Data volume refers to the amount of data collected or processed. It impacts storage requirements, processing power, and data management strategies.
Data quality management in Big Data projects face challenges such as data volume, variety, velocity, veracity, and data integration. Ensuring the quality of data in such projects is crucial for accurate analysis and decision-making. Challenges include data validation, data cleansing, data integration, data privacy and security, and data governance.
Data integration for IoT applications poses significant challenges due to various factors such as volume, data heterogeneity, security, and real-time processing requirements. Integration of diverse data sources, handling large data volumes, ensuring data privacy and security, and addressing real-time processing needs are some of the key challenges in data integration for IoT applications.
