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Data Validation

Data Validation is the process of ensuring that data is correct, complete, consistent, and useful before it enters a system or is used for analysis. In data pipelines, application development, and analytical workflows, poor data validation is one of the most common and expensive sources of bugs and bad decisions.

What is Data Validation?

Data validation encompasses input validation at system boundaries (form fields, API inputs, file uploads), schema validation (JSON Schema, Pydantic, Zod, Joi), data pipeline validation (Great Expectations, dbt tests, Soda), referential integrity checks, statistical validation (detecting unexpected distributions or outliers), and data quality monitoring dashboards. It applies across web development, data engineering, and ML feature engineering.

Why Data Validation matters for your career

Garbage in, garbage out. Systems that don't validate data properly suffer from corrupted databases, security vulnerabilities, incorrect analytics, and degraded ML model performance. Engineers who build robust data validation into their systems prevent entire categories of production incidents.

Career paths using Data Validation

Data validation skills are important for Data Engineer, Analytics Engineer, Backend Developer, and Data Scientist roles. Security-aware validation is also a core skill for any backend engineer exposed to user inputs.

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Frequently asked questions

What's the difference between data validation and data cleaning?

Data validation checks data before it enters a system and rejects or flags invalid inputs. Data cleaning (scrubbing) is the downstream process of fixing already-stored bad data. Prevention (validation) is always preferable to cure (cleaning).

What are dbt tests used for?

dbt (data build tool) includes a testing framework that runs assertions on your data models: not-null, unique, accepted values, and referential integrity checks. These tests run after every dbt build, catching data quality issues before they affect downstream consumers.

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