What is an ETL Database
ETL (Extract, Transform, and Load) is an essential aspect of technology use. In particular, ETL is a crucial component of data extraction, collection, and presentation. However, it can only work with the help of a database – data warehouse. The end product of ETL and a data warehouse has several functions within every human endeavor.
ETL is a data science process that involves the extraction, transformation, and loading of data. Each of these three processes has distinct rules for operation. In addition, ETL is necessary for the operation of most databases. Similarly, data analysis and reporting are involved before data-driven decisions are made.
Another essential aspect of ETL is consistency. Therefore, you must use consistent and standard processes to ensure the quality outcome of the process. Doing this helps ensure the same output when the process is repeated.
Database (data warehouse) defines a reservoir of data, which is used for decision making. In essence, such data can be used in different sectors such as business, education, and government. This data storage system has been used for almost 3 decades. It was made to become part of the operations decision-making framework of a business model.
To constitute a database, you will have to collect information from various sections. These places include customer review, marketing, and sales. Once gathered, data can then be used for any purpose.
To form an ETL database, you must extract, transform, and then load data. Once this is done, the ETL database is formed.
The first step of creating an ETL database involves data extraction from various sources. This process usually takes a long time to finish. In most cases, the source may be disorganized. Therefore, knowing the data to pull can be challenging. In addition, this data must be collected multiple times to keep the database current. Once this phase is completed, the data transformation begins.
In most cases, data transformation is referred to as data cleansing. This process filters and makes the data suitable for processing. Also, two basic database transformation techniques are used. These tools are homogenization and rectification. Therefore, these tools refer to a bank of information when cleansing data. In particular, the data point is rectified by correcting typographical and identification of synonyms. Specific rules are used to assist with the correlation between data points. Finally, the data is converted from one format to a form suitable for the database.
This process is the last step of the ETL database. Here, the desired data format is stored in the database. The loading process must be done correctly. In addition, a smaller number of resources should be used when loading data.
Data loading can be done either by refreshing or updating the database. When a database is refreshed, it is overwritten. All the files within the database are changed. In most cases, the refresh option is used for a new database. However, the update method is used when a user wants to add data to an existing database. Using this method incorporates new data points without losing information in the database.
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