Extract, transform, and load (ETL) is the process of integrating data from multiple, typically disparate, sources and bringing them together into one central location. It is a key component to businesses successfully making use of data in a data warehouse.
Sure, the process itself is fairly straightforward, and when done right, ETL prepares an organization for powerful business intelligence initiatives. However, a lot goes into a successful ETL process. Let’s discuss the three steps involved and why data management practices are an essential foundation to carrying ETL out properly.
What happens during an ETL process?
If you’ll remember, ETL stands for Extract, Transform, and Load. An organization looking to complete an ETL process must: 1) Extract data from the database(s) to integrate it from various systems or applications 2) Transform data so that it matches the target system’s required formatting, and 3) Load the final data into the target system.
Extraction is the action of extracting data from a source system to be processed at a later stage. This step is focused on obtaining data as efficiently and with as little impact to the source system as possible.
Businesses today collect and store data in a variety of sources—each with their own way of organizing and formatting that data—and oftentimes the sheer volume of data can make this first step in the ETL process the most time-consuming.
After the desired data has been extracted, it then undergoes a transformation (i.e. conversion) to meet the requirements of the target system. This step can involve:
This process entails several transformation types that ensure the quality and integrity of data. Without this step, businesses can’t be confident in the data being migrated or integrated into the target system—which can mean weeks or even months of effort and budget lost!
The Load step concludes the ETL process with the loading of the extracted and transformed data into the end system. The successful completion as well as complexity of this step is dependent on the volume of data, structure of that data, and frequency at which you will load that data.
Data management best practices for successful ETL processes
When the ETL process pulls data from multiple sources, some may be historic or legacy data and therefore may not be optimized for business use and analytics. Through ETL, it can be migrated to a central data warehouse in a standardized format, which is much more suitable for business intelligence.
With the importance placed on being able to make data-driven decisions, it is critical to take proactive measures to ensure data quality. Cleansing, validating, deduplicating, and profiling data are foundational best practices that not only provide faster time-to-value for ETL and other data integration and migration processes, but empower organizations to make far better use of the data they have.
Find out how we help businesses undergo successful ETL processes.