Ever since the database technologies were incepted, the IT industry has been going through a transitional phase.

On that note, SQL vs No SQL is a hotly debated topic where many people hold different opinions. Some experts believe that the legacy system is better, while others are concerned that the shift to No SQL databases could save a lot of time, hassle, processing power, and other resources for the better!

The question is when it comes to SQL vs No SQL, which side do you concur with?

This analogy gains further importance, given that you are still using SQL databases for getting things done. Especially if your company is a mid-sized IT organization with dated processing platforms and database technologies, they need to bite the bullet and port things over to No SQL databases.

For starters, No SQL is relatively new, when compared to its predecessor: SQL DBMS and the new technology claims to speed up large-scale data projects by 4x output levels. According to MarkLogic, “No SQL databases are used not just for handling structured data queries. They can adapt to change faster and scale easier as per the size and requirement quota of the databases in question.”

As it is evident by now, this post answers a couple of hot questions about SQL vs No SQL, and how the latter tends to be better than the former DBMS platform. Read on…

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SQL vs No SQL – What Does the General Consensus Say?

As we said earlier, in the middle of the generational shit in the database and IT industry, people seem reluctant to adopt new solutions.

However, the natural order of things calls for “change” – and the sooner we do it, the better it is. SQL vs No SQL is not an exception by law. One technology is old and dated; the other technology is newer and helps to resolve problems quickly.

But then again, the doubt factor falls into two main categories where we have some valid arguments:

• Doubting that NoSql is not efficient enough to solve problems.
• Doubting that the relational database(s) itself has issues to begin it.

To answer these questions associated with relational database technologies, let’s see what the NoSql database actually is.

What is a Typical NoSql Database Management System?

In short term, we’ll call it NoSql DBMS, just to save time on our part. Frankly, writing down the entire Sql vs. No Sql database management system seems like a tongue twister and rather repetitive.

So, where were we?

Yes, we were about to define NoSql DBMS in layman’s terms.

A NoSql/ No SQL database management system is a group of libraries that can help to manage, monitor, and administer non-relational databases. Likewise, when it comes to non-relational DBMS, we also see a multitude of unstructured data that do not follow a schema orientation.

As a result, the abundance of data falls into the ‘Big Data’ category. Amazon uses YARN, HADOOP, HIVE, and other methods to deal with big data queries. And they’re really good at it!

NoSql database systems are developed to handle multiple operations, different models, and queries scattered over any number of target functionalities.

That being said, NoSql is not the perfect fit for each scenario. Sometimes, despite its shortcomings, SQL works better than No SQL because of the job requirements.

The Column Based Model Dilemma Vs No Sql vs SQL – Which One to Use?

A column database management system orients data in column format.

You can imagine a table in your head with an ample number of rows and columns that associate with one another. Each row in this “imaginary” table is associated with a row key. This is the identification key that helps to locate answers when a query is submitted by end-users.

To make things more interesting, each row can have its subset rows and columns – i.e. if the dataset is complex. In addition to having sub-level rows and columns, you have the option of adding additional columns to any row at any given time. Therefore, maintaining the same columns is not entirely necessary when there is the ability to append new columns later.

When Is No SQL Suitable?

If the dataset is already large and complex – i.e. it has multiple rows and columns in an unstructured format, then you should use it. For instance, large-scale blogs have non-volatile data where the content, at the front end, is always being updated in any shape, size, or form. The No Sql approach uses log aggregation and manages non-volatile data as it piles up.

When Is It Not Okay to Use No SQL In Column Based Model?

If the data is in its early stages – and that too, without any major development is done, then it is better to use a conventional database management system. Regardless of whether the query pattern changes quickly, or how soon the data set is aggregated, No SQL is not the perfect fit.

SQL Vs. No SQL Is a Matter of Preference! Here’s why…

Both DBMS technologies have been used for several years now. As mentioned earlier, it falls down to the requirements that call for a specific DBMS use case.

While it is true that we are in the age of big data where NoSQL is a better option, it also doesn’t mean that it is the alternative solution to RDBMS (*Relational Database Management System).

Traditionally, when Big Data comes into play, RDBMS generates gaps between resources, queries, and dataset management. To that effect, No SQL supports horizontal scaling and a schema-less model. SQL, on the other hand, scales vertically and it is ACID compliant – i.e. Atomicity, Consistency, Isolation & Durability).

Therefore, both systems have their pros and cons.

The Doubters’ Dilemma – There is a Big Issue with Relational Databases:

Falling back to the first two questions where we talked about doubters’ suggesting the integrity of relational databases, let’s see what real-life programming environments call for.

By default, a relational database is not designed to lots of changes. You need to follow a systematic approach to managing and administering RDBMS for scaling, and heterogeneous data workloads.

But, then again, it is also true that some changes are very easy to make.

The plot thickens!

If it is not that hard to make changes to relational database models, then it is okay to use SQL, instead of No SQL, right?

Well, as far as you are talking about a simple incremental change in an RDBMS, it is okay to stick to SQL. For instance, the client asks for adding a table and needs to change the data type, itself, for a column, there is no such issue with making those changes.

On the contrary, when there is one Uber Schema where you are locked into having it, the workload progress hinders. Relational databases are not suitable in that sense when new data sources come in – and that too, with a high degree of non-volatility and no proper structure, the idea of using SQL seems mundane.

At that point, it is extremely difficult to switch to No SQL because you spent a lot of time setting things up for workload to progress on a SQL RDBMS model. The new challenges are something that you or the company wasn’t prepared for.

Starting over is like ripping a band-aid.

After all, it is not just about making “one” change in RDBMS models. The data is in flux where new changes are constantly required.

Online shopping systems are complex and that’s the type of scenario that doesn’t stop at one point. The data piles up over time and power users cannot deploy a mundane database management technology, to begin with. Think of it as this way: swapping the wings of a plane, while it’s in mid-flight is not only expensive but an impossible feat!

Do Mid-Sized Organizations Need to Go for No Sql System?

Yes, they do!

It’s the natural order of things. Adapt change, or perish, while scrambling to do it later.

Imagine a bank system that’s in its mid-sized state. The workload isn’t much but it is eventually expected for the bank to grow and expand. As a result, the usual approach to modeling data, monitoring it, and administering it will require an iterative database management system with a sense of flexibility.

Similarly, many Fortune 500 companies use DBMS instead of RDBMS because the projects are done 4 times faster and the modeling phase is 15 times smooth.

Image Source: Mark Logic.

Top it all off for the ability to shard and partition your data across other nodes. In that sense, NoSQL can easily help to pull off table partitioning, indexing, and dividing datasets into smaller, malleable chunks over time.

Does all of it sound too difficult, to begin with?

Actually, it’s not that hard. Many companies have already made the “jump” towards No SQL database systems, while others are considering doing it.

There’s also a small proportion of IT businesses in Pakistan, KSA, and other South Asian regions where traditional database management technologies are working at full throttle. The business may be too reluctant to make the switch, or the program requirements are just okay enough to let SQL work better than No Sql.

Regardless, if you are looking to scale up, grow and expand your “mid” sized organization, No SQL is the right approach to modeling and managing unstructured data in a fast-paced environment.