When it comes to analytics, Google BigQuery is a a great option. It provides a managed service approach to data analytics and simplifies how customers can manage and run large analyses in the cloud. However, there are many other options available from other vendors and so in this blog, I wanted to highlight four key benefits of Google BigQuery.
- Distributed architecture – Google distributes the computing used by BigQuery across compute resources dynamically which means that you do not have to manage compute clusters. Competing offerings typically require custom sizing (and pricing) of specific compute clusters, and this can change over time which can be challenging.
- Flexible pricing options – Since Google dynamically allocates resources, prices are dynamic too. Google offers both a pay-as-you-go option where you pay for the data imported into BigQuery and then per query costs. As part of this approach, they provide a reporting tool to provide added visibility into usage and cost trends. Fixed pricing is also an option for larger users.
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Fully managed – Because BigQuery is a fully managed service, the backend configuration and tuning is handled by Google. This is much simpler than competing solutions that require you to choose a number and type of clusters to create and to manage over time.
- High Availability – BigQuery automatically replicates data between zones to enable high availability. It also automatically load balances to provide optimal performance and to minimize the impact of any hardware failures. This is different from competing solutions which typically focus on one zone only.
Google BigQuery is unique in that it takes an “Analytics-as-a-Service” approach to the cloud. This provides the benefits of simplified operation and pricing and can be a great option for many customers.
Free ESG Report: 2018 Public Cloud Infrastructure Trends