File Formats & Optimization

Discipline: Data Engineer | Module: Module 2: Distributed Systems, Storage Engines & Big Data Architecture | Estimated Study Time: 28 Hours

Welcome to File Formats & Optimization. This topic delivers foundational and advanced concepts designed for production engineering and real-world workflows.

Key Learning Objectives

1. Row-based vs. Columnar Storage (Apache Parquet, ORC vs. Avro, JSON, CSV)
2. Performance Techniques (Predicate Pushdown, Compression: Snappy, Gzip, ZSTD)

Detailed Curriculum Breakdown

Row-based vs. Columnar Storage (Apache Parquet, ORC vs. Avro, JSON, CSV)

Explore the fundamental principles, real-world patterns, and best practices for Row-based vs. Columnar Storage (Apache Parquet, ORC vs. Avro, JSON, CSV). Practice hands-on implementations to master these concepts.

// Code Example: Row-based vs. Columnar Storage (Apache Parquet, ORC vs. Avro, JSON, CSV)
// Implement verified patterns for production use
console.log("Mastering Row-based vs. Columnar Storage (Apache Parquet, ORC vs. Avro, JSON, CSV)");

Performance Techniques (Predicate Pushdown, Compression: Snappy, Gzip, ZSTD)

Explore the fundamental principles, real-world patterns, and best practices for Performance Techniques (Predicate Pushdown, Compression: Snappy, Gzip, ZSTD). Practice hands-on implementations to master these concepts.

// Code Example: Performance Techniques (Predicate Pushdown, Compression: Snappy, Gzip, ZSTD)
// Implement verified patterns for production use
console.log("Mastering Performance Techniques (Predicate Pushdown, Compression: Snappy, Gzip, ZSTD)");

Practical Application & Exercises

1. Architecture Review: Evaluate how File Formats & Optimization integrates with upstream and downstream systems.
2. Implementation Challenge: Build a functional prototype demonstrating each of the subtopics.
3. Validation & Testing: Verify performance and error handling under edge-case scenarios.

Summary Checklist

• Studied foundational architecture for File Formats & Optimization
• Completed practical coding challenge
• Validated edge cases and error handling routines