Big Data Architecture Stack Layers In Order

Part 2 of this big data architecture and patterns series describes a dimensions based approach for assessing the viability of a big data solution.

Big data architecture stack layers in order.

The data warehouse layer 4 of the big data stack and its companion the data mart have long been the primary techniques that organizations use to optimize data to help decision makers. New big data solutions will have to cohabitate with any existing data discovery tools along with the newer analytics applications to the full value from data. Some unique challenges arise when big data becomes part of the strategy. Security and privacy requirements layer 1 of the big data stack are similar to the requirements for conventional data environments.

As you see in the preceding diagram big data architecture or unified architecture is comprised of several layers and provides a way to organize various components representing unique functions to. Typically data warehouses and marts contain normalized data gathered from a variety of sources and assembled to facilitate analysis of the business. Towards a generalized big data technology stack. Security layer this will span all three layers and ensures protection of key corporate data as well as to monitor manage and orchestrate quick scaling on an ongoing basis.

This is the stack. How do organizations today build an infrastructure to support storing ingesting processing and analyzing huge quantities of data. We propose a broader view on big data architecture not centered around a specific technology.