Foundational data architecture patterns - Making Sense of NoSQL

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nonprogrammers to quickly create reports that slice and dice sales into the categories

they need. We then took a short look at how specific non- RDBMS database systems like

directory services and DNS are used for high availability. Lastly, we showed how distrib-

uted document revisioning systems have developed rapid ways to compare document

trees and how these same techniques can be used in distributed NoSQL systems.

There are several take-away points from this chapter. First, RDBMS s continue to be

the appropriate solution for many business problems, and organizations will continue

to use them for the foreseeable future. Second, RDBMS s are continuing to evolve and

are making it possible to relax ACID requirements and manage document-oriented

structures. For example, IBM , Microsoft, and Oracle now support XML column types

and limited forms of XQuery.

Reflecting on how RDBMS s were impacted by the needs of ERP systems, we should

remember that even if NoSQL systems have cool new features, organizations must

include integration costs when calculating their total cost of ownership.

One of the primary lessons of this chapter is how critical cross-vendor and cross-

product query languages are in the creation of software platforms. NoSQL systems will

almost certainly stay in small niche areas until universal query standards are adopted.

The fact that object-oriented databases still have no common query language despite

being around for 15 years is a clear example of the role of standards. Only after appli-

cation portability is achieved will software vendors consider large-scale migration away

from SQL to NoSQL systems.

The data architecture patterns reviewed in this chapter provide the foundation for

our next chapter, where we'll look at a new set of patterns called NoSQL patterns.

We'll see how NoSQL patterns fit into new and existing infrastructures to assist organi-

zations in solving business problems in different ways.

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