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For decades, traditional relationship database management systems (RDBMS) have reigned supreme.

Giants like Oracle and Microsoft have made billions in licensing costs for those databases.

But the entire database marketplace is being turned on its head.

In this article, we look at why you need new tools for data and what some of those tools are.

Dynamic Data Types Alongside Rapid Data Growth


There has been plenty of talk about big data.

According to a 2016 survey by IDG Enterprise, 75% of enterprises have already deployed or plan to deploy big data projects.

Companies see the potential to do more with the massive amounts of data they have.

The challenge that they have is making use of all of their data.

According to Veritas, 52% of all of a company’s data is dark data. Dark data is data that gets collected and then never analyzed or used.

On top of that, another 33% of enterprise data is redundant, obsolete, or trivial (also known as ROT). IBM states that a big challenge in using data is that much of it is unstructured. 

Applications Are Different Today

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Applications and how they utilize data has evolved. If you think about websites just 10 years ago, they were very static.

You went to an eCommerce site, found what you were looking for, added it to your cart, and checked-out.

Now, you are presented with real-time personalization and recommended items based on what you have purchased or viewed in the past.

Data-intelligent organizations want to gain insights into their data to make critical decisions, and they want to do that in real time.

You might want to know why one brand or product is performing better than others. You could see that a certain feature is requested by 87% of users. Data can help show you that and tell you why.

Along Comes a New Set of Data Tools


Because data is coming in at such a fierce rate from different sources, traditional relational databases just cannot keep up.

In relational, you first have to define the data structures, or schema. You pre-set the rows and columns to the data type that will reside there. And the data that comes in must rigidly fit into those structures.

NoSQL databases have emerged to fill this need. The NoSQL model dispenses with those rigid data structures.

They each accomplish this in their unique way so that the data can still be stored, identified, retrieved, and analyzed. NoSQL is the fastest growing area of the open source database management systems (OSDBMS) market—and the OSDBMS market is growing.

Gartner states, “By 2018, more than 70% of new in-house applications will be developed on an OSDBMS, and 50% of existing relational DBMS instances will have been converted or will be in process.”

NoSQL has solved many of the data-related problems and delivered improved flexibility by eliminating rigid structures.

But some data still performs quite well in very structured formats. Applications like your ERP or CRM are perfect for relational database models in many cases. Many of those are built on the giants like Oracle and Microsoft SQL Server.

These software giants carry huge licensing price tags and ongoing annual maintenance costs, which eat away at the IT budget.

To combat that cost burden, many companies are turning to open source relational databases.

Examples of those include EnterpriseDB, which is based on PostgreSQL, and MariaDB, which is a cousin of MySQL. And some NoSQL databases like MongoDB have added some SQL functionality.

The Database Landscape Will Continue to Evolve


Other examples of OSDBMS include Neo4J, Redis, and a long list of others.

The corporate database landscape has changed dramatically over the last several years.

And one thing is certain: As our applications and requirements continue to evolve, the database tools that we use will evolve as well.

Hardware is often an overlooked component, but it is critical to your database performance.

Click Here to Get your NoSQL Developers’ Guide to Infrastructure and Maximizing Data Performance.

Written by Steve Erickson