Multi-Cloud Data Analytics and Cross-Cloud Capability

What is a multi-cloud strategy? 

Cloud computing itself is now a well-established best practice, and a multi-cloud strategy is becoming more and more common. According to a recent survey by Gartner, 81% of respondents said they are working with two or more public cloud providers.

The term “multi-cloud” refers to a cloud strategy in which a single organization uses multiple cloud computing and storage providers. For example, an organization might use a multi-cloud strategy by employing one cloud vendor for its storage, another for its enterprise software, and yet another for running its data analytics workloads.

Multi-Cloud Data Analytics

Traditionally, data analytics platforms have been tightly coupled, combining both compute and storage capabilities. Multi-cloud data analytics loosens this bond, giving customers greater flexibility, agility, and control over their analytics workloads.

Multi-cloud deployment empowers organizations to control not only their enterprise architecture but also their data. The choices offered by multi-cloud service providers across different vendors opens possibilities in the data analytics space for managing data warehousing, business intelligence, and analytics in new ways. For example, using multi-cloud providers, analytics and data scientists can take advantage of open analytics APIs from Google Analytics Reporting to Microsoft Text Analytics and many others to find (really cool) insights from their data. 

The market offerings continue to evolve, as well. Vendors are offering storage or warehousing as a service. Snowflake is one such vendor offering “data warehouse-as-a-service”. Snowflake is unique in that they are a single platform consisting of storage, compute, and services layers that are integrated logically yet scale infinitely (and independently) from one another. If you are interested in learning more about Snowflake (or Redshift), then check out my blog post comparing AWS Redshift and Snowflake for a detailed comparison. 

Principles of Cross-Cloud Capability

Data analytics involves many different but interlocking parts: data discovery, data warehouses, data integration, data mining, and machine learning. Moving to a multi-cloud setup gives your organization the power to pick and choose the services and providers that work for your needs.

Data analytics is a great fit for multi-cloud strategies. The key is a solid cross-cloud platform or foundation to get it right. Snowflake’s e-book titled Why Your Multi-Cloud Strategy Needs a Cross-Cloud Foundation discusses that major multi-cloud challenges that exist today, such as data silos and portability limitations that make a multi-cloud strategy incomplete. It outlines principles for  building a solid foundation for multi-cloud success – called cross-cloud capability.

In short, Snowflake’s cross-cloud capability principles remove barriers to data so organizations can:

  1.     Analyze all data for decision-making, no matter where the data is located
  2.     Ensure business continuity and disaster recovery through cross-cloud replication
  3.     Perform account migration without data portability concerns

 

Cross-cloud capability provides a way to bridge the multi-cloud divide. Say your organization has multiple Snowflake environments – marketing, finance, operations, etc. might have their own Snowflake instance plus on-prem data sources. A solution such as Neebo will connect those multiple Snowflake environments (and all your other environments) to provide a unified view of all your analytics assets. Neebo’s Unified Virtual Access Layer sits on top of each cloud region and all cloud infrastructure regardless of which cloud platforms are used. In our example, teams discover new insights by using Neebo as a portal to share and model assets that are across different clouds.

Cross-cloud capability includes data sharing. The true benefits of multi-cloud analytics will not materialize until data can be shared across clouds and regions. With cross-cloud capability, organizations securely share data across regions and cloud accounts while all adhering to the same rules of data sharing (data exists locally in a single source where it’s accessed rather than moved). Within Neebo, analysts and data scientists work side-by-side, in real-time, to create the best analytics from your data.

Conclusion

Multi-cloud data analytics is the future of business – uniting the resiliency and flexibility of multi-cloud strategies with the power of data analytics. Whether you’re already using several cloud providers or you’re still on-premises, a clear multi-cloud strategy with Neebo will take you cross-cloud for the next evolution in data analytics. Reach out to one of our experts for a 14-day free trial of Neebo.

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