Table of Contents
  1. What is Multi-Cloud?
  2. 4 Benefits of Multi-Cloud in Data Analytics
  3. Multi-Cloud Data Analytics
  4. Conclusion

What is multi-cloud data analytics and why are so many companies getting on board?

Cloud computing itself is now a well-established best practice, but a multi-cloud strategy is nearly as common these days. While 94 percent of organizations are now using cloud computing, 84 percent are using a multi-cloud data strategy.

Multi-cloud is an especially fruitful data strategy for companies pursuing data analytics. In this article, we’ll discuss everything you need to know about multi-cloud data analytics.

What is Multi-Cloud?

The term “multi-cloud” refers to a cloud strategy in which a single organization uses multiple cloud computing and/or 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 specifically indicates a situation in which an organization uses more than one cloud vendor. This is in contrast to the term “hybrid cloud,” which indicates a situation in which an organization uses more than one cloud deployment type (i.e. public cloud and private cloud).

4 Benefits of Multi-Cloud in Data Analytics

According to RightScale’s 2019 “State of the Cloud” survey, organizations now use an average of 4.9 different clouds. With so many companies jumping on the multi-cloud bandwagon, it’s not hard to imagine that there are many advantages of multi-cloud.

Below, we’ll discuss 4 major multi-cloud benefits.

1. Geography

Sometimes, organizations pursue a multi-cloud strategy for purely or partially geographical reasons. If latency or internet speeds are a critical concern, it can be useful to work with multiple cloud service providers with servers in different locations.

Multi-cloud strategies may also be necessary for geographical reasons if your organization is subject to data sovereignty laws—i.e. regulations that require you to store cloud data within the same territory or country where it was collected.

2. Resiliency

The best cloud providers will have service level agreements (SLAs) that specify the level of uptime that customers can expect. However, even a guarantee of 99.99 percent uptime corresponds to downtime of nearly 1 hour over the course of a year—and if that hour comes at an extremely inconvenient time for you, you’ll have little recourse.

A multi-cloud strategy is more resilient and protects you from the downtime and outages of any single cloud provider or an in-house solution. You can use one vendor as your primary cloud, while maintaining a backup for your cloud data and software, just in case. Having this redundancy will all but guarantee constant access to your business intelligence and prevent disruptions to your decision-making processes.

3. Cost savings

The cloud is generally seen as less expensive than on-premises data centers, but there are plenty of ways to further reduce your expenses once you’re in the cloud.

Using a single vendor for all your needs—servers, cloud storage, software, networking, and more—is convenient but also potentially expensive. What’s more, cloud vendors might take advantage of this situation by raising prices in the event of vendor lock-in (see the next section).

Multi-cloud lets you shop around to find the best value for each of your cloud services. It also gives you an easier exit strategy if a particular vendor becomes too costly or if you need more resources from a scalability standpoint.

4. Risk management

One of the biggest risks of cloud computing is vendor lock-in, when companies feel compelled to maintain their relationship with a cloud provider due to the high costs of leaving. For example, a provider might charge extortionate fees to migrate your data to another cloud platform, effectively “locking you in” as the amount of data you store continues to increase.

Another nightmare scenario for companies in the cloud: what happens if your cloud provider suffers a disaster or goes out of business? In 2013, for example, customers were left scrambling when the cloud provider Nirvanix unexpectedly shut down, with only two weeks to save their data.

By reducing your dependence on any single provider, you eliminate the competitive advantage of all providers, which is a highly effective risk management strategy.

Multi-Cloud Analytics

No matter what multi-cloud advantages are most important to you, it’s undeniable that data analysis is a perfect fit for multi-cloud strategies.

For one, pursuing a multi-cloud strategy is easier than ever before. Observing this growing trend, public cloud providers such as Google Cloud Platform and Microsoft Azure have sought to facilitate multi-cloud strategies for their customers.

In April 2019, for example, Google introduced Anthos, a new platform for running and managing applications in whatever environment is most convenient to you—whether that’s on-premises or in the public cloud. Not to be outdone, Microsoft then introduced Azure Arc, a hybrid and multi-cloud technology suite that enables customers to run services outside of Azure.

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

Data analytics involves many different yet interlocking concerns: data discovery, data warehouses, data integration, data mining, and machine learning, and more. Moving to a multi-cloud setup gives your organization the power to pick and choose the analytics tools that work best for your company's data needs.

For example, there are a variety of data warehouse solutions: big players like Amazon Redshift, Microsoft Azure Synapse Analytics, and Google BigQuery, as well as smaller competitors such as Snowflake and Cloudera. With multi-cloud analytics solutions, you can select the data warehouse solution that’s best for your purposes, and then complement it with a real-time processing solution such as AWS Kinesis or Azure Event Hubs.

Of course, despite its advantages, planning and implementing a multi-cloud data analytics strategy won’t be a walk in the park. With careful strategic forethought, the right software tools, and expertise for the job, your multi-cloud data analytics project has a much greater chance of success.

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, the multi-cloud strategy represents the next evolution in data analytics, helping you get more efficient and cost-effective business insights.

If you’re interested in pursuing a multi-cloud data analytics strategy, Integrate.io stands ready to assist you. Integrate.io makes it easy to build pipelines between all of your enterprise data sources, and then integrate them within your centralized data warehouse.

Want to learn more about using Integrate.io for multi-cloud data analytics? Get in touch with our support team today to discuss whether Integrate.io fits your needs and try us out for 14-days.