Data validation can be challenging for a couple of reasons:
- Validating the database can be challenging because data may be distributed in multiple databases across your organization. The data may be siloed, or it may be outdated.
- Validating the data format can be an extremely time-consuming process, especially if you have large databases and you intend to perform the validation manually. However, sampling the data for validation can help to reduce the time needed.
Whether you validate data manually or via scripting, it can be very time-consuming. However, after you have validated your data, a modern ETL tool can help you to expedite the process. As a part of your assessment of your data, you can determine which errors can be fixed at the source, and which errors an ETL tool can repair while the data is in the pipeline. You can then automatically integrate, clean, and transform data as it is moved to your data warehouse.
You can perform data validation in one of the following ways:
- Scripting: Data validation is commonly performed using a scripting language such as Python to write scripts for the validation process. For example, you can create an XML file with source and target database names, table names, and columns to compare. The Python script can then take the XML as an input and process the results. However, this can be very time intensive, as you must write the scripts and verify the results by hand.
- Enterprise tools: Enterprise tools are available to perform data validation. For example, FME data validation tools can validate and repair data. Enterprise tools have the benefit of being more stable and secure, but can require infrastructure and are costlier than open source options.
- Open source tools: Open source options are cost-effective, and if they are cloud-based, can also save you money on infrastructure costs. But they still require a level of knowledge and hand-coding to be able to use effectively. Some open source tools are SourceForge and OpenRefine.