Big Data and NoSQL: Analyzing Complex Application Portfolios

There is more data to manage today than ever before, and this is creating an increasingly pounding headache for business executives that no dose of aspirin will soon relieve. With so many different forms of data and ways of storing that information within the organization, new data management methodologies are needed to make sense of this mind-numbing flood also known as Big Data.

Enter NoSQL. Differing from its much older and experienced brother – SQL – NoSQL has come onto the scene as the “new” and “hip” database paradigm (much like we talk about the Millennial generation). Also known as “Not Only SQL”, NoSQL is a flexible approach to data management and design that is useful for very large sets of distributed, unstructured data.

SQL is known for its ability to quickly query very structured, small sets of data, and it does so quite effectively. But with growing enterprise complexity and new forms of data emerging from things like connected devices and the Internet of Things, has given rise to NoSQL, which is less relevant for “small” structured datasets that are heavily transactional. NoSQL shines when handling big datasets that are queried frequently, in many different ways, requiring fast results. For this reason, NoSQL has become more popular when trying to analyze complex application portfolios and evaluate the structural soundness of software applications within complex ecosystems.

NoSQL encompasses a new family of database models that are not based on the original model by Edgar F. Codd, which was eventually named SEQUEL – then SQL – by IBM and Oracle. NoSQL is unique because it can easily manage a large number of server resources elastically using scale-out, rather than scale-up. The danger of NoSQL, however, is that it can diminish relational database skills because it is a framework that automatically manages itself.

Overall, NoSQL engines are specialized to ensure either consistency or high availability in distributed architecture. NoSQL engines are segmented to take advantage of this feature, rejecting historical “table” SQL databases. Instead, NoSQL agrees well with the unstructured system of Big Data. This is usually done by following five structuring models depicted below:

While still fairly new to the scene (standard best practices for NoSQL have yet to be established), CAST expects to see more business applications using the NoSQL data architecture, and we have started consulting some of our customers in this regard.

Effectively and correctly managing enterprise data is extremely important to an application’s robustness and security. Poor data management can often result in data corruption that leads to software glitches and widespread outages, taking business operations down for days.

As applications that use NoSQL become more core to the business, their structural quality must also be closely monitored and measured.

To conclude, here’s what the archetypical MapReduce function would look like if it were written in SQL:

 

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