Visitors: 0

ETL (Extract, Transform & Load)

person holding white Samsung Galaxy Tab

ETL stands for Extract, Transform Load. 

It is a data integration process, that combines data from multiple sources into a single, consistent data store that is loaded into a data warehouse or other system.

Databases started growing popular in the 1970s, and then only ETL was introduced as a process for integrating and loading data for computation and analysis, which became the primary method to process data warehousing projects.

Because ETL helps in the integration and storing of data at a place in a data warehouse, it provided the foundation for data analytics and machine learning workstreams. Through a series of business rules, ETL cleanses and organizes data in a way that addresses specific business or HR intelligence needs, which can improve back-end processes or end-user experiences. ETL is often used by an organization to:

  • Extract data from systems
  • Clean the data to improve data quality
  • Gather data into a centralized database (Data Warehousing)

Is there a difference between ETL and ELT?
Yes, there is. ELT is usually used to get data from huge sources, because of which their order of operation differs from ETL.

ELT exports the data from the source locations, but instead of loading it to a staging area for transformation, it loads the raw data directly to the target datastore to be transformed as needed.

While both processes maintain similar techniques like data repositories, such as databases, data warehouses, and data lakes, each process has its advantages and disadvantages. ELT is particularly useful for high-volume, unstructured datasets as loading can occur directly from the source. ELT can be more ideal for big data management because it does not need much upfront planning for data extraction and storage. 

The ETL process on the other hand, requires more definition at the onset. Specific data points need to be identified for extraction along with any potential "keys" to integrate across disparate source systems. Even after the work is over, the business rules for data transformations need to be constructed. This work usually have dependencies on the data requirements for a given type of data analysis, which will determine the level of summarization that the data needs to have. 

Although use of ELT has become more prominent because of use of cloud databases, but there are certain best practices to be established as this is quite new in the market comparatively.

How ETL works?
The easiest way to understand how ETL works is to understand what happens in each step of the process.

Extraction: During data extraction, raw data is copied or exported from source locations to a staging area. Data management teams can extract data from a variety of data sources, which can be structured or unstructured. Those sources include but are not limited to;

  • SQL
  • NoSQL
  • ERP Systems
  • Email
  • Files
  • Web Pages

Transformation: In the staging area, the raw data undergoes data processing. Here, the data is transformed and consolidated for its intended analytical use. This phase usually involves;

  • Cleaning and Filtering of data
  • Validating and Authenticating the data
  • Performing analysis on the data so gathered
  • Following the data governance and encrypting or protecting the data as per required standards
  • Formatting the data as per the format of data warehouse

Loading: In this stage, which is also the last one, the transformed data is moved from the staging area into a target data warehouse. 

This is done by initially loading the data into the target data warehouse, and the maintaining it or updating it as per the requirements. This is usually done when the traffic of the data source is lowest (i.e., the off-hours).

Benefits of using ETL data integration method
One of the greatest benefits of ETL data integration method is that, all the filtering, cleaning, and computation of data is done before hand, which is a time consuming process.

This really helps in cases where we have to ensure that the data provided is of best quality. For example, when we have to upload Human Resource Data, we need to ensure that proper filtering, calculation of leaves, and other analysis is done, and then we can upload the data in our data warehouse, which enables us to use it readily for information extraction.