Pretty much all companies today work with a lot of data. Accounting for expenses, determining revenue and profit, and analyzing user requests and product traffic—it is all about data. To process various metrics, an analyst needs to consolidate them into one table and compare them with previous reports. Over the course of time-consuming work, metrics can become irrelevant. If the analyst does not have all the data, the final report may be incomplete as well.
In turn, ETL applications help solve a number of critical BI tasks:
Data extraction is the first step in the ETL BI process where data is extracted from different source systems. The source can be a relational database, a flat file (.csv, .excel, .txt, etc.), an API, an IoT device, a website—you name it. Almost any channel that generates or stores data can be used as a source in the ETL process in business intelligence. It can be streaming data coming from social media networks or near real-time stock market buy/sell transactions, or it can come from existing corporate databases and data warehouses.
During the transformation phase, rules and processes are applied to the data to prepare it for uploading to the target system. This is usually done in an auxiliary working environment, called a “staging area”. Here, the data is cleansed to ensure reliability and reconciled to grant compatibility with the target system. Many other transformations may be applied, including:
- Cleanup: correction of any errors or missing values;
- Filtering: selecting only what is needed;
- Merging: merging disparate data sources;
- Normalization: conversion of data into common units;
- Data structuring: conversions from one data format to another, such as JSON, XML, or CSV, in database tables;
- Anonymization and encryption: ensuring data privacy and security;
- Sorting: ordering data to improve search efficiency;
- Aggregation: summarizing detailed data.
During the load phase, the transformed data is uploaded to the target environment. The environment can be as simple as a data table, such as an Excel spreadsheet. It can also be a database, which could be part of a much more complex system, such as a data warehouse, data lake, or some other centralized data repository that forms the basis for data analysis, modeling, and processing.
In short, ETL in business intelligence most often occurs in the following scenarios:
- The extraction process retrieves data from one or more sources.
- The conversion process converts data into a format suitable for its purpose and intended use.
- The final download process takes the transformed data and loads it into a new environment, making it ready for visualization, exploration, further transformation, and modeling.