Why A Big Data Stack Must Include An Infrastructure Layer

It is very thrilling to see the cloud industry begin to unite around the big data stack, a complex idea on which to build other reference architectures for more scalable and secure big data systems. Current dialog on what comprises “the stack” will increase the probability of expanding repeatable processes that can result in big data successes and that is an outcome that we all should strive for.

Like many other views, we are convinced that the big data stack’s time has finally arrived and you should be very excited about your role in distributing data applications in the context of a company’s overall information structure. Nonetheless, our vision of the best practices stack will begin very differently, beginning with:

The Infrastructure Layer – Explained

The Infrastructure Layer – Companies will require enterprise grade computing, scalability, storage and networking as they move from the proof-of-concept to the production of big data apps. This changeover may be tough and some companies will struggle, because for successful big data solutions, they will need to scale quickly and place heavy requests on their infrastructure for a time.

The Data Layer – This layer is where companies will manage their information architecture and data assets. New big data solutions must be able to cohabitate with any current systems, so companies will be able to leverage their current investments and incorporate disparate internal/external data sources. Processes, people and technology for data governance will also reside in this data layer, thus ensuring timely and accurate data so companies can extract maximum value.

The Insight Layer – Business intelligence applications are thoroughly ingrained in a lot of companies and data specialists have a clear affinity with their specific tools. This new big data solution has to cohabitate with the existing tools, along with the newer analytics applications, to the full value from the data.

The Security Layer – This security layer spans all three layers, guaranteeing protection of essential corporate data, plus management, monitoring, orchestration and provisioning to allow rapid scaling on an ongoing basis.

Infrastructure Layer Objectives

One basic key business objective with big data is to withstand higher data volumes and the new data sources, which imply the flexibility to rapidly add use cases. If this big data is not aggressively managed, the data sources and new use cases can expose the challenges to the data capacity and space constraints, and the lack of in-house tech skills.

Grasping the stack will require businesses to start with a very strong foundation, and determine whether they have the infrastructure layer to make all of this a reality. We believe that a highly secure and managed service model is the best way to deliver the infrastructure and analytics applications. This will allow businesses to quickly scale and ensure maximum focus on the application and analysis that accelerates delivery of data driven strategies.

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