As data-centric security gains mainstream recognition for its ability to protect sensitive data from theft and malicious use, enterprises are faced with choosing a solution from an increasing array of options. However, not all solutions that claim to be data-centric are designed for the demands of a big data analytics environment, one where scalability, performance, and availability are crucial to support the throughput requirement of today's and tomorrow's analytical workloads. Here's a handy checklist to help you identify whether a data security solution meets the unique requirements of big data:
1. Both native and API-based Integration
Let me put it this way, I could talk for hours about my favorite topic: the fact that even the best security solution on the planet is worth nothing when it doesn't integrate with your existing infrastructure and isn't sustainable. In short, you want to look for a solution that offers flexible integration with big data analytics tools and platforms so you can minimize implementation efforts and avoid the added time and cost of changing existing applications.
2. Linear scalability
As big data environments increasingly include streaming analytics, you need a security solution that can keep up the pace. To get the most out of your data, whether it's real-time or historical, your data-centric security solution should easily scale with your workloads without any impact on performance.
3. High performance
More business intelligence solutions are now using artificial intelligence capabilities and taking advantage of in-memory data processing. To keep up with the speed and performance requirements of these systems, look for a data-centric security solution that delivers high performance with features such as intelligent streaming and load distribution.
4. High availability
Security shouldn't impact the availability of your big data environment. Choose a solution with built-in fault tolerance so that any unexpected failures are resolved automatically without interruption of service. Which brings us to...
5. Flexibility
Big data environments are evolving, making it essential to choose a high-performance solution that is not only data-centric and goes everywhere the data goes, but one that can easily adapt to new technology and infrastructure as they emerge. As mainstream big data technologies such as Hadoop and Spark become legacy systems and new solution and infrastructure emerge, your data-centric security solution must be able to adapt easily to these changes.
6. Support for multicloud and hybrid environments
You need a solution that supports your current and future big data analytics environment, whether that's on-premises, in the cloud, or a hybrid of both.
