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.
The Data Profiling Process in ETL
In the past, data profiling required data engineers who could query data using a programming language, such as SQL. This was a long and often-complicated process that many organizations couldn't afford. In an ETL context, data profiling involves gathering data from different sources for analysis. ETL automates much of the data profiling process, with specialist ETL tools like Xplenty facilitating the data-gathering process for organizations that lack a data engineering team.
The data profiling process in ETL requires a common repository for storing data results and metadata. During the process, organizations identify data consistency and quality issues and correct them in real-time, resulting in fewer errors and higher-quality data analysis.
Organizations use data profiling in ETL in the following scenarios:
- A requirement to automate the data profiling process.
- Reducing human error during data profiling.
- The need for high-quality and consistent data.
- Identifying problems during data profiling.
Data profiling in ETL is a prerequisite to data analysis. As this process improves the structure, quality, and content of source data, users execute better data analysis and generate valuable intelligence about their organization.
When data profiling in ETL, organizations discover whether data is:
- Unique
- Incomplete
- Corrupted
- Duplicated
Organizations then identify patterns and correlations in data and start to generate insights.
Data Profiling Types
There are three main data profiling types:
- Column profiling: counts the number of times data values appear within columns in tables.
- Cross-column profiling: analyzes data across columns in tables.
- Cross-table profiling: analyzes tables for similarities and differences in data types across tables.
Organizations discover patterns within data from these methods.