How ETL Provides Real-Time Data for Data-Driven Applications?

In this digital era full of data, data-based applications have become very important for various people industry to help make smarter and faster decisions. To be able to provide accurate and relevant information, this application requires access to real-time data. Process ETL (Extract, Transform, Load) is one of the main methods for providing such data in an efficient and structured manner.

ETL is a process that involves three main steps:

1. Data is taken from various sources, such as databases, application, or cloud services.
2. Extracted data is modified as needed, such as merging, cleaning, or reformatting.
3. The transformed data is loaded into the target system, as data warehouse or analytical applications.

When this process is carried out efficiently, ETL allows data to be available in near real-time or even real-time, so that applications can immediately use the data to analysis or other needs.

ETL Challenges in Providing Real-Time Data

Although ETL is very useful, providing data in real-time is not easy. Some of the main challenges include:

• With the ever-increasing amount of data, the extraction process and transformation must be able to handle large volumes without sacrificing speed.
• \Data often comes from multiple sources, such as ERP, CRM, and IoT sensors, so they need effective synchronization.
• Converting raw data into a ready-to-use format requires significant time and computing power.

ETL Strategy for Real-Time Data

1. By processing only new or changed data, ETL can provide data more quickly and efficiently than processing the entire data every time.
2. Technology streaming allows ETL to process data continuously as it comes in, instead of waiting for a full batch. This is especially effective for real-time data such as transactions or sensor monitoring.
3. Minimizing unnecessary processes and using efficient compression and encryption technologies can speed up data transformation. For example, using an analytical engine like Spark or Flink for real-time data transformation.

Benefits of Real-Time ETL for Data-Driven Applications

By implementing real-time ETL, data-driven applications can:

• The app can display trends and analysis in real time, enabling faster decision making.
• Real-time available data can help reduce the time required for certain actions, for example, inventory management or demand prediction.
• Applications powered by real-time data can provide a more responsive and relevant experience for users.

Answering the needs of modern businesses, Thrive has designed Keloola Xchange as an advanced ETL platform that supports real-time data processing, so businesses can utilize data more effectively and adaptively. Contact us now to find out how Keloola Xchange can help your data-driven applications provide real-time data reliably and efficiently.