Big Data Integration Course

Lesson 1 • Metadata and Data Catalogs

Explains technical, business, and operational metadata roles. Establishes metadata management as a cross-cutting concern for all integration work.

Lesson 2 • Big Data Landscape Overview

Defines big data characteristics and the integration problem space. Grounds subsequent technical topics in real-world business context.

Lesson 3 • Data Integration Architectural Styles

Surveys ETL, ELT, data virtualization, and event-driven patterns. Provides a decision framework used throughout the course.

Lesson 1 • Distributed File Systems

Covers block-level distributed storage principles and file system semantics. Connects storage architecture to ingestion and processing decisions made later.

Lesson 2 • Storage Format Selection

Compares row-based, columnar, and nested formats for integration workloads. Directly informs schema design and transformation decisions ahead.

Lesson 3 • Object Storage and Cloud Tiers

Explains object storage semantics and cloud-native tiering. Prepares students for cloud-based integration architectures in later chapters.

Lesson 4 • Data Warehouses and Data Lakes

Contrasts schema-on-write warehouses with schema-on-read lakes. Guides storage selection based on query patterns and integration requirements.

Lesson 1 • Data Transformation Fundamentals

Covers filtering, aggregation, joining, and enrichment transformations. Builds the transformation skill set extended in advanced chapters.

Lesson 2 • Data Loading and Target Patterns

Explains upsert, merge, and slowly changing dimension loading strategies. Connects loading logic to warehouse and lake storage covered in Chapter 2.

Lesson 3 • Batch Ingestion Design Patterns

Covers full-load, incremental, and change-data-capture ingestion strategies. Establishes the ingestion layer that feeds all downstream transformation work.

Lesson 4 • Data Extraction Techniques

Teaches extraction from relational databases, files, and APIs at scale. Provides reusable extraction patterns applied in pipeline labs.

Lesson 1 • Stateless and Stateful Stream Processing

Teaches filtering, mapping, windowing, and aggregation on live streams. Builds processing logic that feeds real-time dashboards and downstream systems.

Lesson 2 • Streaming Concepts and Messaging Systems

Defines event streams, topics, partitions, and consumer groups. Establishes the messaging foundation for all streaming pipeline work.

Lesson 3 • Lambda and Kappa Architectures

Contrasts batch-plus-streaming and streaming-only integration architectures. Guides architectural decisions for hybrid workloads encountered in practice.

Lesson 4 • Stream-to-Storage Delivery

Explains sink connectors, micro-batch landing, and compaction strategies. Connects streaming output to the storage systems covered in Chapter 2.

Lesson 1 • Master Data and Reference Data Management

Defines master data domains, golden records, and reference data governance. Provides the data consistency foundation for multi-source integration scenarios.

Lesson 2 • Schema Design for Data Lakes

Explains schema-on-read, zone architecture, and folder conventions for lakes. Extends lake storage concepts from Chapter 2 with practical design guidance.

Lesson 3 • Conceptual and Logical Data Modeling

Covers entity-relationship modeling and normalization for integration contexts. Provides the modeling foundation for physical schema design ahead.

Lesson 4 • Schema Evolution and Compatibility

Covers backward, forward, and full compatibility strategies for evolving schemas. Prevents pipeline failures caused by schema drift in production systems.

Lesson 1 • Quality Rules and Validation Frameworks

Explains rule-based validation, expectation suites, and quarantine patterns. Integrates quality gates into batch and streaming pipelines built earlier.

Lesson 2 • Data Quality Monitoring and SLAs

Covers continuous quality monitoring, alerting, and service-level agreement tracking. Connects quality metrics to operational dashboards and governance processes.

Lesson 3 • Profiling and Anomaly Detection

Covers statistical profiling, pattern analysis, and outlier detection techniques. Provides diagnostic tools used before and during pipeline execution.

Lesson 4 • Data Cleansing Techniques

Teaches standardization, deduplication, imputation, and parsing transformations. Extends the transformation skills from Chapter 3 with quality-focused logic.

Lesson 1 • Pipeline Observability and Logging

Teaches structured logging, distributed tracing, and metrics collection for pipelines. Provides the visibility needed to diagnose failures in complex integration flows.

Lesson 2 • Advanced Workflow Orchestration

Covers dynamic DAGs, cross-pipeline dependencies, and SLA-driven scheduling. Extends basic orchestration from Chapter 3 to enterprise-scale workflows.

Lesson 3 • Pipeline Testing and CI/CD

Explains unit, integration, and end-to-end testing strategies for data pipelines. Embeds quality assurance into the deployment lifecycle of integration code.

Lesson 4 • Performance Tuning and Capacity Planning

Covers bottleneck identification, resource sizing, and scaling strategies for pipelines. Ensures integration systems meet throughput and latency SLAs under load.

Lesson 1 • Regulatory Compliance and Audit Trails

Explains data retention, right-to-erasure, consent management, and audit logging. Aligns integration pipelines with cross-industry data protection obligations.

Lesson 2 • Access Control and Identity Management

Covers role-based and attribute-based access control for data platforms. Secures data assets across the storage and pipeline layers built in earlier chapters.

Lesson 3 • Encryption and Secure Data Transport

Covers encryption at rest, in transit, and key management for integration systems. Protects data across all movement and storage operations in the pipeline.

Lesson 4 • Data Governance Frameworks for Integration

Defines governance roles, policies, and stewardship models for integrated data assets. Provides the organizational structure that supports all technical governance controls.