Data analysis, capturing, and interpretation has become key factor in business success. ETL and ELT are two types of data management practices that help organizations make data-driven decisions. To deliver these decisions and ensure that they are interpreted into business intelligence solutions, ETL and ELT are practiced, depending on the organizational requirements.
Data analysis, capturing, and interpretation has become key factor in business success. ETL and ELT are two types of data management practices that help organizations make data-driven decisions. To deliver these decisions and ensure that they are interpreted into business intelligence solutions, ETL and ELT are practiced, depending on the organizational requirements.
With the increasing volume of data and its extraction from a progressively increasing number of sources, making business-appropriate decisions has become complex. With ETL and ELT, we can make this entire process a lot more efficient and rewarding.
What is ELT and ETL?
ETL and ELT are data integration processes. With these, we can shift or move raw data to a database. These databases are usually called data lakes or data warehouses. To send the data to the desired location, either ETL or ELT is implemented. ETL and ELT processes work with cloud-managed services, which helps provide universal access to interpreted data based on access control settings.
1. Extract, Transform, Load (ETL)
One of the ways organizations can use to store and manage data includes collecting, reformatting, and storing it on the desired server. After extraction, the data is formatted based on predefined parameters.
This is the staging area, where data is transformed into understandable bits, visualizations, patterns, and trends. In the load phase or stage, the formatted data is moved to a data warehouse or data lake. From here, anyone with access to the storage server can access the data and make business decisions.
ETL origins go back to the decade of 1970s when companies were starting to collect large amounts of data from multiple sources. To process, they started to arrange this data into different datasets.
This led to a significant issue of disjointed and cluttered databases. And as these complex databases began to increase, collecting data was quickly becoming a redundant exercise with no beneficial outcome.
Then ETL arrived and provided businesses with an effective way to manage large datasets with ease. For the next three decades, ETL was the mainstay for organizations to convert raw data into business intelligence.
For Whom ETL is an Ideal Data Integration Method?
Disperse Data Sources
Businesses that have diverse and spread-out data sources will benefit the most from ETL. These are companies that have customers, suppliers, partners, and stakeholders in different regions addressed via multiple ventures. ETL helps these businesses collect data from different repositories and formats in unison, then load everything onto a target location.
Shift from Legacy Systems
A particular use case of the ETL system is when organizations working with legacy systems want to implement a collective data shift to a modern system. In this case, as well, the ETL process can extract, transform the data into an understandable format, and load it to the target location. So, we can say that ETL is an important part of digital transformation solutions.
So, ETL is best suited for situations where you have multiple environments and have to process data collectively before viewing it on a separate medium. Original source: ETL vs ELT