• Vinayak Pillai

Big data analytics has become a critical success factor for organizations seeking to perform large-scale data analyses to derive value. This paper aims to present an overview of the main approaches to Big Data and Big Data processing, specifically ingestion, storage, cleaning, and analysis, as well as the integration with machine learning and artificial intelligence techniques. The research uses cross-sectional, quantitative, and qualitative data from case industries, including healthcare, telecommunication, and finance. Apache Hadoop, Spark, and Kafka are the tools most investigated for their effectiveness in dealing with data. This paper has shown that, by integrating both the batch and stream processing methodologies, it is possible to make a complete analysis for large data and at once obtain scalability. Data governance, quality management, and data privacy concerns are also discussed, and future study avenues are identified. The findings contribute focused insights into how organizations can apply big data technologies to utilize the data harvested and convert raw data into insights for company decision-making processes.

Big Data Analytics, Data Processing Techniques, Machine Learning and AI Integration, Batch and Stream Processing, Data Governance and Privacy, Apache Hadoop, Spark, and Kafka.

"Techniques for Processing and Analyzing Large Data Sets Using Big Data Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.e50-e64, October-2024, Available :http://www.jetir.org/papers/JETIR2410406.pdf

"Techniques for Processing and Analyzing Large Data Sets Using Big Data Analytics", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppe50-e64, October-2024, Available at : http://www.jetir.org/papers/JETIR2410406.pdf