Which technique is NOT typically used to handle missing data in Databricks?

Handling missing data is a common task in data analysis, and various techniques are employed to deal with this issue. Ignoring all missing values means that you simply disregard any rows or entries that contain missing data. This approach is generally not advisable because it can lead to loss of potentially valuable information, especially if a significant portion of the dataset contains missing values or if the missingness has patterns that could be informative.

Instead, other techniques such as imputation, filtering, and replacing with default values are widely used. Imputation fills in the missing values based on other available information in the dataset, while filtering might involve removing rows that have missing values only when appropriate. Replacing with default values provides a way to maintain dataset integrity while still utilizing all available records. Each of these strategies aims to maintain the robustness and accuracy of the data analysis process, which is why ignoring missing data completely is typically not a recommended practice.