A time series is a series of data points indexed, listed or graphed in time order. Many applications deal with such data: those that feature financial transactions with associated timestamps, inventories that continually change over time, and other processes involving logs and Internet of Things (IoT) sensor events coming in 24 hours seven days a week. Analysis of the Internet of Things or IoT sensor data is expected to grow at a rapid pace, with the number of cellular IoT connections exceeding 3.5 Billion by 2023. With 90% of senior executives focusing on IoT rollouts in 2018, worldwide technology spending on IoT is forecasted to exceed $1.2 Trillion by 2022. In order to go beyond basic monitoring, all organizations must figure out how to analyze the avalanche of IoT sensor and other time series data in a timely manner to gain business benefits from the investment.

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Virtualization has transformed the Network and IT landscape, leading to the pooling of computing, storage, and networking resources. Network and IT infrastructure administrators have the flexibility to share the resources and deploy workloads for multiple applications, services, and business units. Increasingly, data centers are using IoT sensors to monitor the health of each network or IT resource. The time-series data from hundreds of thousands of sensors attached to network & IT resources is analyzed by TigerGraph in real-time to detect when a resource such as a storage array, server, network switch or router shows the signs of wear, requires maintenance or is nearing its peak capacity. Using TigerGraph, the administrators can also figure out which workloads are affected and how to minimize the impact.

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