10 Data Operations and Pipelines

In the real world, data does not come in as structured data sets, instead we deal with data feeds, or data streams, where the data arrives on a regular basis and upon arrival must be handled appropriately.

• 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