BIG DATA AND ITS ANALYTICAL TECHNIQUES
Abstract
In the era of information abundance, the surge in data volume has given rise to unprecedented challenges and opportunities. This article delves into the multifaceted world of Big Data Analytics, exploring its foundations, technologies, methodologies, applications, and future trends. The foundations rest on the quintessential characteristics of volume, velocity, variety, veracity, and value, extended by the 3Vs model to include scalability, real-time processing, and data quality assurance.
Keywords
Big Data Analytics, Data Volume, Data Velocity, Data Variety, Data Veracity, Data Value, 3vs Model, Scalability, Real-Time Processing, Hadoop Ecosystem, Apache Spark, Nosql Databases, Cloud Computing, Data Ingestion, Data Preprocessing, Data Governance, Data Security.
References
- Search for articles in reputable journals such as the "Journal of Big Data," "Big Data Research," and "IEEE Transactions on Big Data."
- Refer to authoritative books on Big Data Analytics by authors like Doug Laney, Thomas H. Davenport, and Viktor Mayer-Schönberger.
- Look for influential papers in conferences like the International Conference on Big Data (IEEE Big Data) or ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
- Reports from organizations like Gartner, Forrester, and McKinsey often provide insights into the latest trends and technologies in Big Data Analytics.
- White papers from technology companies and organizations specializing in data analytics tools and solutions.
- Refer to documentation provided by organizations behind key technologies, such as Apache Hadoop, Apache Spark, and various databases.