ETL is an acronym that stands for extract, transform, and load. It is a process used in data warehousing and business intelligence (BI) systems that involve extracting data from one or more data sources, transforming it to meet the requirements of the new target system, and loading it into the new target system. ETL can be used to move data between different data stores or to move data into and out of data warehouses and data marts. The goal of ETL is to provide business users with timely, accurate, and actionable information.
To put it simply, if you’ve ever wondered “what is ETL?” it’s basically a way for organizations to commence their digital transformation processes or upgrade to new systems while keeping all of their previous data and tracking it in more efficient ways. It’s also a great way for data scientists to transfer huge volumes of data between different big data sources.
ETL tools can be used to extract data from a wide variety of source systems, including enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, human resources (HR) systems, financial systems, and more. There are three main steps in the ETL process in order to get big data sets from one source system to another: extracting data from various sources, transforming it into a uniform format, and loading it into a data warehouse or data mart. Here is a detailed breakdown of the steps, as well as some common use cases.
The first step in the ETL process is to extract the data from the source systems. This process usually involves extracting the data from the source system in a specific format, such as a delimited text file, and then importing that data into the staging area. The extraction process can also involve transforming the data as it is extracted, such as converting the data from one format to another or cleansing the data before it goes to the staging area. The staging area is a temporary area where the data is stored before it is loaded into the data warehouse or data mart. Ultimately, the extraction process prepares data from disparate sources to be collected and tracked by the same system, a step that’s crucial for master data management.
The second step in the ETL process is to transform the data. This step involves transforming the data from its source format into the format that is required by the data warehouse or data mart. The transformation process includes the following steps:
- Identify the source and target columns.
- Map the source columns to the target columns.
- Convert the data type from one type to another.
- Remove any invalid data.
The goal is to make the data as clean and consistent as possible so that it can be loaded into the data warehouse and used for reporting and advanced data analysis.
The last step in the ETL process is to load the newly transformed and cleaned data into the new target system. This can be done by writing the data to a file or inserting it into a database. Validation is also an important part of this process, which means checking the data against any validation rules that have been defined. The final step is to clean up the new data in the target system. This can involve deleting any duplicate or invalid data, or simply formatting the data in a way that is easy to work with.
Benefits of ETL
The greatest benefits of the ETL process include improved data, analysis, and tracking across the board. Data is one of the most powerful and valuable tools to any organization, but you can only monetize it properly when it’s up to date and current. ETL helps with this by improving data accuracy, consistency, completeness, timeliness, security, and more.
Without ETL, organizations will remain stuck with legacy systems that aren’t able to process data from enterprise big data sources in a timely fashion, so those who neglect ETL will be left behind.