(structure:spreadsheets=index)=

Spreadsheets¶

So far, we’ve seen relational, matrix, and dataframe data models with rigid requirements (fixed, atomic, etc.), and we’ve also seen semi-structured data models with more relaxed requirements (nested, flexible, non-atomic). In this note, we’ll introduce another data model with "relaxed" requirements: the cell-structured data model.

Spreadsheets are a ubiquitous tool, used by as many as 10% of the world, or 800 million people, and dates back to 1969, about as old as databases. There are many use cases for spreadsheets, ranging from professional, personal, and even here in education.

• Data 101 Course Notes
• 1 SQL
  • 1.1 SQL Review
  • 1.2 String Manipulation
  • 1.3 Truncating Relations
  • 1.4 Creating Tables and Views from Data
  • 1.5 Common Table Expressions (CTE)
  • 1.6 Subqueries
  • 1.7 Window Functions
  • 1.8 DML—Data Manipulation Language
  • 1.9 DDL–Data Definition Language
  • 1.10 Keys & Constraints
• 2 Relational Algebra
  • 2.1 Relational Algebra Overview
  • 2.2 Extended RA Operators
• 3 Query Performance
  • 3.1 Index Selection
  • 3.2 Query Planning
  • 3.3 Rules of Thumb
  • 3.4 PostgreSQL Schemas
• 4 Data Modeling
  • 4.1 Data Models
  • 4.2 Entity Relationship(ER) Diagrams
• 5 Data Preparation
  • 5.1 Structural
  • 5.2 Numerical, Granularity
  • 5.3 Outliers
  • 5.4 Imputation and Entity Resolution
• 6 Semistructured Data
  • 6.1 Semistructured Data
  • 6.2 MongoDB
• 7 Sampling
  • Database Sampling
• 8 Transactions
  • 8.1 Transactions
• 9 Parallel/Distributed Computing
  • Parallel and Distributed Computing
• 10 Data Operations and Pipelines
  • Data Operations and Pipelines
• 11 Business Intelligence (BI)
  • Business Intelligence (BI)
• 12 Graph Databases
  • Property Graphs
  • Triplestores/RDFs
  • Ontologies
• Spreadsheets
  • The Spreadsheet Data Model
• Appendix
  • 12.1 SQL Style Guide
  • 12.2 MongoDB Style Guide