Data Infrastructure Optimization in a Multi-Cloud Architecture
As multi-cloud architectures grow in popularity, so does the challenge of optimizing data infrastructure that spans a multi-cloud environment. And while there’s no denying that data infrastructure optimization is more challenging on a multi-cloud architecture than it is when you use a single cloud (or no cloud at all), those challenges can be overcome.
Let’s explore how.
What is Multi-Cloud?
Multi-cloud refers to an infrastructure that includes, well, multiple clouds. Those clouds could be two or more public clouds, like AWS or Azure. Or they could include a combination of public and private clouds.
Multi-cloud architectures have grown in popularity in the last couple of years because they can help to increase availability; if one cloud fails, you still have other clouds to host your data and applications. They also optimize costs, because they allow you to select from several equivalent services from different clouds, depending on which cloud offers the service at the best price point for you.
Multi-Cloud Data Infrastructure: Benefits and Challenges
By its nature, a multi-cloud architecture can help to optimize data infrastructure in some ways. This is especially true when it comes to data availability. By spreading your data across more than one cloud, you make it easier to keep that data available even if one cloud fails.
At the same time, however, multi-cloud architectures create new data infrastructure challenges, including:
- Data formatting. An application hosted on one cloud may not accept or store data in the storage formats that you use on another cloud. Or, you might have data that is stored in one format on one cloud (like an EBS snapshot from AWS) that you want to convert to use on a different cloud (like Azure), but lack native, vendor-supplied tools to do the conversion.
- Data migration. Moving data between different clouds is likely to take longer than moving data within the same cloud. The reason why is that the speed at which you can migrate data from one cloud to another is limited by the bandwidth of the public Internet connection between the clouds. Internal cloud data migration is typically much faster because it does not require the public internet.
- Data analytics tools. Most public cloud vendors offer all of the major data analytics tools, like Spark and Hadoop, as a service. You can also run those tools on a private cloud. The problem that arises on a multi-cloud architecture, however, is that one cloud’s implementation of a given analytics tool is rarely the same as another vendor’s implementation. This means that, if you choose to run analytics tools in multiple clouds, you’ll need to reconcile versioning differences, and possibly data formatting inconsistencies as well.
- Knowledge and expertise. A final challenge of multi-cloud storage is that it requires your IT team to master several different cloud environments. Having to manage multiple clouds means that your employees will have to learn the nuances of similar services from different clouds.
Overcoming Multi-Cloud Data Infrastructure Challenges
How can you address the challenges described above? Consider the following best practices for optimizing data infrastructure when your data is spread across multiple clouds:
- Avoid using equivalent services on different clouds at the same time. Instead of storing data in both AWS S3 and Azure Storage at the same time, or using Hadoop on AWS while also running it on-premise, choose one or the other. This approach means that you will lose the data availability advantages of having data in multiple clouds, but it simplifies management. And there are other ways to increase data availability without using multiple clouds; for example, you can host data in multiple regions of the same cloud.
- Store data where it is collected. This strategy helps to avoid the delays that can result from data migrations between clouds. If you collect one type of data in one cloud, run analytics on that data in that cloud, too, instead of transferring it to a different cloud to do analytics.
- Optimize data before you migrate it. By transforming and, if appropriate, compressing data before you transfer it from one cloud to another, you can decrease transit times and help ensure that the data is ready to use as soon as it arrives at its destination.
- Avoid vendor-specific data formats. While data formats like EBS snapshots can come in handy when you only use one cloud, they may pose more trouble than they are worth in a multi-cloud environment.
- Adopt-third party data management tools, rather than relying on those supplied by cloud vendors (or those that work only with a specific cloud). This approach allows your team to master only one type of tool and use it on multiple clouds.
Data infrastructure is inherently complicated when it spans multiple clouds. But it can be managed effectively, in a way that minimizes costs while maximizing availability.
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