dataLoadR (sm)

The process of modifying data before it is ingested into a system is referred to as custom transformations in data intake. Changes in data types, the addition or deletion of columns, or the use of functions to create calculated columns are some examples of these transformationsAd1. You can alter your data in a way that best satisfies your requirements and preferences by using custom transformations. For instance, you can apply customized transformations to weed out useless data, add analytics or outside data to current data, or conceal sensitive or private information.

Data validation is the process of ensuring that data has undergone some sort of cleansing or checks to make sure the data quality is as expected and the data is correct and useful1. Data validation can be done at different levels, such as at the record, file, or process level (or a combination). At the process level, you can check if the process itself is working as expected. At the file level, you can check if the files you’re receiving are what you’re expecting. At the record level, you can check if the details in each record are correct.

The practice of adding additional and supplementary information to existing datasets is known as data enrichment, which aims to improve the quality of data. The value of the data is then increased by having this data checked against outside sources. Businesses may tailor their sales efforts, better understand their clients, and improve the customer experience with the aid of data enrichment. By concentrating on maintaining data that is important to the business, such as customer contact information or transaction histories, and deleting or moving less important data to less expensive long-term storage facilities, it can also help cut costs.

A rules engine is a software component that allows you to define, execute, and maintain business rules. A rules engine can be used to create and carry out rules that modify, verify, or enhance incoming data before it is ingested into a system in the context of data ingestion. For instance, you may use a rules engine to create a rule that verifies the accuracy of incoming data or converts data between formats. By automating the data import process, rules engines can guarantee that data is processed consistently and precisely.