Data profiling is a process of examining data from an existing source and summarizing information about that data. You profile data to determine the accuracy, completeness, and validity of your data. Data profiling can be done for many reasons, but it is most commonly part of helping to determine data quality as a component of a larger project. Commonly, data profiling is combined with an ETL (Extract, Transform, and Load) process to move data from one system to another. When done properly, ETL and data profiling can be combined to cleanse, enrich, and move quality data to a target location.
For example, you might want to perform data profiling when migrating from a legacy system to a new system. Data profiling can help identify data quality issues that need to be handled in the code when you move data into your new system. Or, you might want to perform data profiling as you move data to a data warehouse for business analytics. Often when data is moved to a data warehouse, ETL tools are used to move the data. Data profiling can be helpful in identifying what data quality issues must be fixed in the source, and what data quality issues can be fixed during the ETL process.