What data validation techniques are essential for Data Engineering?

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Data validation is the process of ensuring that the data you collect, store, and analyze meets your expectations and requirements. It is an essential skill for data engineering, as it helps you avoid errors, inconsistencies, and anomalies that can compromise your data quality and reliability. In this article, you will learn about some of the data validation techniques that you can use in your data engineering projects, such as schema validation, data profiling, data quality rules, and anomaly detection.

Key takeaways from this article
  • Schema validation:
    Implementing schema validation ensures your data adheres to a specified structure, which is crucial for maintaining data integrity. By using validation tools or writing custom scripts, you can catch and correct errors early on.
  • Configurable frameworks:
    Developing a framework that validates data between systems based on configurable parameters offers flexibility. It adapts to specific needs, streamlining the validation process and ensuring data consistency across platforms.
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