Defining the schema

Let's start by defining the expected schema for data validation. This is a critical step in ensuring data quality throughout the ETL pipeline.

You'll use the pointblank library to define the schema structure.

The dataset has already been loaded for you as ts.

Cet exercice fait partie du cours

<cours>Designing Forecasting Pipelines for Production</cours>

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Instructions de l’exercice

Start by importing pointblank.
Define the schema using the right method.
Set the respondent column to object type and value column to float64 type.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Import the required library
import ____ as ____

# Define the schema and set columns
table_schema =  pb.____(
    columns=[
        ("period", "datetime64[ns]"),   
        ("respondent", "____"),
        ("respondent-name", "object"),
        ("type", "object"),
        ("type-name", "object"),
        ("value", "____"),
        ("value-units", "object")])

print(table_schema)

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