liveBook · Manning

Data type

As explained in Effective Data Analysis

A data type specifies the kind of value a field can contain and the operations or representation associated with that value.

A data type specifies the kind of value a field can contain and the operations or representation associated with that value. Choosing consistent types is part of structuring data for reliable use. section 11.3.1

Data types matter when teams:

  • Correct discrepancies between incoming data and existing warehouse fields. section 11.3.1
  • Represent dates consistently so users do not need manual conversions. section 11.3.1
  • Choose Boolean fields for operational indicators such as whether a forecast is most recent. section 11.3.1

Weather data integration

A tourism company combines weather API data with warehouse models. Consistent date types support analysis, while a Boolean field identifies the most recent forecast and a zip_code field preserves a usable join key. section 11.3.1

Figure 11.11 Structuring data in a warehouse involves ensuring it can be easily and accurately queried.
figure

Use the warehouse-structure figure to connect data types with accurate querying and broader data organization. section 11.3.1

How Effective Data Analysis covers it

How deep it goes
Explains types through cleaning and ETL examples, but stops short of cataloging type systems or implementation details.
Examples
Uses weather API records, zip codes, date fields, and a most_recent Boolean indicator.
What you can do after
Apply data types when correcting mismatched fields and preserving usable join keys across integrated datasets.
How it connects
Connects data types with data structure, ETL, data warehouses, join keys, dates, and Boolean fields.

Also explained in

Compare how 18 books explain data type →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.