How ETL is Done
ETL (extract, transform, and load) is an essential aspect of data processing. It is the first step before data analysis. Therefore, this process helps you to collect, filter, and store data. For this reason, this process has become instrumental in the operations of most businesses. Similarly, governmental and educational institutions use ETL in their day-to-day activities.
What is ETL
ETL is a process that was developed for the extraction of data from different sources into a database. It involves the extraction (collection), transformation, and storage of data points in the desired format. After an ETL process, raw data becomes refined and ready for processing.
Broadly speaking ETL involves three major steps. These steps are extract, transform, and load. However, clean and analysis are sometimes included. Nonetheless, each of these processes helps improve the quality of data. It also increases the chances of deriving meaningful information from the database.
This process collects data in its raw form. Such data is unstructured with no clear pattern. After collection, it is temporarily stored before it is transformed. The extraction process involves copying data from various source locations. Examples of such sources include emails, flat files, web pages, metrics, and much more.
During the extraction process, a larger size of data is collected than what is needed. Doing this helps get all the required data points. Similarly, having a wider data range is ideal for new data points. In addition, some institutions may require a wider data range for some other needs. In addition, extraction can be either be partial or full.
The transform stage of ETL involves several processes. These processes help to transform data from its raw form to an organized entity. To achieve this, the hollow must be done.
- Data must be cleansed and standardized. Data cleansing involves the repair of damaged data points. Also, it involves the inclusion of missing values. Standardization helps present data in a standard format.
- Next, the data must be validated and verified. Doing this helps remove unwanted and unusable values.
- The validated and verified data is then filtered and sorted. These processes break the data into fields and types.
- At this stage, an audit is carried out on the data. Auditing is mostly done on data that is useful for identification.
- Some data need to be combined or split to facilitate storage and further analysis. Afterward, the data points may require formatting.
- Calculations may be done to make data readable. Therefore, a new field may be created for this purpose. In some cases, data must be translated from one language to another before usage.
The ETL process terminates at load. In simple terms, this step involves the storage or transfer of data into a database. Such a database can be a small or a large data warehouse. The type of storage used depends on the data requirements, its complexities, and its size. Additionally, data is mostly loaded either fully or incrementally. The loading process used depends on the type of data and the nature of the database.
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