Many organizations understand the value of the cloud and big data, and the current trend is to address both cloud and big data use cases as a combined project. These projects may start with a modest investment but can scale very quickly with overwhelming demand from the business and seemingly exponential increase in data volumes. The early stage of all projects requires an appropriate kickstart approach and seed investment, but understanding how to execute these initial pilots while keeping sight on a five-year TCO can be challenging for organizations with limited experience of either cloud or big data technologies.
Successful projects maintain flexibility in deployment patterns, enabling tuning for workloads and data and continued optimization of resource use and costs. Driving projects from user need through a use-case catalogue can assist in managing the data ingest, management, governance, and exploitation of data. The use-case catalogue also assists in managing changing priorities and feeds into budgets and financial plans.
Drawing on his experience with a range of successful production deployments on AWS and Azure, Christopher Royles offers an overview of the foundations of the minimum viable product (MVP), explains how to kickstart your project, and discusses how growth can be planned, managed, and budgeted effectively. You’ll learn how to effectively size an initial MVP as well as pragmatic methods of scaling to a five-year long-term foundation and growth plan. Along the way, Christopher also shares lessons learned and techniques for managing the initial funding and longer term strategic budget and investment.
Christopher Royles is a systems engineer at Cloudera, where he builds out large-scale data lakes on Amazon and Azure and assists customers from their initial MVP to full-scale production. Chris has advised on UK government open data initiatives as part of the Open Data User Group (ODUG) and sat on the quick wins stream of the UK Government Cloud Program (GCloud). He holds a PhD in artificial intelligence from Liverpool University, which he subsequently applied to voice recognition and voice dialogue systems.
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