Using Big Data and Kafka to Track Resource Utilization in Real Time

With the scale of data in modern systems growing rapidly, the need to perform analytics in a fast and efficient, real-time way is critical as different industries - from healthcare to logistics, even for cloud computing - have to utilise a resource efficiently. Real-time Resource Utilization Analysis: Natural Language Processing, Big Data tools and Streaming Frameworks are used for monitoring, getting insight and dynamically optimizing resources. Link: Real-Time Analytics You cannot make it with Apache Kafka: A High-Throughput distributed Streaming Platform. Here in this paper we proposed strong basement for analysing on Big Data including Kafka Streams for real-time resource utilisation analysis framework and show its high executing efficiency, faster implementations, enhancing reduce for improving executing systems efficiency. The suggested framework also addresses the challenges associated with real-time ingestion, processing, and analysis of streaming data, especially considering the use of Kafka Streams as a fundamental element for distributed stream processing. Learn More About Kafka Streams Builds a Unified Processing PlatformKafka Streams can be used to build real-time business applications, or for creating real-time transformations, or processing streams as continuous business transactions. Measuring the health of the application is thus designed to be scalable, fault-tolerant and abstract over shoe data source sow the data it will act on will be different (i.e. an IoT device or a system log file or an application metrics etc.). Because of distributed architecture of Kafka ensures that the system are highly available and fault tolerant that effectively means even if the analysis fails/terminate the process it does not get interrupted. This framework harnesses advanced analytics models that provide intensive, granular insights, one of its strongest features is monitoring the diversity of resource utilization. These ML-driven models analyse patterns of resource usage, identify underused resources, and determine areas of usage optimisation. Best practices for predictive analytics include utilizing a framework with Big Data management which ensures that any historical data is being taken into account in training models retroactively; since cloud computing and storage make these historical data sets always available, in conjunction with data from Hadoop or proprietary Big Data storage solutions. Moreover, the framework is conducive to data visualization tools that can give stakeholders dashboards of real-time usage and efficiencies. Traditional resource-availability monitoring systems are batch oriented system & these systems have limitation & they cannot fulfill the dynamic aspect. So they really need to work on that aspect, but they have a good solution to solve these issues after integrating Kafka Streams with big data technology.