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.

Acest exercițiu face parte din cursul

Designing Forecasting Pipelines for Production

Vezi cursul

Instrucțiuni pentru exercițiu

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

Exercițiu interactiv practic

Încearcă acest exercițiu completând acest cod de exemplu.

# 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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