ETL stands for Extraction, transform and load, is a data integration process with this process we combine data that come from multiple data sources to the single consistent data source to store data that will be loaded in a data warehouse or another store source.
As the database come into popularity in 1970, then ETL was introduced as the process for integration and loading for various types of analysis and eventually become the method to process data for data warehouse projects. If you find the best ETL service provider then click here.
It provides a base to data analytics or machine learning technologies. With time to time ETL process is going to improve, and now it is able to organize data for specific business intelligence needs for example monthly reporting. Not only monthly reporting is can also able to tackle more advanced analytics with this we can improve our backend process and user experiences. ETL is used by organizations to:
- To Extract data from their old systems.
- To clean or manage the data to enhance the data quality.
- To load data to their target database.
How ETL works:
If you want to clearly understand the concept of how ETL work then you first need to understand what happens in each step of the ETL process.
- Extract: During the time of data extraction huge amount of raw data is copied and pulling data from more than one source. The organization data management teams extract data from a variety of data sources which may be in structured or unstructured form. Those sources are included in ETL but has not limited to.
- Relational and non-relational databases are used for the process of extraction.
- CRM (customer relationship management) and ERP systems.
- APIs (application programming interface).
- Transform: During this phase of the ETL process, the raw data goes to data processing. At this rule stage rules and regulations are applied to create valuable data quality. Here data is transformed and consolidate for analytical use. This process of transformation takes several tasks.
- Irregulars and missing values in data are resolves in this stage.
- Rearrange the unstructured data in structured data.
- Join multiple database tables together.
- Sort the data to make columns in a certain order.
- In transformation, Clean the some data to abolish duplicate and outdated data.
This process is considered as a salient part of the ETL process. It increases the data integrity and ensures data is compatible with a new destination and ready to use.
- Load: This is the last step of the ETL at this stage the transformed data is moved to its destination data warehouse. In most of the organization this process is fully automated, well define, batch-driven. The data can be load in two ways in the first way the data is loaded all at once and in the second way data is loaded in scheduled intervals.
- Full-loading: In the process of full loading, everything that comes from the process of data transformation will go to new records in the data warehouse. This data will be useful in future research, full loading produces data sets that can grow speedily and may become difficult to maintain.
- Incremental-loading: It is a less detailed but more manageable approach. In this technique, Incremental-loading compares data with already existing data and produces additional if new and unique data is found in transformed data.
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