Global demand for new data centers is continuously increasing due to increasing number of scientific and web applications. These applications demands huge number of computing resources for their continuous execution and availability of services to end users. To fulfill such increasing resource demand, Google offers cluster datacenters which are managed by Borg system. This Borg system has three key advantages: First, it avoids the direct interaction of cloud users with resource management and failure handling, and thus cloud users can focus only on application development; second, it ensure the reliability and availability of resources, and third, it enables the efficient execution of workloads across a large number of servers. Therefore, Borg provides smooth functioning and server uses at data centers, but still their is a scope for improvement in some of performance factors like CPU Utilization, memory utilization, energy consumption, etc. In order to deliver guaranteed services and ensure the availability of resources, cloud resources are over-provisioned, that leads to under-utilization of resources and needless energy consumption. To focus on such issues, in this paper, we present comprehensive overview about Borg system, priority distribution to applications, and features of Google cluster data traces. We analyze the traces using Google Colab to find the patterns of resource uses at data centers and provide a brief overview about the characteristics of power data traces 2019 which can help researchers and academicians for data analysis. Results are analyzed for three important parameters such as CPU utilization, memory utilization, and power utilization.