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3 Ways NoSQL Can Be Part of Your Data Architecture
As big data becomes more commonplace, it is important for data management teams to be able to understand where NoSQL fits into their architectures.
In the past two decades, we have seen the term big data burst onto the scene. At one time in the mid-2010s, the term was everywhere, including in publications, presentations, and classrooms. Today, that flurry of excitement around the term has started to abate. It is not that the concepts of volume, velocity, and variety have gone away. It is just that now, these concepts are a de facto standard in data architectures. What used to be considered big data is now just considered data.
This means that today, data architectures must be a combination of different technologies, including data lakes, clouds, real-time streaming, and unstructured data containers, to support these different facets of functionality. The ability to rely solely on the relational database as the core of the data architecture is a thing of the past.
As data architectures become more complex and multifaceted, business users want to be shielded from them. They look to the data management team to take care of the complexity that exists in the back end of this data jungle and provide data in a consistent, clean, and easy-to-access format that they can use for their everyday analysis.
One of the technologies becoming central to modern-day data architectures is NoSQL. NoSQL is a family of technologies that were specially designed to handle the volume, velocity, and variety of data needed to run your business. Within this NoSQL family, there are four different database types: wide column, key-value store, document store, and graph. Each is different in its architecture and strengths, but they share the commonality that each was built to fill a gap that existed in the relational database model.
One of the challenges with these technologies is that they are not always as end-user-friendly for performing analytics as were the relational database and spreadsheet interfaces of the past.
The challenge for data management groups is to incorporate the power of NoSQL in the right places in the data architecture while keeping the user experience clean and easy to use. Data management teams looking for where to leverage NoSQL can focus on three areas: data ingestion, automated insights, and data lakes.
Data Ingestion
Depending on your organization, your data management group might or might not have influence over which data stores are selected as part of the application development process. With options such as document-store databases and key-value databases, many application developers find these NoSQL databases are a better fit for their needs than the traditional relational database.
Developers often attribute a key-value data store with a dictionary data structure found in multiple programming languages to store groups of objects. In this data structure, each object is referenced by a unique key. A key-value data store functions the same, but with its distributed architecture, it outperforms a dictionary data structure because it can be massively scalable and is durable across sessions and systems. It allows developers to expand systems beyond the limits of a single computer to a distributed cloud environment.
Similarly, they often look at document-store databases as more aligned with the object-oriented approaches that they use within their code. Being able to serialize and deserialize their objects into JSON or XML can appear to be a fast-track approach to getting their application code developed quickly. These databases also allow the developers to easily scale out their applications and harness the power of distributed computing.