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NoSQL DB vendors gain recognition -- more adoption next hurdle

Despite finding their way into Gartner Magic Quadrant reports, vendors of NoSQL databases are still in the early-adopter stage with users. Is there a need for NoSQL software in your organization?

NoSQL databases, upstart technologies that offer more design flexibility than SQL-based relational software does, have started being accepted into the mainstream IT fraternity. For example, Gartner Inc. included five NoSQL DB vendors when it plotted the top providers of operational database management systems in a Magic Quadrant report that the consulting and market research company issued in late 2013. One of those vendors also made it into the ranks of leading data warehouse database developers in a similar report published in March 2014.

But NoSQL technology still hasn't found a place in many user organizations. In a survey of IT and business professionals conducted by The Data Warehousing Institute in November 2013, only 11% of the 538 respondents said their organizations were using NoSQL databases in their primary data warehouse architectures. Another 24% said they planned to do so within three years -- but that left 65% saying their organizations had no adoption plans for NoSQL. Even in a survey of people with experience managing big data environments, done by TDWI earlier in 2013, just 32% of the 189 respondents said they had deployed NoSQL systems -- the lowest adoption rate among six types of technology platforms.

In its report on operational databases, Gartner said that more of its clients were starting to use NoSQL products for specific purposes, such as running Web applications requiring high scalability. To help you decide if you have a use for NoSQL technologies, SearchDataManagement and other TechTarget sites have published a variety of content examining what they are and what they're suited for. In one article, we look at the four main categories of NoSQL databases and provide examples of real-world use cases. In another, we explore NoSQL's fit-for-purpose nature. A third delves deeply into how NoSQL software has dented the dominance of relational databases in recent years.

But it isn't always an either-or choice. In a video Q&A, William McKnight, president of McKnight Consulting Group, explains why there's room for combining NoSQL and relational databases in many IT architectures; in another, consultant John Myers of Enterprise Management Associates Inc. discusses the melting pot of data management platforms typically needed to support big data applications. We also look at NoSQL databases from the user point of view -- for example, in a story about MongoDB deployments and a video in which database engineer John Kanagaraj talks about the pros and cons of using NoSQL DB systems in big data environments.

You can find much of the content highlighted here, and more, in our guide to NoSQL database trends and best practices. And if you do decide to take the NoSQL plunge, let me know how the water feels for your organization.

Craig Stedman is executive editor of SearchDataManagement. Email him at and follow us on Twitter: @sDataManagement.

Next Steps

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What's your top piece of advice for organizations that are looking to deploy a NoSQL DBMS?
Here's a top piece of advice with two parts. First, match the type of NoSQL database to your requirements. The most commonly used are key value (e.g. Redis and Riak) and document databases (e.g. MongoDB and CouchDB) but column family data stores (e.g. Cassandra) and graph databases (e.g. Neo4J) fit well in particular use cases. If you need faster retrieval times from databases, storing data in a Redis cache might be a good option. If you need flexibility with attributes and could benefit from embedding structures within your records then document databases are worth a look. The second piece of advice is don’t worry too much about data modeling at first. The queries you will pose to your NoSQL database should drive how you structure your document, key values, graphs, etc. The reason NoSQL data stores were developed is because relational databases were not meeting a critical need. It’s not surprising that we’d approach NoSQL database design differently. Once you have basic structures down you can test and tune. For example you might start by embedding documents within documents but find references to documents give better performance.