Data lakes versus data warehouses: choosing the right approach for big data analytics

In the era of big data, organizations face critical decisions when selecting between data lakes and data warehouses to meet their analytics requirements. This article presents a comprehensive comparative analysis of these two predominant data management architectures, emphasizing their structural di...

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Veröffentlicht in:Journal of Electrical Systems and Information Technology Jg. 12; H. 1; S. 89 - 21
Hauptverfasser: Mezzoudj, Saliha, Khelifa, Meriem, Saadna, Yassmina
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025
Springer Nature B.V
SpringerOpen
Schlagworte:
Architecture
Artificial Intelligence
Big Data
Big data analytics
Blockchain
Business analytics
Business intelligence software
Case studies
Comparative analysis
Cost analysis
Data analysis
Data lake
Data management
Data warehouse
Data warehouses
Decision making
Design
Edge computing
Electrical Engineering
Electrical Machines and Networks
Engineering
Hadoop
Information Systems and Communication Service
Lakehouse architecture
Machine learning
Management information systems
Mechanical Engineering
Organizations
Power Electronics
Quantum computing
Relational data bases
Review
Structured data
Technological change
Trends
Unstructured data
Cloud computing
Scalability
Hadoop
Data lake
Kafka
Big data analytics
Data governance
Business intelligence
Data management
Lakehouse architecture
Spark
Cost-effectiveness
Artificial intelligence
Data warehouse
ISSN:2314-7172, 2314-7172
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Zusammenfassung:In the era of big data, organizations face critical decisions when selecting between data lakes and data warehouses to meet their analytics requirements. This article presents a comprehensive comparative analysis of these two predominant data management architectures, emphasizing their structural differences, functional capabilities, and suitability for diverse analytics workloads. Data lakes offer scalable, cost-effective storage for raw, unstructured, and semi-structured data, supporting advanced analytics and machine learning applications. In contrast, data warehouses provide optimized, schema-on-write frameworks for fast querying and reliable reporting on structured data. Through detailed examination of architectural designs, integration with big data tools including Hadoop, Spark, and Kafka, and evaluations based on performance, scalability, cost, and governance, this paper provides organizations with evidence-based guidance to align their data strategies with business objectives. Case studies from healthcare and retail sectors illustrate practical implications of each approach, while emerging trends such as lakehouse architectures, AI integration, blockchain security, edge computing, and quantum computing highlight future directions. The findings support for a hybrid data management solution that leverages the strengths of both data lakes and warehouses to enable robust, scalable, and innovative big data analytics.
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ISSN:2314-7172
2314-7172
DOI:10.1186/s43067-025-00275-0