System Design · Data at Scale
Data at Scale
Storage, modeling, and processing when data grows beyond one machine.
01
Chapter One
Data Modeling for Scale
Schema Design Decisions That Last Decades
Coming Soon
Data Modeling for Scale
This chapter covers normalization vs denormalization trade-offs, schema design for read patterns, and schema evolution without breaking clients.
📋 Chapter 1 — Summary
- Summary content pending.
02
Chapter Two
Time-Series Data
When Everything Is a Timestamped Event
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Time-Series Data
This chapter covers the unique characteristics of time-series workloads, the tools built for them, and downsampling and retention strategies.
📋 Chapter 2 — Summary
- Summary content pending.
03
Chapter Three
Search at Scale
When Your Database Can't Answer the Question
Coming Soon
Search at Scale
This chapter covers full-text and vector search at data scale, covering sync patterns, embedding pipelines, and hybrid search.
📋 Chapter 3 — Summary
- Summary content pending.
04
Chapter Four
Batch vs Stream Processing
Old Data vs Fresh Data: The Fundamental Trade-off
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Batch vs Stream Processing
This chapter covers batch and stream processing trade-offs, Lambda and Kappa architectures, and how to choose the right processing model.
📋 Chapter 4 — Summary
- Summary content pending.
05
Chapter Five
Data Lakes & Warehouses
Storing Everything to Analyze Anything
Coming Soon
Data Lakes & Warehouses
This chapter covers data warehouses, data lakes, the modern data lakehouse, and ETL vs ELT pipeline patterns.
📋 Chapter 5 — Summary
- Summary content pending.
06
Chapter Six
Consistency Patterns
From Strong Consistency to Eventual Consistency and Back
Coming Soon
Consistency Patterns
This chapter maps the consistency spectrum from strong to eventual consistency, covering the guarantees and costs of each level.
📋 Chapter 6 — Summary
- Summary content pending.