Is ETL Part of Data Science
Many people confuse ETL (extract, transform, and load) with data science. For does not involve in the business of handling data, those terms can mean the same thing. Many people think one term belongs to another. However, that is not the case. There is a lot of difference between these two terms. Nevertheless, they may still have some commonalities.
As stated earlier, ETL means to extract, transform, and load. This process is crucial to data collection and extraction. It provides a user the framework to collect, manipulate, and store data for future use. In general, ETL tries to give the best presentation of data. For example, you will want the ETL process to store the data of your customers in a specific way. You will also want to have all the required fields populated according to each client. If the data you collect is unorganized, you will use cleansing tools to transform it into the desired format.
To understand the concept of ETL, you must understand the individual processes involved in ETL.
The first step of ETL involves data extraction. Data is mostly extracted from different sources with varying formats. Although each data set may contain similar information, each source will have its unique format. in any case, the extraction process can be done using three methods. These methods are partial extraction, update-driven partial extraction, and full extraction.
Partial extraction is simple and easy to implement. However, the system must be notified to extract data when records change. Update-driven partial extraction can check for changes to records. This addition is necessary as not all records update an extraction system. On the other hand, full extraction extracts the entire records. This method is used when a system cannot detect a data change.
Once data extraction completes, you need to transform data into a specific format. To achieve this, data is cleaned, arranged, and transformed. All of these processes are done to increase data quality. Therefore, errors in the data are traced and addressed accordingly.
Once data has been transformed, it is then loaded onto the required database. This database can be new or in existence. As such, the way you go about loading data is also essential. Also, appropriate processes can be used to load data onto a database.
Data science begins after data has been loaded. Therefore, you apply data science tools after ETL. As a result, ETL is mostly used by data engineers. On the other hand, data science is used by data scientists. The job of the data scientist is to use analytic tools to explain raw data. So, data is prepared, and appropriate models are built and used. Similarly, optimization techniques are used to bring out the best insights from the processed data.
For example, ETL will give you the data relating to customer reviews for a product. However, you will need data science tools to make sense of such data. Therefore, data science transforms data into meaningful information for decision-making.
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