OMFYS - Blog-2

24-June-2023

Data plays a crucial role in any business decision-making process. This data can be utilized to gain insights and make conscious decisions. However, to extract insights from data, it needs to be processed and transformed into a structured form. This is where ETL (Extract, Transform, Load) comes into play. ETL is a standard process used by businesses to extract data from multiple sources, transform it into a consistent format, and load it into a target database.

However, with the increasing amount of data, ETL processes are becoming more tedious and time-consuming. This has led to the emergence of ELT (Extract, Load, Transform), a process that goes beyond ETL. ELT involves extracting data from multiple sources and loading it into a centralized location first.

The transformation process occurs after the data is loaded into the data warehouse or data lake.

This allows data analysts and scientists to use powerful tools and frameworks like SQL to transform the data in real-time.

How does ELT helps to increase efficiency on Analytics and Business Insights?

In the realm of analytics and business insights, ELT can help increase efficiency in several ways:

Faster Data Processing:

With ELT, data is first loaded into the target system before the transformation. This reduces the time required to transform data, as the processing power of the target system can be utilized for this purpose. This allows businesses to gain insights from their data faster, which can lead to quicker and more informed decision-making.

Scalability:

By leveraging cloud computing, businesses can significantly reduce their IT infrastructure costs. Cloud providers offer pay-as-you-go models, which means that businesses only pay for the computing resources they use, thus reducing capital expenditures. Businesses can reduce their operational costs by outsourcing their IT management and maintenance to cloud providers.

Flexibility:

Extracting—Loading—Transforming allows businesses to work with a variety of data sources, as data can be extracted from different systems and then loaded into the target system. This flexibility allows businesses to integrate different types of data sources and generate insights from them, leading to more comprehensive insights.

Cost-Effectiveness:

ELT eliminates the need for a separate transformation process, which can result in cost savings. Additionally, ELT can help businesses optimize their storage and processing costs, as only the necessary data is loaded into the target system, and processing resources can be scaled as needed.

In conclusion, while both ETL and ELT have their own advantages and disadvantages, ELT offers certain benefits that make it a better option for handling large volumes of data and supporting real-time analytics.

By loading data into a target system first and then performing transformations, ELT can provide greater flexibility and efficiency, helping organizations make more informed decisions based on the latest data available.

Overall, Extracting—Loading—Transforming can help businesses increase efficiency in their analytics and business insights processes, leading to better decision-making and more accurate insights which can be critical for organizations’ growth.

Author Name:-Antara Mukherjee
Author Email-Id:- antara.mukherjee@omfysgroup.com

Advantages of ELT over ETL for Data Analytics and Business Insights

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