Big Data Architecture Stack Layers

The security requirements have to be closely aligned to specific business needs.

Big data architecture stack layers.

The processing layer is the arguably the most important layer in the end to end big data technology stack as the actual number crunching happens in this layer. The big data architecture might store structured data in a rdbms and unstructured data in a specialized file system like hadoop distributed file system hdfs or a nosql database. Without integration services big data can t happen. The goal of most big data solutions is to provide insights into the data through analysis and reporting.

What makes big data big is that it relies on picking up lots of data from lots of sources. Big data layers 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. 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. The layers are merely logical.

Some unique challenges arise when big data becomes part of the strategy. By judith hurwitz alan nugent fern halper marcia kaufman security and privacy requirements layer 1 of the big data stack are similar to the requirements for conventional data environments. 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. Therefore open application programming interfaces apis will be core to any big data architecture.

The layers simply provide an approach to organizing components that perform specific functions. In this layer analysts process large. Increasingly storage happens in the cloud or on virtualized local resources. They do not imply that the functions that support each layer are run on separate machines or separate processes.

In addition keep in mind that interfaces exist at every level and between every layer of the stack. The data layer at the bottom of the stack are technologies that store masses of raw data which comes from traditional sources like oltp databases and newer less structured sources like log files sensors web analytics document and media archives. The analytics layer interacts with stored data to extract business intelligence.