Cloud Computing and Big Data Course

Lesson 1 • Cloud Computing Concepts and History

Traces cloud evolution from mainframes to modern hyperscalers and defines essential terminology. Provides the conceptual baseline for all subsequent cloud topics.

Lesson 2 • Cloud Service Models Explained

Differentiates IaaS, PaaS, and SaaS through functional comparisons and real-world examples. Enables students to map workloads to the correct service layer.

Lesson 3 • Cloud Deployment Models

Examines public, private, hybrid, and multi-cloud architectures with trade-off analysis. Connects deployment choice to organizational compliance and cost goals.

Lesson 1 • Cloud Storage Services

Covers object, block, and file storage types with durability and access-pattern trade-offs. Prepares students to select storage tiers for diverse application requirements.

Lesson 2 • Identity and Access Management

Teaches role-based access control, policies, and least-privilege principles for cloud environments. Establishes security hygiene required before deploying any production workload.

Lesson 3 • Compute Services and Virtual Machines

Introduces virtual machines, instance types, and auto-scaling groups as core compute primitives. Directly applies cloud service model knowledge to infrastructure provisioning.

Lesson 4 • Managed Database Services

Surveys relational and NoSQL managed database offerings, covering provisioning and backup strategies. Connects storage knowledge to stateful application architecture.

Lesson 1 • Batch and Stream Processing Models

Contrasts batch processing with real-time stream processing using the Lambda and Kappa architectures. Prepares students to design pipelines matching latency and throughput requirements.

Lesson 2 • Introduction to Hadoop Ecosystem

Explains HDFS, MapReduce, YARN, and key ecosystem tools as the foundation of distributed batch processing. Provides historical and technical context for modern big data frameworks.

Lesson 3 • Big Data Storage Architectures

Covers distributed file systems, data lakes, and data warehouses as complementary storage paradigms. Bridges cloud storage knowledge to big data ingestion and retention needs.

Lesson 4 • Big Data Pipeline Design Principles

Introduces ingestion, transformation, storage, and serving layers as a unified pipeline framework. Gives students a design template applied throughout the remaining big data chapters.

Lesson 1 • Data Warehouse Loading Strategies

Covers dimensional modeling, slowly changing dimensions, and bulk-load patterns for analytical stores. Connects ingestion pipelines to query-optimized warehouse structures.

Lesson 2 • Real-Time Data Streaming Ingestion

Teaches message queues, event streaming platforms, and producer-consumer patterns for real-time data. Extends batch ingestion knowledge to low-latency, high-throughput scenarios.

Lesson 3 • Batch Ingestion Techniques

Covers ETL workflows, file-based ingestion, and scheduled data transfers for large datasets. Applies pipeline design principles to structured and semi-structured data sources.

Lesson 4 • Data Quality and Validation

Introduces data profiling, schema validation, and anomaly detection as ingestion quality gates. Ensures downstream analytics and ML models receive trustworthy data.

Lesson 1 • Spark Performance Tuning

Addresses partitioning, caching, broadcast joins, and serialization as key Spark optimization levers. Translates theoretical Spark knowledge into production-grade job efficiency.

Lesson 2 • Spark Streaming and Structured Streaming

Covers micro-batch and continuous processing models for real-time data with Structured Streaming. Extends batch Spark skills to low-latency streaming pipelines.

Lesson 3 • Spark Architecture and Core Concepts

Explains the driver, executor, DAG scheduler, and RDD model as Spark's execution foundation. Builds the mental model needed to write and debug Spark applications effectively.

Lesson 4 • Running Spark on Cloud Platforms

Demonstrates managed Spark cluster provisioning, auto-scaling, and cost control on cloud services. Connects Spark skills to real cloud deployment and operational practices.

Lesson 1 • Serverless and Event-Driven Pipelines

Covers function-as-a-service triggers, event buses, and cloud-native workflow services for lightweight pipelines. Complements Airflow with low-overhead, event-driven automation patterns.

Lesson 2 • Pipeline Monitoring and Observability

Covers logging, metrics, alerting, and lineage tracking for operational pipeline visibility. Enables teams to detect, diagnose, and resolve pipeline failures quickly.

Lesson 3 • Pipeline Orchestration Fundamentals

Introduces DAG-based workflow orchestration, task dependencies, and scheduling concepts. Provides the architectural foundation for automating multi-step data pipelines.

Lesson 4 • Building Pipelines with Apache Airflow

Teaches DAG authoring, operators, hooks, and XComs for building complex Airflow workflows. Applies orchestration concepts to a widely adopted open-source platform.

Lesson 1 • Data Visualization Principles

Introduces chart selection, visual encoding, and storytelling principles for analytical communication. Ensures visualizations accurately represent data and drive informed decisions.

Lesson 2 • Analytical Query Optimization

Teaches query planning, indexing, materialized views, and cost-based optimization for large analytical datasets. Builds on warehouse loading knowledge to maximize query performance.

Lesson 3 • Real-Time Analytics and Streaming Dashboards

Extends batch analytics to real-time use cases using streaming aggregations and live dashboards. Connects Spark Streaming and pipeline knowledge to operational analytics scenarios.

Lesson 4 • Exploratory Data Analysis at Scale

Covers statistical summaries, distribution analysis, and correlation techniques applied to big datasets. Bridges raw data ingestion to hypothesis-driven analytical investigation.

Lesson 1 • ML Workflow and MLOps Foundations

Maps the end-to-end ML lifecycle from data preparation to model serving and monitoring. Establishes the operational framework for all subsequent ML implementation topics.

Lesson 2 • Feature Stores and Data Pipelines for ML

Introduces feature stores as a bridge between data pipelines and ML training and serving. Ensures consistent, reusable features across training and production inference.

Lesson 3 • Model Deployment and Serving

Teaches batch inference, real-time REST endpoints, and edge deployment patterns for ML models. Connects trained models to production systems that deliver business value.

Lesson 4 • Distributed Model Training

Covers data parallelism, model parallelism, and distributed training frameworks for large-scale ML. Applies Spark and cloud compute knowledge to accelerate model training on big data.