ETL vs. ELT: Choosing the Right Data Integration Approach for Data Professionals

Whether you are a data analyst who prefers structured data to analyze information or a data scientist who likes to process raw data, your choice of organizing data matters.

But wait, why is this choice so critical?

Data integration,like a puzzle piece, is the process of combining multiple data sources and datasets from various locations in a single data repository. Data integration is like bringing all your data pieces to the same table. It's crucial for data professionals like you because it stops data from being locked away and hard to use. This means you get more data to work with, and that leads to better insights. 

With data integration:

1. Your data management gets better.

2. The quality of your data goes up.

3. Your data becomes easily accessible

4. Plus, it saves you a ton of time and effort.

   
   

There are two important ways to organize data and process information: ETL & ELT. 

In today's guide, we will explore these two approaches and help you decide which is more suitable for your data adventures. The choice between ETL and ELT can significantly impact your data journeys, affecting everything from data quality to project flexibility.

Understanding ETL (Extract, Transform, Load) 

In this section, we'll delve into the ETL approach, which stands for Extract, Transform, Load,

Defining ETL - The Three Essential Steps

ETL is a process that extracts, transforms, and loads data from various sources like API, a database, or a file to a single data source that is loaded into a data warehouse. With the help of the ETL process, the extracted data is then transformed to match the requirements of the destination system.

Data Extraction: Data extraction is the first stage of the ETL process. It involves collecting data from various sources such as websites, databases, or files. Raw data is extracted from source locations to a staging area during data extraction. Those sources comprise of flat files, email, web pages etc.

Data Transformation: In the staging layer, the raw datasets undergo processing before being loaded into a data warehouse for analysis and reporting.In this phase, data is refined and transformed. It starts with filtering, cleansing, and de-duplication to ensure data quality.

Given the volume and variety of sources that raw datasets come from, the transformation stage is widely known as the most critical step in the ETL process, as it optimizes data integrity and ensures that the data is fully compliant & available for use when it reaches a new location.

Load: The transformed data is moved from the staging area into a target data warehouse in this last step. Usually, this includes an initial data load, followed by regular updates with incremental changes and occasionally, full data refreshes to replace the warehouse contents. In most organizations employing ETL, the process is automated, clearly defined, operates continuously, and is driven by batch processing. Typically, ETL procedures are scheduled during off-peak hours when both source systems and the data warehouse experience minimal traffic.