With the increase in the complexity of enterprise systems, processes and operations, the need for monitoring and analyzing such methods have also grown over the years. Several companies have implemented exceptional monitoring tools, software and applications that go far beyond uncomplicated resource utilization and report generation to monitor single method invocations and trace individual transactions across geographically distributed systems. This high-level of detail permits a more accurate form of analysis and prediction but comes at the price of accumulating high data rates, that is big data.
To maximize the beneﬁt of data analysis and monitoring, the data has to be stored for an extensive period of time for ulterior analysis. This modern wave of big data analytics imposes unique challenges, especially for performance monitoring applications in enterprises. The monitoring data has to be stored in a system that can support the high data rates and at the same time enable an up-to-date view of the underlying infrastructure. With the arrival of innovative key-value stores, a variety of data storage systems have been developed that are created with a focus on scalability and high data rates that are predominant in the current technological era.
Big Data Analytics for Enterprise
Large scale enterprises and businesses nowadays can comprise of complete data centers with thousands of servers across different branches geographically. These systems are heterogeneous and have many interdependencies which make their administration a very complex task. To give administrators an on-line view of the system health, performance monitoring frameworks have been developed. Nevertheless, from an industry perspective, in domains of stringent response time and availability requirements, a more accurate view of the monitored system is needed.
Enterprise application tools and instruments retrieve information about response times of speciﬁc services or combinations of services, as well as about failure rates, resource utilization, etc. Various monitoring targets such as the acknowledgment rate of a speciﬁc servlet or the CPU utilization of a host are customarily referred to as metrics. In the current enterprise systems, it is not unusual to have several different metrics that are reported from a particular host machine. In order to allow for detailed on-line as well as off-line analysis of this big data, it is persisted at a centralized store that can be controlled by an application. With the continuous growth of enterprise systems, more data is generated resulting in Big Data that extends over multiple data centers. With enterprise application solutions, these data can be seamlessly monitored, tracked and handled with more detailed accuracy, which in turn creates a considerable return of investment for the enterprise.
The Different Analytics Approaches Of Big Data Using Enterprise Application Solutions
Analytics involves the use of statistical techniques (inputs from IoT devices, customer data obtained through web application, enterprise application solutions and so on), information system software (data mining, sorting routines), and operations research methodologies (linear programming) to explore, discover, and monitor patterns or trends in data for enterprises. The analytics process in big data using enterprise application solutions reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. It has extensive applications which include marketing, object recognition, credit risk assessment fraud detection, etc.
There are several types of analytics approaches, and these can be categorized as:
This is a simple statistical technique or graph that describes what is contained in a data set or database. Descriptive statistics, including measures of central tendency (mean, median, mode), measures of dispersion (standard deviation), charts, graphs, sorting methods, frequency distributions, probability distributions, and sampling methods. The result of this process can be used to ﬁnd possible business-related opportunities.
Predictive analytics is an application of advanced statistical, information software, or operations research methods to identify predictive variables and build predictive models into a descriptive analysis. The results here predict possibilities in which the enterprise can take support to enhance their products and services.
This analytics uses the study of previous data to determine the cause of certain events. Therefore, diagnostic analytics increases descriptive analytics by enquiring why certain events occurred using the patterns in the data collected by enterprise applications. The diagnostic analytics process is efficiently utilized in machine health monitoring and prognosis, fault detection and maintenance.
Prescriptive analytics deploys the power of decision science, management science, and operations research methodologies (applied mathematical techniques) to make the best use of allocated resources. Resources are allocated to take advantage of the predicted opportunities.
Big Data Use Cases In Enterprise Application Development
Big data accumulation predominantly consists of a methodology for flexibly controlling computing resources and storage, scalable performance supervision, along with data transfer via fast networks. The outcome is improved performance and scalability for enterprises with the implementation of smart applications that rely on as well as accumulate big data. It is also capable of providing another data point that contributes to self-reported descriptions using big data techniques.
Big data use cases in enterprise and mobile app development services are classified as below:
• More elaborate input into business intelligence, querying, reporting, exploring, including quick and easy implementations of monitoring, filtering and indexing.
With the implementation of big data, enterprise mobile applications speed up aggregation for reporting, report generation, current enterprise trend analysis, and also comprehensive information retrieval.
• It provides enhanced administration for general data management operations, with the majority big data concentrating on log storage, data storage and archiving. An enterprise application helps in monitoring, sorting, extraction/transformation/ loading (ETL) processing, other sorts of data conversions, as well as duplicate analysis and elimination.
• Data mining and analytical applications, including social networking platform analysis, object and facial recognition, profile matching, OCR and text analytics through smartphone applications.
Moreover, big data and enterprise application solutions play a vital role in the successful operation and maintenance of an enterprise. In addition, the successful implementation of the big data solution requires good team members with the right enterprise applications and tools. Organizations must invest the time and money in developing their own expertise applications for the big data analytics team and computing infrastructure.
Author Bio: Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies, a software services company focusing on technologies like BigData, IoT, and AI services. He is also a contributor to several prominent online publishing platforms such as Medium.com and HubSpot.