If you have any knowledge about the database or warehouse then you can comprehend the word ETL very well. To understand the term, we need to know the meaning of the word stands for extract, transform and load.
In this article, we will get familiar with the term “ETL” and look into its work process.
The word ETL means to extract, transform and load. It comes under the data integration process that is a mixture of data forms different into one data store. It consists of a single data store that is equipped data warehouse.
Around 1970, When the database has seen its surge in popularity the ETL service emerges as a solution for the improvement in the efficiency of the system. With the most efficient process to collect, integrate and process the data for warehousing project purposes.
The ETL has laid a foundation for advanced techs such as machine learning and artificial intelligence. Through a series of sophisticated mechanism filters and arranges, the data significantly fulfills the needs of the business intelligence ecosystem.
With simple work such as monthly reporting to process advanced analytics, ETL upgrades the back-end solutions and improves the user experience.
There are several occasions in the practical world when the ETL plays a crucial role.
a. Extracting data or filtering data from the existing data and legacy system
b. Improve the data quality and maintain the successive activities
c. Load all the data in some primary data warehouse.
How does ETL work?
The working mechanism of ETL can be understood through the following points.
The part is called extraction in which data is copied and stored. It is moved from a source location to the designated destination area. The management of the data with the team members is extracted from a number of sources. The collected data can be in a regular or irregular form.
In this second stage of the ETL model, the extracted data is in the raw form undergoes data processing and then In this stage, the data is ready to be used after careful analysis so that it can be used for analytical tools. In this phase, there are certain tasks followed in this stage
· Separation, deduplication, authorization, and authenticating the data
· To do a process having summarization and take out conclusive points after consolidation. It also includes converting currencies into the industry set standard form, converting rows and columns, etc
· Removal and application of encryption of data according to the demand of the project.
· Standardization of data into the right formats to match the requirement of the data warehouse.
In this final stage of ETL, the data is moved from the stage area to the target warehouse area. This stage includes loading all the data after the loading of the incremental form of data. In this third stage for some projects, the stored information is revised with the required set time. Usually in this stage, the information which is stored is rewritten in some cases because of the demand of clients.
At this stage, the whole process is well designed and automated to push maximum efficiency.
Challenges with ETL
The whole process of ETL is quite a complex process. There are major issues spiked up during the working on projects if the system is improperly designed. The wide range of the numerical values of data might surpass the results or underperform the system of the project operations. During the analysis of data, it is necessary to right profiling the right information resources.
The information is stored in the warehouse is taken from multiple sources in many formats and purposes. The main purpose of the ETL is to assemble this data in a standard and uniform manner to take the necessary actions with time.
Sometimes the data in the many formats surmounts to a huge form and increases with the sudden rise in demand. And the time for the extraction same as before. Here the difficulties arise with time. The scalability of some ETL systems has been designed to handle the increased load but some are not. So with this system, we need to be very careful with processing data in a limited time and terabytes of data need to be designed to handle those increased demands.