This post is part 2 in a series on how companies use the hybrid cloud to solve real-world problems. Part 1 covered using hybrid clouds to add data center capacity.
As hybrid clouds become more and more common in enterprise IT settings, a number of different use cases and journeys are beginning to become apparent. In part 1 of this series on how enterprise IT is using the hybrid cloud, we looked at how the hybrid cloud can be a faster and more economical way to add new data center or server capacity—or even an entire new or better data center.
Hybrid Cloud Can Add "Cloud Capabilities"
But the ability to provision computing resources quickly is only one part of the cloud story. The public cloud also provides some unique capabilities that are very attractive for use in many applications, including applications that are not naturally cloud-based applications. Taking advantage of these capabilities in the cloud is typically much easier (or at least less impossible) than building them yourself, saving significant development time and cost, which benefits both enterprise dev and ops teams.
A simple example is Amazon S3, or Simple Storage Service, which provides an inexpensive, efficient, easy-to-use yet secure, resilient, and massively scalable file-storage mechanism for many applications. Imagine, for instance, that your company has a video management app that needs to store large video files and make them accessible to users around the world. Taking advantage of S3 is a popular way to deliver that functionality without having to build it and provide the infrastructure to operate it yourself.
Coping With Huge Quantities of Data
Another example of cloud capabilities is its “edge” capabilities in providing highly scalable data bandwidth. For instance, some mobile applications and Internet of Things (IoT) use cases require huge quantities of data to be imported and stored for later processing. This may be because customers download lots of data from an application (such as video streaming), or because an application must communicate with a large number of agents all over the Internet (such as with IoT applications). If a company needs a bigger data intake or data export pipe than their data center can cost-effectively provide, the public cloud is extremely good at performing this type of “edge” data intake/export at almost any scale.
Then there’s the need for unique data processing, such as video processing. If you are adding new capabilities to your applications that deal with giant data sets, you may be able to find already optimized solutions available in the cloud. The cloud-based versions can give you a rapid way to add these capabilities to your application or company, usually without a massive upfront investment.
The cloud also offers managed capabilities that can help reduce your operational support burden. Dealing with databases, managing services, and creating application environments are use cases perfectly tailored to cloud-based services such as Amazon’s RDS and Elastic Beanstalk. Especially for internal-use applications, experimental applications, or application-testing environments, the ability to leverage the cloud to handle much of the operational support burden is highly valuable.
Finally, the cloud offers a highly scalable ability to handle extremely large data sets, making it easier to build data warehouses, perform map-reduce operations, and perform other data analyses useful in providing business analytics and other high volume data-processing operations.
Monitoring Challenges When Adding Cloud-Based Capabilities
Edge tier data-bandwidth connections and high-volume data processing capabilities are likely central to your application, so you need a monitoring solution that monitors them as easily as it does the rest of your infrastructure—including the on-premise components.
More generally, if you think about the cloud capabilities that make moving these components to the cloud useful in the first place, it’s typically the cloud’s ability to handle large scaling requirements and/or huge data management. But don’t forget that large scaling and big data applications also generate large quantities of analytics and monitoring data. Your monitoring toolkit must be able to handle this cloud-scale volume of data, and that typically means a cloud-based monitoring solution.
Read the first post in this series: Using Hybrid Clouds: Adding Data Center Capacity.