Optimizing Modern Cloud Data Warehousing Solutions: Techniques and Strategies

Registration ID: IJNRD_226641

Published ID: IJNRD2303501

DOI: Click Here to Get

ER. FNU ANTARA , PROF. (DR.)PUNIT GOEL , ER. OM GOEL

• Cloud Data Warehousing • Optimization Techniques • Performance Optimization • Automated Scaling • Query Optimization • Scalability • Horizontal Scaling • Vertical Scaling • Elastic Scalability • Cost Optimization • Pricing Models • Data Compression • Security and Compliance • Data Integrity • Comparative Analysis

As organizations increasingly migrate to cloud-based environments, optimizing modern cloud data warehousing solutions has become a critical focus for ensuring efficient data management and analytical capabilities. This research explores the techniques and strategies for optimizing contemporary cloud data warehousing solutions, aiming to provide actionable insights for organizations seeking to enhance their data warehousing performance, scalability, and cost-efficiency. The study begins by examining the fundamental principles of cloud data warehousing, highlighting the differences between traditional on-premises systems and modern cloud-based solutions. It delves into key cloud data warehousing platforms, such as Amazon Redshift, Google BigQuery, and Snowflake, assessing their architecture, features, and optimization techniques. One significant area of focus is performance optimization. The research investigates techniques such as data partitioning, indexing, and materialized views, which are essential for improving query response times and overall system performance. It also explores the role of automated scaling, where cloud platforms dynamically adjust resources based on workload demands, enhancing both performance and cost-efficiency. The study emphasizes the importance of query optimization practices, including the use of efficient SQL queries and optimization hints, which contribute to reduced query execution times and improved system throughput. Another crucial aspect addressed is scalability. The research evaluates strategies for scaling cloud data warehousing solutions to handle varying data volumes and user loads. Techniques such as horizontal scaling, where additional resources are added to distribute the load, and vertical scaling, where existing resources are upgraded, are discussed. The paper also explores the concept of elastic scalability, a hallmark of cloud environments, which allows for seamless adjustments in capacity based on real-time needs. Cost optimization is a critical concern for organizations adopting cloud data warehousing solutions. The study examines methods to manage and reduce costs, such as selecting appropriate pricing models (e.g., on-demand vs. reserved instances), optimizing storage and compute resource usage, and employing cost monitoring and management tools. Strategies for minimizing data transfer and storage costs, including data compression and lifecycle management, are also discussed. The research further explores security and compliance considerations, addressing how to optimize data warehousing solutions while maintaining robust security measures and regulatory compliance. Techniques for securing data at rest and in transit, as well as managing access controls and auditing, are evaluated to ensure that optimization efforts do not compromise data integrity and security.

Published Paper   E-Certificate

ER. FNU ANTARA, PROF. (DR.)PUNIT GOEL, & ER. OM GOEL (March-2023). Optimizing Modern Cloud Data Warehousing Solutions: Techniques and Strategies. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), e772-e783. https://ijnrd.org/papers/IJNRD2303501.pdf

Volume 8 Issue 3, March-2023

Pages : e772-e783

Paper Reg. ID: IJNRD_226641

Published Paper Id: IJNRD2303501

Downloads: 000121976

Research Area: Engineering

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303501.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303501

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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