Paper Title
Key Technologies and Methods for Building Scalable Data Lakes
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Keywords
Keywords: Data lakes, scalability, big data, cloud-based solutions, distributed storage, Apache Hadoop, Apache Spark, AWS, Azure, data ingestion, storage optimization, metadata management, data governance, data-driven decision-making.
Abstract
Abstract: In the era of big data, organizations face the challenge of efficiently storing, processing, and analyzing vast amounts of data. Data lakes have emerged as a solution to address these needs, offering a flexible and scalable architecture for managing diverse data types. This paper explores the key technologies and methods essential for building scalable data lakes, focusing on the integration of cloud-based solutions, distributed storage systems, and advanced data processing frameworks. It discusses the role of technologies such as Apache Hadoop, Apache Spark, and cloud platforms like AWS and Azure in enhancing data lake performance and scalability. Furthermore, the paper examines best practices for data ingestion, storage optimization, and metadata management to ensure efficient data retrieval and governance. By analyzing case studies and real-world implementations, this study provides insights into the benefits and challenges of deploying scalable data lakes in various industries. The findings aim to guide organizations in leveraging data lakes to unlock the potential of big data, facilitating data-driven decision-making and innovation.
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How To Cite (APA)
SHANMUKHA EETI, , SHALU JAIN,, & DR. POORNIMA TYAGI, (July-2022). Key Technologies and Methods for Building Scalable Data Lakes. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(7), 1-21. https://ijnrd.org/papers/IJNRD2207179.pdf
Issue
Volume 7 Issue 7, July-2022
Pages : 1-21
Other Publication Details
Paper Reg. ID: IJNRD_226553
Published Paper Id: IJNRD2207179
Downloads: 000121990
Research Area: Engineering
Author Type: Indian Author
Country: GHAZIABAD, UP, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2207179.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2207179
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