Overcoming Multi-Cloud Challenges

As more and more organizations move their workloads into the public cloud, many are increasingly working with multiple cloud providers. According to Flexera’s State of the Cloud Report, 89% of enterprises have a multi-cloud strategy. Multi-cloud approach can be both planned and unplanned. Some firms may have regulatory requirements leading them to multi-cloud, while others may be seeking a best-of-breed cloud services that best suited for their workload requirements. Others seek best innovations clouds, such as support for containers and Kubernetes, or best AI and machine learning (AI/ML) toolkits or a particular data warehouse service offering.

Although there are some technical challenges to multi-cloud deployments, enterprises often find ways to overcome them. However, the functional challenges and skillset gap can be truly daunting. Managing data across multiple clouds presents difficulties that businesses need to consider. Here are some of the main ones:

1. 1. Data silos across clouds
   2. High data egress fees
   3. Data lock-in

Preventing Data Silos

When having your data in the public cloud, one of the more common problems organizations face is fragmented data that makes wrangling data into a single source of truth challenging. A data silo could be  a collection of data that one business unit or department has access to, but others do not. Silos may also surface as a result of binding data to apps and limiting access to a set of users.

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Digging Deeper into Data Silos

Although data silos aren’t inherently a technology only problem, there are some technical solutions that can reduce or fix the problem. Having strong functional teams that control and manage data governance can help you avoid, or at least identify and diagnose, data silos. Having a centralized data location that acts as the single source of truth delivers faster time to value from your data. Faction’s Multi-cloud Data Services can help you eliminate data silos by offering you a single point of data storage that is accessible from any public cloud.

Many organizations have adopted a data lake model, especially given the low costs of cloud storage, and available tools. A data lake is a highly scalable storage repository that ingests raw data, regardless of its format. The data lake approach is an efficient way for companies to capture large amounts of raw data, such as log or usage information, especially when the data does not need to be processed or analyzed immediately.

Network Egress Fees

Once you have your multi-cloud networking in place, your job is still not complete. A widely used cloud pricing model is one that treats data flowing into the cloud as a free service, whereas data leaving the cloud is metered and has a cost per gigabyte associated with it. These data egress fees, as the charges called, are one of most common “surprise” costs associated with public cloud deployments. According to the Faction blog, “Cloud Egress Charges: How to Prevent These Creeping Costs,” some famous examples of steep egress costs include Adobe racking up an $80,000 per day bill on Microsoft Azure, and Capital One having a sudden 73% increase in their invoice from Amazon Web Services.

When you use multi-cloud deployments, you’ll inherently have data egress and egress fees. As you move more and more data across clouds, those costs will also increase. Organizations seek to reduce these costs through several techniques, including data deduplication or compression, which can increase the complexity of data processing.

A more effective way to reduce these costs is to store a single copy of your data in a central location, where it is accessible by all your cloud workloads. This strategy can reduce your costs by a factor of 3 to 4.

Avoiding Data Lock-In

One of the main reasons organizations move to multi-cloud architectures is to take advantage of best-of-breed services from each hyperscaler, including Amazon AWS, Microsoft Azure, Google Cloud Platform and Oracle Cloud Infrastructure. Whether it be a data warehouse offering, such as Redshift or Azure Synapse Analytics, or machine learning tools such as Google Cloud’s AutoML, each cloud has its own strengths. This strategy can lead to data getting locked into specific services in each cloud. As the size of the stored data grows, it becomes more challenging to move. The inability to freely move data between clouds is better known as data vendor lock-in. The threat of lock-in has the potential to worsen as a result of data gravity, which describes the increased dependance of data by growing number of apps that seek to manipulate the growing data. While the best-of-breed clouds and apps strategy makes sense, it inherently silos data within a particular cloud’s service, making company-wide reporting challenging. It also results in incurring high egress costs without ensuring that the organization has a single source of truth. These approached and design pattern can exacerbate the problems of data silos.

Multi-cloud deployments introduce many complexity into your enterprise’s environments. Without careful planning you’ll face major infrastructure and data challenges. Whether you’re figuring out how to cross-connect networks across multi-cloud services, or trying to work with multiple data sources, having a Multi-cloud Data Services solution that offers a single copy of your data simultaneously accessible to all clouds breaks data silos and democratizes data. Having a single data copy that works with all your applications and available to all authorized users—with minimal infrastructure configuration—greatly simplifies infrastructure and reduces operating overhead. It can even make it easier to give customers access to data as needed.

Faction Multi-cloud Data Services can reduce the complexity of your  cloud deployments and help you realize more value by using the best of cloud services from AWS, Azure, Google Cloud or Oracle OCI which allow you to get better and faster value from your data. Having this broad range of multi-cloud tools can boost innovation in any cloud and grow your business faster. It will also help you deliver superior customer experiences with personalized services and responsive apps. View a short video to learn more and book a demo today.

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About the author: Joseph D’Antoni is a Principal Consultant at Denny Cherry and Associates Consulting. He is recognized as a VMWare vExpert and a Microsoft Data Platform MVP, and has over 20 years of experience working in both Fortune 500 and smaller firms. He has worked extensively on database platforms and cloud technologies and has specific expertise in performance tuning, infrastructure, and disaster recovery.