Multicloud analytics powers queries in life sciences, agritech and more
In the 2020 Gartner Cloud End-User Buying Behavior survey, nearly 80% of respondents who cited use of public, hybrid or multi-cloud indicated that they worked with more than one cloud provider1.
Multi-cloud has become a reality for most, and in order to outperform their competition, organizations need to empower their people to access and analyze data, regardless of where it is stored. At Google, we are committed to delivering the best multi-cloud analytics solution that breaks down data silos and allows people to run analytics at scale and with ease. We believe this commitment has been called out in the new Gartner 2020 Magic Quadrant for Cloud Database Management Systems, where Google was recognized as a Leader2.
If you, too, need to enable your people to analyze data across Google Cloud, AWS and Azure (coming soon) on a secure and fully managed platform, take a look at BigQuery Omni.
BigQuery natively decouples compute and storage so organizations can grow elastically and run their analytics at scale. With BigQuery Omni, we are extending this decoupled approach to move the compute resources to the data, making it easier for every user to get the insights they need right within the familiar BigQuery interface.
We are thrilled with the incredible demand we have seen since we announced BigQuery Omni earlier this year. Customers have adopted BigQuery Omni to solve their unique business problems and this blog highlights a few use cases we’re seeing. This set of use cases should help guide you on your journey towards adopting a modern, multi-cloud analytics solution. Let’s walk through three of them:
Biomedical data analytics use case: Many life science companies are looking to deliver a consistent analytics experience for their customers and internal stakeholders. Because biomedical data typically resides as large datasets that are distributed across clouds, getting holistic insights from a single pane of glass is difficult. With BigQuery Omni, The Broad Institute of MIT and Harvard is able to analyze biomedical data stored in repositories across major public clouds right from within the familiar BigQuery interface, thus making this data available to enable search and extraction of genomic variants. Previously, running the same kind of analytics required ongoing data extraction and loading processes that created a growing technical burden. With BigQuery Omni, The Broad Institute has been able to reduce egress costs, while improving the quality of their research.
Agritech use case: Data wrangling continues to be a big bottleneck for agriculture technology organizations that are looking to become data-driven. One such organization aims to reduce the amount of time and money spent by their data analysts, scientists, and engineers on data wrangling activities. Their R&D datasets, stored in AWS, describe the key characteristics of their plant breeding pipeline and their plant biotechnology testing operations. All of their critical datasets reside in Google BigQuery. With BigQuery Omni, this customer plans to enable secure, SQL-based access to their data living across both clouds, and help improve data discoverability for richer insights. They will be able to develop agricultural and market-focused analytical models within BigQuery’s single, cohesive interface for their data consumers, irrespective of the cloud platform where the dataset resides.
Log analytics use case: Many organizations are looking for ways to tap into their logs data and unlock hidden insights. One media and entertainment company has their user activity log data in AWS and their user profile information in Google Cloud. Their goal was to better predict media content demand by analyzing user journeys and their content consumption patterns. Because each of their AWS and Google Cloud datasets were updated constantly, they were challenged with aggregating all the information while still maintaining data freshness. With BigQuery Omni, the customer has been able to dynamically combine their log data from AWS and Google Cloud without needing to move or copy entire datasets from one cloud to another, thus reducing the effort of writing custom scripts to query data stored in another cloud.
A similar example that blends well with this use case is the challenge of aggregating billing data across multiple clouds. One public sector company has been testing multiple ways to create a single, convenient view of all their billing data across Google Cloud, AWS and Azure in real time. With BigQuery Omni, they aim to break down their data silos with minimum effort and cost and run their analytics from a single pane of glass.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose
1. Gartner, “2021 Planning Guide for Data Management”, Sanjeev Mohan, Joe Maguire, October 9, 2020.
2. Gartner, “Magic Quadrant for Cloud Database Management Systems”, Donald Feinberg, Merv Adrian, Rick Greenwald, Henry Cook, Adam Ronthal, November 23, 2020