Data Lake Course

Lesson 1 • What Is a Data Lake

Defines data lakes and contrasts them with data warehouses and data marts. Provides the conceptual baseline for all subsequent architectural decisions.

Lesson 2 • Common Pitfalls and the Data Swamp Problem

Identifies governance failures that turn data lakes into unusable data swamps. Awareness of these risks motivates best practices introduced later.

Lesson 3 • Data Lake Use Cases and Business Value

Maps real-world scenarios—analytics, ML, archiving—to data lake capabilities. Learners connect technical features to measurable business outcomes.

Lesson 1 • Storage Tiering and Cost Optimization

Explains hot, warm, and cold storage tiers and automated tiering policies. Learners design cost-effective retention strategies aligned with access patterns.

Lesson 2 • Compression Strategies for Data Lakes

Covers Snappy, Gzip, Zstd, and LZ4 trade-offs between compression ratio and speed. Proper compression reduces storage cost without degrading query performance.

Lesson 3 • Columnar and Row-Based File Formats

Compares Parquet, ORC, Avro, CSV, and JSON for analytical workloads. Format choice directly impacts query speed and storage efficiency.

Lesson 4 • Object Storage as the Foundation

Explains object storage mechanics, bucket organisation, and durability guarantees. Object storage is the dominant physical layer for modern data lakes.

Lesson 1 • Pipeline Reliability and Monitoring

Teaches dead-letter queues, data quality checks at ingestion, and pipeline observability. Reliable pipelines prevent silent data loss and downstream corruption.

Lesson 2 • Streaming Ingestion Fundamentals

Introduces event streaming concepts, message brokers, and exactly-once semantics. Streaming ingestion enables near-real-time analytics on continuously arriving data.

Lesson 3 • Ingestion Architecture Overview

Maps ingestion patterns to source types and latency requirements. This overview frames the design decisions covered throughout the chapter.

Lesson 4 • Ingestion from Diverse Source Types

Addresses databases, APIs, files, IoT devices, and SaaS platforms as ingestion sources. Each source type requires specific connectors and schema handling strategies.

Lesson 1 • Technical and Business Metadata

Differentiates technical metadata (schema, lineage) from business metadata (ownership, glossary). Both types are required for effective data discovery and governance.

Lesson 2 • Zone-Based Lake Architecture

Introduces raw, cleansed, curated, and sandbox zones with clear promotion criteria. Zone separation enforces data quality boundaries and access control.

Lesson 3 • Data Lineage Tracking

Explains column-level and dataset-level lineage capture and visualisation. Lineage enables impact analysis and builds consumer trust in data assets.

Lesson 4 • Naming Conventions and Folder Structures

Establishes standards for bucket names, prefixes, and dataset identifiers. Consistent naming enables automation, discovery, and governance at scale.

Lesson 1 • Access Control Models for Data Lakes

Compares role-based, attribute-based, and row/column-level access control models. Selecting the right model balances security with operational flexibility.

Lesson 2 • Data Masking and Anonymization

Covers static masking, dynamic masking, tokenization, and pseudonymization techniques. These methods protect sensitive data while preserving analytical utility.

Lesson 3 • Encryption at Rest and in Transit

Explains server-side encryption, client-side encryption, and TLS for data in motion. Encryption is the baseline control for protecting lake data from unauthorized access.

Lesson 4 • Audit Logging and Compliance Reporting

Designs audit log pipelines, retention policies, and compliance dashboards. Audit trails demonstrate control effectiveness to internal and external reviewers.

Lesson 1 • Distributed Processing Fundamentals

Introduces distributed execution models, DAGs, and shuffle operations. Understanding these mechanics is prerequisite to optimizing any large-scale transformation job.

Lesson 2 • Data Quality Transformation Rules

Implements validation, standardization, deduplication, and enrichment as transformation steps. Embedding quality rules in pipelines prevents bad data from reaching consumers.

Lesson 3 • Pipeline Performance Optimization

Applies caching, partition pruning, predicate pushdown, and job tuning to reduce runtime. Optimised pipelines lower compute costs and meet SLA requirements.

Lesson 4 • Streaming Transformation Patterns

Teaches stateful stream processing, watermarks, and late-arriving data handling. Streaming transformations enable continuous analytics on real-time data flows.

Lesson 1 • Lakehouse Architecture Design

Integrates open table formats with query engines, BI tools, and ML platforms into a unified lakehouse. This architecture eliminates the need for a separate data warehouse in many scenarios.

Lesson 2 • Apache Iceberg and Apache Hudi

Compares Iceberg's hidden partitioning and Hudi's record-level upserts to Delta Lake. Learners select the right format based on workload and ecosystem requirements.

Lesson 3 • Limitations of Traditional Data Lakes

Identifies consistency, update, and time-travel gaps in plain object-store lakes. These limitations motivate the adoption of open table formats.

Lesson 4 • Open Table Format Concepts

Explains metadata layers, transaction logs, and snapshot isolation common to all open formats. A shared conceptual model simplifies comparison of specific implementations.

Lesson 1 • Data Retention and Lifecycle Governance

Designs retention schedules, deletion workflows, and legal hold processes. Lifecycle governance reduces risk and storage cost while satisfying regulatory obligations.

Lesson 2 • Data Mesh and Federated Ownership

Applies data mesh principles—domain ownership, data products, and federated governance—to large lakes. Federated models scale governance beyond central team capacity.

Lesson 3 • Roadmap Planning and Maturity Assessment

Uses maturity models to assess current lake capabilities and prioritize improvement initiatives. A structured roadmap aligns technical investments with business strategy.

Lesson 4 • Cost Management and FinOps for Data Lakes

Applies tagging, showback, chargeback, and rightsizing to control lake spending. Cost visibility drives accountability and prevents uncontrolled storage and compute growth.