What are the Common NoSQL Database Mistakes you want to avoid?

The enterprise database scenario is changing day by day. After the SQL era, we can see that now more and more people are adopting NoSQL databases, which is followed by NewSQL DBs lately. Compared with SQL, NoSQL can solve many persisting dataset problems, especially related to the scalability of DBs. However, sometimes this rush to use new cool technologies may also end up in trouble if you have not done thorough baseline research. Notably, the newbies into NoSQL or those who are trying to migrate from SQL to NoSQL without enough knowledge in it tend to make a lot of mistakes.


Here in this article, we will discuss the most common mistakes. DBAs tend to make while adopting NoSQL DB. Knowing these, you will be able to keep a watch on these, identify, and avoid them at the first point of occurrence.


NoSQL errors to keep an eye on

Like many other emerging new technologies, NoSQL also has gone through a hype over the last few years with widespread implementations across the globe with mixed results. However, not all applications went successful. The business owners cannot be blamed for hoping to get a painless solution for their database challenges. But, as now the first-generation NoSQL solutions seem to mature, the time has come to review the mistakes and lessons learned and move forward with more practical and reliable solutions. Without much ado, let us get into the NoSQL errors and how to avoid those.


Error #1: NoSQL is more than just about scalability

When you are thinking about scaling out, the database is not a problem. The actual issue is of understanding how to manage it operationally. When it comes to NoSQL, the most common mistake DBAs and users tend to make is merely equating NoSQL with a web-scale. Now, it has almost become an inside joke at the database community. This is, however, an assumption well understandable. The progenitors of NRDBMS were like Amazon and Google, who focused on addressing massive scalability challenges in the web environment. We can see this even in the naming of the NoSQL like MongoDBcame from the term humongous, which is a classic reference to the volume of traffic and data this database store is meant to address.


However, thinking about NoSQL only from the scalability point of view may lead to poor decisions. Even the smallest organizations with limited database requirements can benefit from adopting NoSQL database solutions if they are dealing with social media or web response data etc. However, on the other hand, a larger organization with a lot of data to handle may still need an SQL solution if the need is to handle more sophisticated queries.


So, it is not just about the volume of data, but the choice of a database is more dependent on the use cases and the functional and non-functional requirements with the data. So, when it comes to database technologies, one should never just get sucked into the next big thing, but put your actual business needs into realities to look for solutions. Those looking for customized database solutions and remote DBA support will explore what can offer you.


Errors #2: Developers following their known practices only

You can see one big example of this mistake, as pointed out by Dan McCreary and Ann Kelly, who authored Making Sense of NoSQL. They had unveiled how a high-profile web project recently failed by the poor selection of the integration team. The client adopted a very robust and powerful NoSQL database named MarkLogic and then brought in an integrator which only had Java programmers with only relational database knowledge. The integrator consulting spent about $30 to 40 million to build the code, which did not have any purpose other than just being thrown away later.


It was not just a single odd example where this had happened. A team with no prior experience developing codes for the nor-relational DBs stepping into it may face the same issues. When the developers tend to use their same old practices with the new technology, it is quite possible to over-develop and bear overheads. In the streamlined document stores of modern-day databases, users need not rely on the UML and Java-based scripts to manipulate those. When the documents may map onto objects directly, then the code can be very lean and focused.


Mistake #3: Distribution makes it hard, even if it is said to be easier with NoSQL

The same principle applies here as it is not about the database volume, but how to operationally manage it. For example, you may adopt Couch DB to run a bunch of customized queries on a single machine. However, once you distribute the DB across multiple nodes, it naturally becomes scattered, and the scope is entirely different. To understand and manage this, you need to be a talented administrator.


Fortunately, some of the top NoSQL databases are, by default, able to help the developers from shooting on their own feet. We may find Couch DB as an example as it uses MapReduce automatically with an assumption which users are querying against different distributed servers. Riak can also be seen as another example of the same. For those who identify Riak as a difficult to manage DB, the fact is that it is so because writing the queries in a distributed environment itself is hard to manage.


So, the key takeaway is to choose an appropriate NoSQL DB, which suits your purpose and enforces the industry’s best practices. Remember that the critical value store remains one of the simplest basic patterns which is scalable. So, there is no need for an enterprise running well on the RDBMS systems to think of migration to NoSQL unless and until they find it most essential to think of it based on the functional requirements of their databases.


Moreover, database technology is continually evolving, and as you can see, NewSQL, which is said to bring forth the best of both SQL and SQL together. So, the key to success for any DBA on the choice and scaling of the database in hand is to under the nature of the business served with their database and the actual current and future challenges it is going to face on the go.


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