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DP-750 · 3.4.2 · Prepare and process data

Data type checks

Implement data type checks

Validate and cast incoming values to the target schema, handling unsafe conversions as quality failures instead of silent assumptions.

Inspect types before enforcing them

Azure Databricks notebook output table showing column data types and null values in a DataFrame.

Type checks start with knowing what the engine actually inferred. The notebook output exposes those types beside the data; the diagrams below show how to validate and reject incompatible values.

Treat types as a data contract

  1. 1Raw valueArrives from source
  2. 2Parse or castConvert to target type
  3. 3ValidateDetect unsafe conversion
  4. 4RemediateKeep, quarantine, drop, or fail
Type validation

Dates, timestamps, numeric measures, and keys benefit from explicit type checks before loading.

Do not hide failed conversions

  • Conversion is validKeep typed value
  • Source is malformed but recoverableQuarantine or correct
  • Bad value breaks the contractFail or reject
Cast result

A malformed date is a quality problem, not a reason to silently accept a bad value.

Schema enforcement is another layer

Delta protects the target from incompatible writes, but it does not replace deliberate source validation.

Key points

  • Target schema: Defines expected column types
  • Safe cast: Converts values only when semantics allow
  • Invalid input: Detect malformed values before loading
  • Delta write: Enforces compatible target types

Exam trap

Automatic safe casts do not justify ignoring malformed source values.

Sources · 2 Microsoft Learn pages Show ▾

Checked on October 4, 2026. Report a problem

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