HomeData EngineeringData NewsOvercoming Multi-Cloud Networking Challenges

Overcoming Multi-Cloud Networking Challenges

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Within an enterprise, teams are turning to cloud services for varied reasons. As more organizations move to multi-cloud, there’s a growing need to simplify the multi-cloud landscape. Leveraging multiple public clouds at the same time often presents significant networking challenges.

Data’s being used with increasing fluidity. Business units, tech teams, and clients, alike, are tapping into an enormous and rapidly growing universe of data. Within an enterprise, teams are turning to cloud services for varied reasons. As more organizations move to multi-cloud, there’s a growing need to simplify the multi-cloud landscape.

When networked efficiently with centralized data management, multi-cloud can provide an interconnected repository that offers all the benefits of public cloud: easily migrating workloads, reducing risks and costs, and accelerating innovation. Data that’s accessible by all public clouds at the same time, over low-latency connections, allows users to leverage the best cloud tools (including SaaS and PaaS services) to access and analyze data. Yet all too frequently, orchestrating data across clouds is cumbersome. Here’s a look at the main networking challenges and how to overcome them.

Without Multi-Cloud Capability, Networking Complexity Grows

Leveraging multiple public clouds at the same time often presents significant networking challenges. Consider orchestrating a workload scaling data processing event, then bringing that data back to a shared repository. Without a cohesive multi-cloud implementation, common hurdles include:

  • Job management: Without a common storage repository, you have separate database instances across various engines. If you’re working across three different public clouds, you’ll have three job engines, each creating additional challenges.
  • Scratch space & output collation: Limited performance, based on what’s available within that cloud, requires manual collation of data. You’ll have data endpoints or results captured in GCP, Azure, or AWS, which means your team will need to figure out how to bring data together and leverage it as a single data set again.
  • Source data: Storing duplicate copies of data in each cloud leads to higher storage costs and network charges, driving the need for data orchestration.
  • Availability of resources: Resources may be limited because the instances deployed will be specific to that public cloud. In some cases (such as seasonal pushes or GPU-enabled instances), instances are not highly available. This reduces the ability to tap into a particular public cloud when you’re in a single cloud workspace.
  • Cost arbitrage: When generating workloads across multiple public clouds, the goal is to do it in the most cost-effective way. But spot instances can change price frequently. Relying on a single cloud limits your opportunity to save money, taking you hostage to the prices of the day or having to choose to simply turn it off until prices become affordable.
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