Validation

Definition

Checking that data meets expected rules before using it. Validation is a safety gate: it catches typos, missing fields, wrong types, and out-of-range values at the boundary, when data enters your system, rather than letting bad data cause mysterious failures later.

More from Data & Transformation

• Aggregation

  Combining many individual data points into a summary. Aggregations answer questions like 'how many,' 'what's the total,' or 'what's the average' by collapsing a list of values into a single result.

• Data Pipeline

  An automated flow that extracts data from a source, transforms it into a useful shape, and loads it into a destination. Often called ETL (Extract, Transform, Load). Data pipelines run on a schedule to keep dashboards, reports, and databases up to date.

• Deduplication

  Removing duplicate entries from a dataset so each record appears only once. Duplicates creep in when data is imported from multiple sources, forms are submitted twice, or syncs overlap. Deduplication keeps your data clean and your counts accurate.

• Normalization

  Restructuring data to reduce duplication and inconsistency. Instead of repeating the same information in multiple places, normalization splits data into related tables or structures and connects them with references.

• Null Value

  A special placeholder meaning 'no data here.' Null is different from zero, an empty string, or false. Those are all real values. Null means the value is deliberately absent or unknown. Forgetting to handle null is one of the most common sources of bugs.

• Parsing

  Reading raw text and extracting structured information from it. Parsing turns unstructured data (a string, a file, a response body) into something your code can work with: objects, arrays, numbers, dates.