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The Rise of Apache Parquet
How a Columnar Format Reshaped Big Data Analytics

Book Details
About this book:

The Rise of Apache Parquet Every technological revolution rests on quiet infrastructure—the unglamorous plumbing that makes the extraordinary seem ordinary. *The Rise of Apache Parquet* tells the definitive story of how a columnar file format born at Twitter and Cloudera shattered the I/O bottleneck of the Hadoop era, transforming analytical processing from a grinding, disk-bound slog into the interactive, cloud-native standard we rely on today. From the tyranny of delimited text files and the limits of row-oriented giants like Avro, through the "Format Wars" with ORC, to the architectural breakthroughs of definition levels, predicate pushdown, and vectorized execution, this book reveals why Parquet became the invisible lingua franca of global analytics.

Go beyond the "what" to master the "how" and "why." You’ll peel back the byte-level mechanics—metadata footers, dictionary encoding, bit-packing, and the Dremel-inspired algorithm that tames nested data—while tracing the ecosystem dynamics that cemented dominance: Spark’s embrace, the rise of Presto and Trino, the shift to object storage, and the birth of the modern Data Lakehouse (Iceberg, Delta Lake, Hudi). Later chapters tackle the operational realities every senior engineer faces: schema evolution at scale, streaming ingestion bottlenecks, modular encryption, and the cross-language rewrites in Rust and C++ powering the next generation of data tools.

Whether you are a data engineer fighting cloud costs and query latency, an architect designing disaggregated platforms, or a technologist fascinated by how storage shapes compute, this is your essential guide. Understand the format that turned petabytes into interactive insights—and gain the architectural foresight to navigate the next decade of analytical computing.

Table of Contents:
  • Introduction
  • Chapter 1 The Row-Oriented Past: Early Storage in Big Data
  • Chapter 2 The Columnar Breakthrough: Analytical Processing Reimagined
  • Chapter 3 Genesis at Twitter and Cloudera: The Birth of Parquet
  • Chapter 4 Standing on the Shoulders of Dremel: Core Design Principles
  • Chapter 5 Anatomy of a Parquet File: Headers, Footers, and Metadata
  • Chapter 6 Row Groups, Column Chunks, and Pages: The Storage Hierarchy
  • Chapter 7 Modeling Complex Data: The Definition and Repetition Level Algorithm
  • Chapter 8 Shrinking the Footprint: Encodings, Bit-Packing, and Compression
  • Chapter 9 Entering the Apache Incubator: Building an Open Community
  • Chapter 10 The Format War: Parquet, ORC, and Avro Compared
  • Chapter 11 Engine Integration: How Apache Spark Embraced Parquet
  • Chapter 12 Pushing Down Predicates: Accelerating Interactive SQL Engines
  • Chapter 13 The Rise of Presto and Trino: Querying at Scale
  • Chapter 14 The Cloud Migration: Parquet as the Foundation of Object Storage
  • Chapter 15 Cost and Scale: Economic Impacts of Storage Efficiency
  • Chapter 16 Arrow and Parquet: Bridging Disk and In-Memory Analytics
  • Chapter 17 Machine Learning Pipelines: Fueling Modern Feature Stores
  • Chapter 18 Streaming Data Challenges: Micro-Batching and Ingestion Bottlenecks
  • Chapter 19 Schema Evolution: Managing Change in Dynamic Environments
  • Chapter 20 Data Lakehouse Architecture: Delta Lake, Iceberg, and Hudi
  • Chapter 21 Securing the File: Modular Encryption and Access Control
  • Chapter 22 Beyond Java: Porting Parquet to C++, Rust, and Go
  • Chapter 23 Real-World War Stories: Scale, Failures, and Optimization Lessons
  • Chapter 24 The Modern Data Stack: Parquet as the Invisible Standard
  • Chapter 25 The Road Ahead: The Next Decade of Analytical Storage
Extended Catalog title. This book is part of our extended catalog and is sourced after you order. Usually this process takes less than an hour to a few hours. We'll email you when your ebook is ready.
Author:

Jerry U. Thompson

Published By:

MixCache.com Extended Catalog

Type:

Nonfiction

Date Published:

September 6, 2026


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