Multi cloud data analytics
A particularly successful data strategy for businesses pursuing data analytics is multi-cloud. While 94 percent of businesses today use cloud computing, just 84 percent have multi-cloud data strategies in place.
Running applications over different clouds is known as multi-cloud. In a multi-cloud deployment, the customer receives compute, cloud storage, and network resources from many cloud providers including AWS, Google Cloud, and Microsoft Azure. Using this method, enterprises can pick and choose which cloud service provider (CSP) services to use, ensuring that they never rely solely on one CSP.
What is Multi Cloud Data Analytics?
A cloud strategy known as "multi-cloud" involves a single firm utilising a provided by the cloud compute and/or storage providers. A firm might, for instance, utilise a multi-cloud approach by using one cloud provider for storage, another for enterprise applications, and yet another for managing workloads related to data analytics.
Multi-cloud refers especially to a scenario in which a company works with many cloud service providers. In contrast, "hybrid cloud" refers to a situation when an organisation uses more than one form of cloud deployment (i.e. public cloud and private cloud).
Benefits of Multi Cloud Data Analytics
The two main advantages of employing several cloud platforms, which underpin all of the benefits of this deployment approach, are no lock-in and increased choice. Others consist of:
1- Analytics everywhere
As a business switches to a multi-cloud environment, they can choose the optimal cloud and toolkit for each analytics workload. A firm can optimise cost and performance by matching cloud storage and computing solutions to its unique data sources and operational needs. Analytics are no longer restricted to a single CSP, and any service becomes a viable alternative for any application.
2- Speeded-up innovation
Businesses are free to look around for the newest cloud computing solutions that will speed up analytics. If Cloud Supplier B has already developed an innovative solution that meets their demands, they don't have to wait for Cloud Vendor A. The increased selection made possible by multi-cloud also makes it easier to build cloud environments for certain use cases, including improved management of health data.
3- Enhanced redundancy and resilience
Businesses are highly vulnerable to an outage when they rely solely on a one-cloud stack, which is known as the "all eggs in one basket" issue. Yet multi-cloud aids in their diversification. Data can be failed-over and migrated between clouds by using a cloud for backup that is different from the one utilised for production workloads. Or they might enhance their on-premises setup with a cloud DR service. Performance is more resilient thanks to multi-cloud.
4- Cost reduction
Becoming multi-cloud may result in lower cloud expenses since businesses can shift between services in response to price fluctuations. Also, they can look for the computing and storage setup that will enable the most economical workload scaling.
How Multi Cloud Analytics Works?
Looker and BigQuery Omni are two essential components of Google Cloud Platform for multi-cloud analytics. BigQuery Omni, which has been announced as widely accessible in October 2021, will receive a lot of attention on this site. No matter where the data is located, you can analyse cloud providers and break down silos with Omni thanks to its secure, scalable, and cloud platform data warehouse. Google Cloud BigQuery Omni has three key components, including:
● A Single Window of Visibility: Access data via dashboards from many cloud providers and a straightforward SQL query.
● Cross-Cloud Analytics: Access integrated ML capabilities for powerful multi-cloud analytics while transferring data across clouds. For more information about creating ML pipelines with GCP, view our on-demand webinar.
● Infrastructure that is fully managed: Data management is quick and simple thanks to analytics across the Big Three Public Cloud with no underlying technical setup.
In order to realise its goal of developing an order to capture the full data platform for analytics, Google Cloud offers additional services like:
● Big Lake Tables: Provides platform-agnostic datasets with comprehensive security and control.
● Cross-Cloud Transfer: With Cross-Cloud Transfer, copies-related problems are eliminated. The data can now be returned to GCP.
● A soon-to-be-released service called Cross-Cloud Materialised Views can analyse data and provide novel insights from a variety of datasets.
With the BigQuery Omni dashboard, researchers may filter information coming from various sources and cloud - based services for aggregate analysis while having files from AWS/Azure perform queries using the well-known BigQuery user interface.
Use Cases of Multi Cloud Analytics
Frequently, "multi-cloud" only refers to many public cloud services. According to some definitions, hybrid clouds include both public and private cloud resources, with private clouds being described as cloud environments exclusive to a single customer.
A multi-cloud environment's cybersecurity measures depend on both the users of the customer's users and the cloud providers themselves. Due to the fact that data now resides in more locations, multi-cloud strategies can make security more complex. However, major CSPs are at the forefront of cybersecurity and have put in place multi-layered safety for their services.
Software used for Multi Cloud Analytics
A linked cloud data system that enables solid results, bidirectional scaling, and a primary source of truth works well in a multi-cloud context. Any demand, from simple data warehousing to sophisticated analytics, can be met by such a solution, which connects to and synchronises everything.
More significantly, it makes analytics future-proof. Businesses benefit from maintaining a variety of cloud service alternatives while relying on their data platform to function flawlessly with any CSP or solution. A proper inter solution will be capable of receiving information from any source and give consistent, affordable, and scalable performance since avoiding information silos and data drifting is a core inter difficulty.
It may become clear during this process that a hybrid cloud, which consists of a single cloud computing service plus an on-premises environment, is preferable to a multi-cloud approach. Alternatively, a hybrid multi-cloud configuration might be the best option.
Conclusion
The future of business is multi-cloud data analytics, which combines the adaptability and resilience of multi-cloud solutions with the strength of data analytics. Whether you're still on-premises or already use a number of cloud providers, the multi-cloud strategy is the next step in data analytics, enabling you to gain more insightful business information more quickly and affordably.
