4 Big Data Infrastructure Pain Points and How to Solve Them
Making the most of big data requires not just having the right big data analytics tools and processes in place, but also optimizing your big data infrastructure. How can you do that? Read on for tips about common problems that arise in data infrastructure, and how to solve them.
What is big data infrastructure?
Big data infrastructure is what it sounds like: The IT infrastructure that hosts your “big data.” (Keep in mind that what constitutes big data depends on a lot of factors; the data need not be enormous in size to qualify as “big.”)
More specifically, big data infrastructure entails the tools and agents that collect data, the software systems and physical storage media that store it, the network that transfers it, the application environments that host the analytics tools that analyze it and the backup or archive infrastructure that backs it up after analysis is complete.
Lots of things can go wrong with these various components. Below are the most common problems you may experience that delay or prevent you from transforming big data into value.
Slow storage media
Disk I/O bottlenecks are one common source of delays in data processing. Fortunately, there are some tricks that you can use to minimize their impact.
One solution is to upgrade your data infrastructure solid-state disks (SSDs), which typically run faster. Alternatively, you could use in-memory data processing, which is much faster than relying on conventional storage.
SSDs and in-memory storage are more costly, of course, especially when you use them at scale. But that does not mean you can’t take advantage of them strategically in a cost-effective way: Consider deploying SSDs or in-memory data processing for workloads that require the highest speed, but sticking with conventional storage where the benefits of faster I/O won’t outweigh the costs.
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Lack of scalability
If your data infrastructure can’t increase in size as your data needs grow, it will undercut your ability to turn data into value.
At the same time, of course, you don’t want to maintain substantially more big data infrastructure than you need today just so that it’s there for the future. Otherwise, you will be paying for infrastructure you’re not currently using, which is not a good use of money.
One way to help address this challenge is to deploy big data workloads in the cloud, where you can increase the size of your infrastructure virtually instantaneously when you need it, without paying for it when you don’t. If you prefer not to shift all of your big data workloads to the cloud, you might also consider keeping most workloads on-premise, but having a cloud infrastructure set up and ready to handle “spillover” workloads when they arise—at least until you can create a new on-premise infrastructure to handle them permanently.
Slow network connectivity
If your data is large in size, transferring it across the network can take time—especially if network transfers require using the public internet, where bandwidth tends to be much more limited than it is on internal company networks.
Paying for more bandwidth is one way to mitigate this problem, but that will only get you so far (and it will cost you). A better approach is to architect your big data infrastructure in a way that minimizes the amount of data transfer that needs to occur over the network. You could do this by, for example, using cloud-based analytics tools to analyze data that is collected in the cloud, rather than downloading that data to an on-premise location first. (The same logic applies in reverse: If your data is born or collected on-premise, analyze it there.)
Sub-optimal data transformation
Getting data from the format in which it is born into the format that you need to analyze it or share it with others can be very tricky. Most applications structure data in ways that work best for them, with little consideration of how well those structures work for other applications or contexts.
This is why data transformation is so important. Data transformation allows you to convert data from one format to another.
When done incorrectly—which means manually and in ways that do not control for data quality—data transformation can quickly cause more trouble than it is worth. But when you automate data transformation and ensure the quality of the resulting data, you maximize your data infrastructure’s ability to meet your big data needs, no matter how your infrastructure is constructed.
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