S2 E1: Analyst Sanjeev Mohan talks about optimizing data cost and performance with BigQuery
15Sanjeev Mohan is one of the most widely respected figures in the field of data and analytics. A former Gartner Research Vice President of Big Data and Advanced Analytics and current Principal of SanjMo, his data trend advisory, Sanjeev has spent years advising customers on their cloud migration journeys.
Sanjeev works with customers to fix their most pressing problems. And today, the issue that’s consistently top-of-mind is: how do I save costs while driving innovation in the cloud? In his experience, customers are tired of fixed-fee pricing structures requiring them to pay for features they don’t use. “The way I think about it is, customers are asking ‘why would I pay for an all-you-can-eat buffet when all I want is a single entrée?’” says Sanjeev. It’s expensive and doesn’t necessarily translate to more efficient workloads.
As a direct result of this customer feedback, Google Cloud has introduced new features like autoscaling and compressed storage to help combat runaway costs while building and innovating in the cloud. According to Sanjeev, autoscaling capabilities from Google BigQuery don’t follow the oft-used “t-shirt size” pricing models, giving customers the benefit of finer-grained scaling options. Rather than buying into small, medium, or large tiers, customers can incrementally scale up based on actual need and they can do it automatically by setting pre-determined thresholds. Plus, customers only pay for features they actually use. “You end up saving money on costs and resources that will eventually be provisioned as your needs increase,” he says.
To hear more about the benefits of autoscaling and best practices from Sanjeev, watch the full episode of Google Data Journeys with Bruno Aziza.
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