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The study analyzes the optimization techniques in Apache Spark using a secondary qualitative methodology. The research outlines three main themes: performance bottlenecks, optimization strategies, and practicability. This can help to identify the challenges as well as their strategies for improving the efficiency of Spark. Results indicate the existence of inefficiencies based on poor memory management, skew, as well as poor tuning. Combined optimization techniques Adaptive query execution, Caching, and dynamic resource allocation, make a significant impact on scalability and processing speed. The combination of all these methods allowed the study to fill the gaps in the literature and present a comprehensive outlook on the comprehensive sustainable ability to improve the work of Spark. The results provide useful information to data engineers and analytics students.
Keywords:
Apache Spark, Performance Optimization, Big Data Analytics, Distributed Computing, Resource Management, Scalability, Execution Efficiency
Cite Article:
"Enhancing Data Processing Efficiency Using Apache Spark: Techniques and Optimization Strategies", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 9, page no.940-944, September-2022, Available :http://www.ijrti.org/papers/IJRTI2209130.pdf
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000267
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator