Introduction to Data Lake Architectures
Chapter 1: Architectural Foundations
Decoupling Compute and Storage
Object Storage Semantics
The Medallion Architecture
Lambda and Kappa Architectures
Chapter 2: File Formats and Optimization
Row-Oriented vs Columnar Storage
Apache Parquet Internals
Open Table Formats
Compression Algorithms
Partitioning Strategies
Hands-on Practical: Optimizing File Layouts
Chapter 3: Ingestion Pipelines
Batch Ingestion Workflows
Change Data Capture (CDC)
Handling Schema Evolution
The Small File Problem
Idempotency in Pipelines
Hands-on Practical: Building a CDC Pipeline
Chapter 4: Metadata and Cataloging
The Role of the Metastore
Partition Discovery
Technical Governance
Data Lineage Implementation
Hands-on Practical: Configuring a Catalog
Chapter 5: Querying and Performance
Distributed Query Engines
File Pruning and Skipping
Vectorized Query Execution
Caching Strategies
Hands-on Practical: Query Analysis
Batch Ingestion Workflows
Was this section helpful?
- Data Lake Architecture: How to Build and Manage a Scalable Data Lake, Ben Sharma, Michael Armbrust, and Xin Liu, 2021 (O'Reilly Media) - A comprehensive guide to designing and implementing data lakes, including detailed discussions on ingestion patterns, data layering (Bronze/Raw), and architectural considerations relevant to batch workflows.
- Delta Lake Documentation, The Delta Lake Project Contributors, 2023 - Official documentation for Delta Lake, an open-source storage layer that brings ACID transactions to data lakes, addressing critical concerns such as consistency, isolation, and reliability for batch ingestion.
- Designing Data-Intensive Applications, Martin Kleppmann, 2017 (O'Reilly Media) - A foundational book covering principles of data systems, including batch processing, data models, consistency, and distributed storage. It provides understanding relevant to data ingestion challenges like isolation and late-arriving data.
- Tuning Spark, The Apache Spark Project Contributors, 2023 - The official Apache Spark documentation provides guidelines and best practices for optimizing Spark applications, including strategies for data partitioning and managing file sizes to improve query performance in data lakes.
© 2025 ApX Machine LearningEngineered with