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Implementing big data storage at scale is a complex and arduous task that requires an advanced infrastructure. With the rise of public cloud computing, various big data management services can be readily leveraged. As a critical part of Twitter's "Project Partly Cloudy", the cold storage data and analytics systems are being moved to the public cloud. This paper showcases our approach in designing a scalable big data storage and analytics management framework using BigQuery in Google Cloud Platform while ensuring the security, privacy and data protection. The paper also discusses the limitations on the public cloud resources and how they can be effectively overcome when designing a big data storage and analytics solution at scale. Although the paper discusses the framework implementation in Google Cloud Platform, it can easily be applied to all major cloud providers.

In this work, we plan to develop a system to compare virtual machines with container technology. We would devise ways to measure the administrator effort of containers vs. Virtual Machines (VMs). Metrics that will be tested against include human efforts required, ease of migration, resource utilization and ease of use using containers and virtual machines.

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