Written by Priya GeorgeContent Writer
Multi-cloud is an emerging reality in today's cloud computing landscape. With the onset of advanced data and AI/ML capabilities, it has become essential for companies to become more data-driven. However, a Gartner survey revealed that 81% of the survey respondents used more than one public cloud service provider, thus having the data scattered across clouds.
There are several challenges for multi-cloud data analytics:
Google Cloud Platform is one of the few cloud providers that aims to provide companies with cloud-agnostic, cost-effective, and user-friendly services. For instance, with services like Anthos, application modernization and development can be carried out wherever organization workloads are. Similarly, with BigQuery Omni, Google Cloud aims to bring advanced BigQuery data analytics services and insights to wherever your data resides without you having to transfer or copy it.
Besides deriving accurate insights, businesses also need services to protect data from external threats and compliance risks. Want to learn more about how to protect data privacy? Download and read our whitepaper to learn more.
Google Cloud Platform has two critical products for multi-cloud analytics: Looker and BigQuery Omni. This blog will heavily focus on BigQuery Omni, which was announced as generally available in Oct 2021. With Omni, you can simplify silos and analyze cloud providers with a secure, scalable, and serverless data warehouse no matter where the data resides. There are three essential features of Google Cloud BigQuery Omni, including:
For any new tool in the marketplace, the most crucial question is what are its applications? One of the prominent use cases where this multi-cloud analytics tool comes in handy is when companies need datasets across different platforms. For example, e-commerce or retail companies often need to collate and analyze data from multiple sources, such as ad and in-store data. By accessing the sources where data resides, data engineers can create aggregate models that measure outcomes such as customer lifetime value. Read our blog for an overview of how data analysis and the insights derived go a long way towards driving organizational growth.
There are additional services that help Google Cloud achieve its vision for creating a comprehensive multi-cloud platform for data analytics, such as:
With storage and compute being decoupled in BigQuery Omni, processing occurs wherever data resides without moving it back into BigQuery. As a result, the data flow between the cloud platforms is very smooth as queries can be sent or relayed quickly with the help of the BigQuery control and data plane. Furthermore, with Cross-Cloud Transfer, you can load the data from alternate cloud providers like the AWS S3 buckets and Azure Blob Storage into BigQuery for advanced data analytics and insights.
A significant advantage of the BigQuery Omni dashboard is that analysts can have files from AWS/Azure run queries through a familiar BigQuery user interface and filter data from other sources/cloud platforms for aggregate analysis.
By consulting with our Google Cloud-certified experts, your organization can assess your degree of readiness to develop a data analytics solution across cloud services providers. In addition, we also have the GCP services to implement custom end-to-end solutions that meet your business requirements.
Get in touch with our experts today to learn how you can successfully analyze data and develop multi-cloud analytics insights for your organization. For more information, contact us at [email protected] or visit us at www.royalcyber.com