Articles
Using AI to Enhance Data Engineering and ETL
by Swagatika B. Digital MarketerAs data analytics becomes highly important to improve enterprise business performance, data aggregation (from across the enterprise and from outside sources) and adequate preparation of this data stand as critical phases within the analytics lifecycle.
An astonishing 40-60% of the overall effort in an enterprise is dedicated to these foundational processes.
It is here that the raw datasets are extracted from source systems, and cleaned, reconciled, and enriched before they can be used to generate meaningful insights for informed decision-making.
However, this phase often poses challenges due to its complexity and the variability of data sources.
Enter Artificial Intelligence (AI). It holds the potential to significantly enhance how we do data engineering and Extract, Transform, Load (ETL) processes. Check out our AI enabled ETL accelerator solution (Intelligent Data Accelerator) here.
In this blog, we delve into how AI can enhance data engineering and ETL management. We focus on its pivotal role in
- Setting up initial ETLs and
- Managing ongoing ETL processes efficiently.
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About Swagatika B. 
Digital Marketer
15 connections, 0 recommendations, 59 honor points.Joined APSense since, February 6th, 2023, From Jersey City, United States.
Created on Oct 9th 2023 06:18. Viewed 49 times.
