Rather than out-and-out replacing their relational counterparts, MongoDB and other NoSQL databases will coexist with traditional RDBMSs. However, as more — and more varied — data swamps companies, the scalability and data-model flexibility of NoSQL will make it the management platform of choice for many of tomorrow’s data-analysis applications.
There’s something comforting in the familiar. When it comes to databases, developers and users are warm and cozy with the standard, nicely structured tables-and-rows relational format. In the not-too-distant past, nearly all of the data an organization needed fit snugly in the decades-old relational model.
Well, things change. What’s changing now is the nature of a business’s data. Much time and effort has been spent converting today’s square-peg unstructured data into the round hole of relational DBMSs. But rather than RDBMSs being modified to support the characteristics of non-textual, non-document data, companies are now finding it more effective to adapt databases designed for unstructured data to accommodate traditional data types.
Two trends are converging to make this transition possible: NoSQL databases such as MongoDB are maturing to add the data-management features businesses require; and the amount and types of data are exploding with the arrival of the Internet of Things (IoT).
Heterogeneous DBs are the wave of the future
As ReadWrite’s Matt Asay reports in a November 28, 2014, article, any DBAs who haven’t yet added a NoSQL database or two to their toolbelt are in danger of falling behind. Asay cites a report by Machine Research that found relational and NoSQL databases are destined to coexist in the data center: the former will continue to be used to process “structured, highly uniform data sets,” while the latter will manage the unstructured data created by “millions and millions of sensors, devices, and gateways.”
Relational databases worked for decades because you could predict the characteristics of the data they held. One of the distinguishing aspects of IoT data is its unpredictability: you can’t be sure where it will come from, or what forms it will take. Managing this data requires a new set of skills, which has led some analysts to caution that a severe shortage of developers trained in NoSQL may impede the industry’s growth.
The expected increase in NoSQL-based development in organizations could be hindered by a shortage of skilled staff. Source: VisionMobile, via ReadWrite
The ability to scale to accommodate data elements measured in the billions is a cornerstone of NoSQL databases, but Asay points out the feature that will drive NoSQL adoption is flexible data modeling. Whatever devices or services are deployed in the future, NoSQL is ready for them.
Document locking one sign of MongoDB’s growing maturity
According to software consultant Andrew C. Oliver — a self-described “unabashed fan of MongoDB” — the highlight of last summer’s MongoDB World conference was the announcement that document-level locking is now supported. Oliver gives his take on the conference happenings in a July 3, 2014, article on InfoWorld.
Oliver compares MongoDB’s document-level locking to row-level locking in an RDBMS, although documents may contain much more data than a row in an RDBMS. Some conference-goers projected that multiple documents may one day be written with ACID consistency, even if done so “locally” to a single shard.
Another indication of MongoDB becoming suitable for a wider range of applications is the release of the SlamData analytics tool that works without having to export data via ETL from MongoDB to an RDBMS or Hadoop. InfoWorld’s Oliver describes SlamData in a December 11, 2014, article.
In contrast to the Pentaho business-intelligence tool that also supports MongoDB, SlamData CEO Jeff Carr states that the company’s product doesn’t require a conversion of document databases to the RDBMS format. SlamData is designed to allow people familiar with SQL to analyze data based on queries of MongoDB document collections via a notebook-like interface.
The SlamData business-intelligence tool for MongoDB uses a notebook metaphor for charting data based on collection queries. Source: InfoWorld
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