What Is ETL and ETL Testing?
Extract-Transform-Load (ETL) is a data integration concept that describes how data is transferred or loaded from its source to the data warehouse. While the first iterations of the ETL process are considered to be athing of the past with the rise of Big Data analytics, these iterations, together with data warehouses and the Business Intelligence (BI) that they deliver, have evolved and are widely used in practice by businesses today. For businesses with stringent financial reporting and audit-intensive requirements, data warehouses and ETL still provide awell-modeled and structured solution compared with emerging solutions such as Hadoop.
To better understand why ETL is still widely used today, let us take a closer look at both ETL and ETL testing.
Obtaining Business Intelligence (BI) — the meaningful insights that help enterprises make important decisions — is the primary objective of enterprise systems. BI is derived from the data that enterprises possess such as daily transactions and correspondences with customers, suppliers, and other stakeholders. However, for this data to be useful, it must first be transformed into information that is easily accessible and consumable by BI applications and tools, as well as end-users. Only then can the information be analyzed to create true Business Intelligence.
The usual process of generating Business Intelligence involves a series of steps:
The fourth step alone (the generation of Business Intelligence) consumes a significant part of a system’s capacity. That’s why enterprises have found it beneficial to separate the transaction workload from the analysis workload. This is where ETL comes in.
ETL is a process that simplifies the first three steps. From its abbreviation, it “extracts” data from the multiple and disparate source systems such as records databases, “transforms” this data into usable information for decision makers, and “loads” the data into data warehouses, from which end-users can readily extract usable data for query and analysis. It is also important to note here that the transform process also standardizes the data collected from the databases into formats or schemas readable by the data warehouses.
ETL enables enterprises to effectively separate the transaction workload from the analysis workload by utilizing data warehouses. Instead of end-users going directly to source databases, adding to the transaction workload, and processing the extracted data, they can gather already-processed information from data warehouses. ETL also helps end-users avoid disrupting the transaction processes itself.
To illustrate how ETL works, imagine an enterprise with several departments such as marketing and finance. Each of the departments uses data such as customer information differently. For example, finance may be more concerned about the transactions made by customers while marketing is more concerned about the customers’ demographics. Thus, each department stores different attributes in their systems. Finance may also have recorded each transaction by customer name, while marketing may have stored customer information by customer ID.
Suppose the marketing end-users want to check the transaction history of a customer. In that case, they cannot easily gain access to this information as it is stored in a separate database, the one used by finance. In this system, gathering information for analysis is tedious and may be disruptive of other processes.
ETL solves these problems by extracting data from all the departments, processing or transforming the data into a standard and usable format, and storing it into a single data warehouse from which all end-users have access. By utilizing ETL and data warehouses, marketing in our example can easily extract necessary data for analysis without disrupting the finance department.
