Building an AI-Ready Data Strategy Using Lakehouse Technology

This article explores how organizations can leverage Lakehouse technology to build robust AI-ready data strategies. Lakehouse architecture represents a paradigm shift in data management by unifying data lake flexibility with data warehouse reliability. The integration of this technology enables organizations to overcome traditional barriers to AI implementation through unified storage, efficient processing, and comprehensive governance. By examining key components including data ingestion, preparation, metadata management, governance, and scalable infrastructure, the article illustrates how Lakehouse technology establishes a foundation for advanced AI applications like predictive analytics, recommendation systems, natural language processing, and automated decision-making. The article addresses common implementation challenges and provides solutions for data governance, quality assurance, infrastructure scaling, and integration with AI/ML tools, offering organizations practical guidance for transforming their data infrastructure into a catalyst for AI innovation.

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Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-08
DOI
https://doi.org/10.32996/jcsts.2025.7.3.76
Citations
1
Primary Topic
Reservoir Engineering and Simulation Methods
Type
article
Field-Weighted Citation Impact
1.15
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article

Building an AI-Ready Data Strategy Using Lakehouse Technology

Jyoti Aggarwal
1 citations
Journal of Computer Science and Technology Studies
Reservoir Engineering and Simulation Methods
1.15
article

Building an AI-Ready Data Strategy Using Lakehouse Technology

Jyoti Aggarwal
article en
1 citations

Abstract

This article explores how organizations can leverage Lakehouse technology to build robust AI-ready data strategies. Lakehouse architecture represents a paradigm shift in data management by unifying data lake flexibility with data warehouse reliability. The integration of this technology enables organizations to overcome traditional barriers to AI implementation through unified storage, efficient processing, and comprehensive governance. By examining key components including data ingestion, preparation, metadata management, governance, and scalable infrastructure, the article illustrates how Lakehouse technology establishes a foundation for advanced AI applications like predictive analytics, recommendation systems, natural language processing, and automated decision-making. The article addresses common implementation challenges and provides solutions for data governance, quality assurance, infrastructure scaling, and integration with AI/ML tools, offering organizations practical guidance for transforming their data infrastructure into a catalyst for AI innovation.

Journal of Computer Science and Technology StudiesVol. 7(3)
Carnegie Mellon University (US)
Openalex Percentile: Top 24%
Reservoir Engineering and Simulation Methods
1.15
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