Data modeling, and therefore NoSQL data modeling, is evolving with the rise of NoSQL databases. Last year, Forrester stated that “NoSQL is not an option — it has become a necessity to support next-generation applications.”
And industry has begun adoption, with queried for Forrester’s 2016 survey having already implemented or beginning to implementNoSQL technology.
But to determine NoSQL, and NoSQL data modeling’s growing reach, we must first consider its predecessor.
SQL (Structured Query Language) is a traditional programming language used to manage data in a relational database. It has been widely adopted because it helps to maintain the referential integrity, constraints, normalization and structured access for data across disparate systems.
These factors are important for data modelers who work to define and analyze data requirements needed to support business processes within the scope of corresponding information systems within organizations.
However, the business landscape is changing. Organizations are generating and have access to astonishing amounts of data that must be managed, including harnessing it, storing it, analyzing it and getting it to the appropriate stakeholders so strategic decisions can be made.
Of course, these processes require inserting and/or querying data from underlying databases. And because of today’s competitive and overwhelmingly digital business environment, the agility of these processes is key, or opportunities will be missed and costs will go up.
Despite its prominence, the inflexible SQL database does not lend itself to agility. Therefore, the more malleable NoSQL database is gaining traction. This approach gives organizations the ability to store enormous amounts of unstructured data from disparate sources, scaling to petabytes of information from across the world.
NoSQL provides high performance with high availability. As I’ve already mentioned, it scales easily and offers rich query language in addition to flexible storage, although not stored in tables with keys and relations. But NoSQL and NoSQL data modeling is radically different, even counterintuitive, and not what traditional database administrators and data modelers are used to.
But the discipline of data modeling always starts with the fundamental question: what do I want to achieve with this information? Then you determine the queries you need to run and how frequently you’ll change, create or delete data.
In the NoSQL world, there are no data normalization or storage rules. In fact, denormalization and duplications often are encouraged to make queries faster, which makes business operations more efficient.
In addition, joins are rarely supported and are instead the responsibility of the application. Therefore, you should think this through before applying your data because application joins are expensive and will impact query times.
Data modeling is especially helpful here, and you need to know how you’re going to use your data and its characteristics so you can reference them at run time.
Because organizations are beginning to transition to NoSQL databases due to their many benefits, NoSQL data modeling had to be addressed. That’s why erwin has developed an extension of erwin Data Modeler called erwin DM NoSQL.
We want to help our customers use the most current database technologies while maintaining the integrity, quality and governance of their underlying business-critical information.
Here’s how data modeling works with erwin DM NoSQL:
Experience how easy NoSQL Data Modeling is for yourself. Click here to take erwin DM NoSQL for a free spin.