International Journal For Multidisciplinary Research

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Data Lake Design Patterns: Building Scalable Architectures for Enterprise Analytics

Author(s) Venkata Raghavendra Vutti
Country United States
Abstract As enterprises battle exponential data expansion and increasingly complicated analytics requirements, the
demand for scalable, efficient data infrastructures has never been more vital. This extensive technical
article gives a complete approach to developing and implementing a modern data lake architecture using
AWS cloud services. A production-grade solution leverages Amazon S3 for persistent storage, AWS Glue
for robust ETL processing, and Amazon Athena for cost-effective querying capabilities. The design
integrates fundamental features of data governance, security, and operational excellence while solving
typical difficulties in enterprise data management. A deep investigation of component layers-from data
ingestion to storage organization, processing pipelines, and query optimization provides practical insights
for developing a scalable data platform. The article covers essential concerns for data architects and
engineers, including storage optimization tactics, data quality frameworks, monitoring systems, and cost
control procedures. Real-world implementation patterns and case studies highlight how firms can migrate
from classic data warehousing systems to modern, cloud-native data architectures that enable advanced
analytics and machine learning projects.
Keywords Keywords: Data Lake Architecture, AWS Cloud Infrastructure, ETL Processing, Scalable Storage Systems, Enterprise Data Management.
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-18
Cite This Data Lake Design Patterns: Building Scalable Architectures for Enterprise Analytics - Venkata Raghavendra Vutti - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.33251
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.33251
Short DOI https://doi.org/g8wkhk
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