![]() ![]() Let’s take a look at each of the phases in detail below:Įxtraction - or, data extraction - is the first stage of the ETL process. The ETL tool’s name might sound self-explanatory, but there is much more that takes place within the process. After collating the extracted datasets, it would transform them into structured, standardized, and uniform formats - for example, job application dates would be standardized into DD/MM/YYYY formats, and candidates’ names would be standardized into ‘First Name, Last Name’ formats.įinally, the ETL tool would load the transformed datasets into a centralized location for the next step of the BI lifecycle. This is where the ETL tool would come in. These datasets would originate from various sources and applications (e.g., the recruitment website, Google forms, Word documents, Excel spreadsheets, mobile apps, and more), and would therefore be in an array of different formats. Here’s an example of how ETL works - let’s say a recruitment company wants to consolidate all of their datasets, such as their resumes, candidates’ details, job applications, company portfolios, market rates, and more. Additionally, ETL tools eliminate the need for manual data collating, cleaning, and loading, which is often error-prone and tedious. Optimizes and expedites workflows because most ETL tools do not require any data expertise (e.g., technical scripting).This creates a long-term picture of the datasets, allowing older datasets to be analyzed with and compared to newer ones. Allows historic data to be merged with current data across various sources, formats, and applications.Improves data integrity through clean and uniform datasets, thereby enabling more accurate and streamlined data analysis, reporting, and auditing.The ETL tool in the data pipeline is important for the following reasons: The ETL process is an important part of the data preparation stage within the Business Intelligence (BI) lifecycle, whereby a business collects the relevant sets of data, transforms them into a consistent and usable format, and stores them in a repository for analysis.ĮTL is a frequently used process when it comes to data warehousing, machine learning, cloud computing, and more as it allows businesses to properly collate, organize, and manage all their raw datasets. So, let’s take a look at the key areas below regarding ETL:ĮTL - or, Extract, Transform, Load - is defined as a method of extracting, transforming, and loading raw data from various sources into a single and centralized location (e.g., data warehouses or other centralized data storage systems). In the data analytics world, raw data goes through a similar process before it can be analyzed and reported on - and this is where ETL comes in. Finally, depending on store orders, they are sent out to various locations. Then, they are sorted into their respective categories, cleaned to remove any bacteria and soil, and packaged uniformly during the preparation stage. Before any fruit and vegetables are sold in supermarkets, they are first harvested from various farms and orchards.
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