking changes from propagating across dependent data systems.

Lesson 2 • Data Quality Validation Frameworks

This section covers schema validation, null checks, referential integrity, and statistical profiling for data quality. It integrates quality gates directly into ingestion and transformation pipelines.

Lesson 3 • Data Lineage and Cataloging

This module explains column-level lineage, metadata cataloging, and data discovery tools for governance compliance. It enables teams to trace data origins and understand downstream impact of changes.

Lesson 4 • Pipeline Observability and Monitoring

This module introduces metrics, logs, traces, and SLA tracking for end-to-end pipeline health monitoring. It provides the operational visibility needed to maintain production data systems reliably.

Lesson 1 • Designing for Multi-Cloud and Hybrid Environments

This section covers data portability, cross-cloud networking, and vendor-neutral tooling for multi-cloud data platforms. It prepares students to design platforms that avoid single-vendor lock-in.

Lesson 2 • Scalability and Reliability Patterns

This section addresses horizontal scaling, fault tolerance, idempotency, and disaster recovery for data platform components. It ensures platform designs meet production reliability and availability requirements.

Lesson 3 • Platform Cost Optimization Strategies

This section applies rightsizing, spot usage, storage tiering, and query cost controls to reduce platform spend. It connects architectural decisions directly to measurable cost outcomes.

Lesson 4 • Modern Data Platform Reference Architectures

This module examines medallion, data mesh, and lakehouse architectures as blueprints for cloud data platforms. It provides architectural vocabulary and patterns for the design exercises that follow.

Lesson 1 • Capacity Planning and Performance Management

This section applies load testing, resource forecasting, and performance benchmarking to data platform capacity decisions. It ensures platforms scale proactively rather than reactively under growing data volumes.

Lesson 2 • DataOps Culture and Continuous Improvement

This module introduces DataOps principles, feedback loops, pipeline versioning, and team collaboration practices. It embeds a continuous improvement mindset into day-to-day data engineering operations.

Lesson 3 • Environment Management and Configuration

This section covers dev, staging, and production environment parity, secrets management, and environment-specific configs. It prevents configuration drift and credential exposure across deployment environments.

Lesson 4 • CI/CD Pipelines for Data Engineering

This module builds automated testing, linting, deployment, and rollback workflows for data pipeline code. It applies software engineering delivery practices to the data engineering lifecycle.