How to Validate Data Before Loading It Into Snowflake
Imagine a company loading thousands of customer records into Snowflake. Some records contain missing email addresses, a few have incorrect date formats, and others appear more than once. If these problems are not detected early, reports and dashboards may show incorrect results. This is why data validation matters. At Trendnologies , learners exploring data engineering can build their understanding of how data moves through pipelines and why data quality checks are important in real projects. What Is Data Validation in Data Engineering? Data validation is the process of checking whether incoming data meets the expected rules before it is accepted for further processing or storage. These checks may include verifying column names, data types, required fields, duplicate records, valid values, and relationships between datasets. For example, an order table may require every order to have an order ID, a valid order date, and a non-negative order amount. If a record does not meet these...