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Data profiling is the process of examining, analyzing, and creating useful summaries of data. The process yields a high-level overview which aids in the discovery of data quality issues, risks, and overall trends. Data profiling produces critical insights into data that companies can then leverage to their advantage.

Column analysis can be used to discover what the domain of a particular field is, while domain analysis is used to check whether a specific data value is contained within a domain of values.

Column analysis is a data profiling process that examines the content of columns in your data. You run a column analysis job to understand the structure of your data and to find anomalies that might be affecting the quality of your data.

Column profile includes the count of errors and empty values, it also includes the distribution of values in the column. However, the column profile includes much more information too. Information such as the minimum, and the maximum values, count of unique values, and the distribution of values in detail.

The data profiling tools provide intuitive ways to clean, transform, and understand query data, such as key statistics and distributions. In addition, by using the Count Rows command, you can also get a row count of all your query data. By default, Power Query profiles data over the first 1,000 rows.

You run a column analysis job to evaluate the following characteristics of columns in your data: Minimum, maximum, and average field length. If a field is too short, you can lose data. If the field is too large, it uses memory that might be needed elsewhere.

Column profiling provides statistical information regarding the distribution of data values and associated patterns that are assigned to each data attribute, including range analysis, sparseness, format and pattern evaluation, cardinality and uniqueness analysis, value absence, abstract type recognition, and attribute ...

Data profiling in ETL is a detailed analysis of source data. It tries to understand the structure, quality, and content of source data and its relationships with other data. It takes place during the Extract, Transform and Load (ETL) process and helps organizations find the right data for projects.