Around the Storage Block

Hybrid cloud solutions for next generation apps with HPE Data Storage platform

Enterprise IT requirements are ever-changing from monolithic applications built for the client-server age to distributed applications using a mix of on-premises and cloud services built for the mobile, social age. At the forefront of this wave are the next generation infrastructure applications which are used in a variety of use-cases such as social media, content management, Internet of Things, big data and AI pipelines, to name a few.

In this article, I focus on two categories of next generation applications that have gained rapid adoption across various deployment considerations such as bare-metal, virtualized deployments, containerized deployments, or as-a-S deployments in the public cloud, across all industry segments.

A robust portfolio for NoSQL database solutions

Data today comes in various shapes and forms. Each use-case seems to require some tweaks and customizations. That is why HPE’s portfolio has a lot to offer for NoSQL database solutions. NoSQL databases expose data models such as: columnar, graph-based, document stores, key value stores, and time-series databases. These can be used in a variety of use-cases ranging from big data pipelines to social media applications. Just trying to migrate use-cases that use legacy relational databases to a NoSQL database is can be a daunting effort. But – whatever is blocking you from realizing your NOSQL database transformation journey – HPE is here to help!

If you are like many of our HPE Storage customers who have consolidated on relational databases, but like to test waters with some new NoSQL database models for your applications, you can absolutely consolidate all your workloads on to your existing HPE Storage platform for business critical or the mission critical apps. The good news is that you can still maintain similar performance.

If you are considering migrating completely to a NoSQL database solution from your existing relational database, you will have a lot to deal with in your database transformation journey. You probably won’t want to worry about your Infrastructure stack at this point in time.

hybrid-cloud_Splunk_NoSQL_hyperconverged_blog.pngA marriage of compute and storage

HPE Nimble Storage dHCI brings together compute and storage in a hyperconverged platform that preserves all the benefits of HPE Nimble Storage – plus the benefit of consolidating your NoSQL use-cases on an easily scalable hyperconverged stack.

Let’s also talk about how to handle the extreme volume of data that comes in from the NoSQL databases used in big data use-cases. The key is to have a balance between performance and capacity without compromising one or the other. HPE Apollo platforms are the perfect option for this scenario! HPE Apollo 4200s are 2U ultra-dense systems providing the right balance of capacity and performance. What does that means for you? Your Mission Critical NoSQL databases that require high performance with extremely low latency, or your content management systems that need extreme capacity with moderate latency, can all be consolidated on the same Apollo system.

Splunk architectures move front and center

I work with many organizations where Splunk has taken a place front and center for big data analytics, enterprise security and IT operational analytics use-cases. Splunk offers two data architectures for storing the data it collects through its forwarders and indexers on the backend. The first architecture is called Splunk Classic. It is based on the data lifecycle management theory that includes HOT, WARM, COLD and FROZEN tiers, based on parameters such as capacity, data retention and others.

The shortcomings of this particular architecture is that servers and storage are tightly coupled. This causes both availability and scaling issues. Splunk was also aware that there was some room for improvement in the Splunk Classic Model, where the failure of any indexer servers contributed to searches failing – even  though the storage layer was fully available.

The genesis of Splunk SmartStore

This is where Splunk SmartStore plays a leading role.

This second architecture acts to decouple servers from storage. The HOT tier in the SmartStore architecture functions similar to the classic model. However, Instead of a separate WARM and COLD tiers, you have a single S3 object storage layer. This decoupling provides performance and capacity scaling independently. Or – if you have long-term compliance requirements – you can simply add storage to the SmartStore layer.

A beautiful relationship

The beauty of working with HPE is that, no matter what your Splunk architecture looks like, we have the products, solutions and services that can satisfy all your Splunk data needs. HPE’s industry-leading All Flash Storage platforms in HPE Primera and HPE Nimble are the perfect fit for your HOT and WARM tiers.

If you would rather not worry about the infrastructure stack in your classic model, The HPE Nimble Storage dHCI solution is the perfect option for Splunk Classic in a hyperconverged platform. HPE Apollo 4000 systems, combined with Scality RING, are the perfect fit for COLD and FROZEN tiers. However, if you just want the simplicity of Splunk SmartStore, the HPE Apollo 4000 systems, combined with Scality RING S3 Object Storage, is the platform for you!

AI-powered predictive analytics

Another huge benefit is that all of our Storage solutions come with predictive analytics – HPE Infosight – built into the solution. HPE InfoSight offers AI-powered autonomous operations that ensure your environment is always-on, always-fast, and always-agile. And for those of you who are looking for an as-a-Service experience, HPE GreenLake for Splunk provides an on-premises as-a-Service experience at a much lower cost, when compared to the public cloud.

Did you enjoy this blog, or have any questions? I look forward to hearing from you!

Bharath headshot.jpg


Meet HPE Storage Blogger, Bharath Ram Ramanathan. Barath is a Principal Product Manager at HPE, focusing on managing teams and strategizing data solutions around Databases and Big Data applications.

You can connect with Bharath on LinkedIn!  


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