Deciphering the Digital Tsunami: An In-depth Exploration of Big Data’s Impact on Decision-Making, Innovation, and Business Transformation (Published)
In the contemporary landscape of information technology, the advent of big data has catalyzed a digital tsunami, reshaping the foundations of decision-making, fueling innovation, and driving profound business transformations. This research paper, titled “Deciphering the Digital Tsunami,” undertakes an extensive exploration of the multifaceted impact that big data exerts across these critical domains. The investigation begins by unraveling the intricate dynamics of big data, going beyond its sheer volume to delve into the nuanced aspects of velocity, variety, and veracity. By dissecting these dimensions, the paper provides a comprehensive understanding of the challenges posed by the influx of diverse and rapidly evolving data streams. Furthermore, the study delves into the profound influence of big data on decision-making processes within organizations. It scrutinizes the ways in which data-driven insights are shaping strategic choices, optimizing operational efficiency, and fostering a culture of evidence-based decision-making. Through real-world case studies and empirical analyses, the paper offers insights into the transformative potential of big data in enhancing decision-making precision and efficacy.
Keywords: Big Data, Business Transformation, Data Streams, Data-driven Insights, Decision Making, Decision Support Systems, Digital Transformation, Information Technology, Innovation, Organizational Strategy, Variety, Velocity, Veracity, data analytics
Parallel Query Processing for Big Data: Architectures and Performance (Published)
In the era of big data, organizations are faced with the daunting task of efficiently processing vast amounts of data to extract valuable insights. Traditional databases and data processing systems often struggle to cope with the scale and complexity of these datasets. To address this challenge, parallel query processing has emerged as a key technique, enabling the distribution of query workloads across multiple computing resources. This abstract explores the architectures and performance considerations associated with parallel query processing for big data. Architectures: MPP (Massively Parallel Processing) Databases: Many big data systems leverage MPP databases, which distribute data across multiple nodes and employ parallelism to execute queries efficiently. We delve into the principles underlying MPP databases and discuss how they partition and parallelize data for rapid query execution. Hadoop MapReduce: The MapReduce programming model is widely used in big data processing. We examine how MapReduce divides tasks into map and reduce phases, leveraging parallelism to process data efficiently. Additionally, we discuss the Hadoop ecosystem, including tools like Hive and Pig, which simplify query processing on Hadoop clusters. This abstract serves as an introduction to the complex and evolving field of parallel query processing for big data. The architectural insights and performance considerations discussed here are crucial for organizations seeking to harness the power of big data analytics while optimizing query performance.
Keywords: Apache Spark, Big Data, Data Distribution, Hadoop MapReduce, MPP Databases, Parallel Query Processing
Big Data: Technology, Opportunities and Challenges (Published)
Dealing with large amount of data and running analytics on those data is becoming challenging with rapidly increase in various types of data. Big data is the technology which deals with such large amount of data analytics. It covers wide range of application areas from managing data of social networking sites to the large amount of data on ecommerce portals for decision making. In this paper an attempt is made to present a review of State of Art technology in Big Data, its importance, major benefits and challenging in this domain.
Keywords: Big Data, HDFS, Hadoop, Hive, MapReduce, Medical Image Processing, Remote Sensing, Security