Forms and Features of Big Data Databases in Enterprise Application Development

To clearly understand the Big Data concept, we should first understand the significance of data in modern-day enterprise applications. Big Data stores consist of data in really gigantic sizes. As its name suggests, Big Data is a collection that keeps on growing every second. As this data collection is unimaginably complex and huge, traditional databases that are meant for handling structured data may not handle it well.

Various forms of Big Data

Big Data takes three different forms as structured data, unstructured, and semi-structured data. Let us explore each of these in detail.

Structured data

Data can be stored easily in a fixed format can also be processed easily on being structured data. Over a given course of time, the changes in technology have also brought in some bigger achievements in many new data management applications to optimize enterprise databases' performance. However, as the data volume started to increase hugely lately, there may be some issues also which we tend to overlook while handling data in zettabyte sizes (FYI, one zettabyte is one billion terabytes). On considering this gigantic size of data growth, one can imagine what ‘Big Data' will imply and the reach changes that will happen in terms of data storage and processing. Datastores in the conventional relational DB systems are primarily structured data.

Unstructured data

Any data which lacks a proper structure is unstructured. In addition to the gigantic size of Big Data to manage; unstructured data also put forth challenges in delivering full value. Heterogeneous data sources now contain a huge amount of data in text, images, videos, data from various sensors, etc. Even small organizations now have access to various data sources, which generate a huge volume of data in various formats. However, many of the data owners don't' fully know how to derive value out of their available data as most of these are in unstructured form.

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Semi-structured data

These types of data consist of both structured and unstructured data. However, apart from these, semi-structured data can also be defined as per the table definitions of relational databases.

Benefits of big data

 As modern organizations are now looking for Big Data implementations to enjoy the first to market benefits and gain actionable insights on businesses, it has become evident that relational DBMS cannot handle the need for huge database management.

As a result, there are plenty of challenging big data applications that have emerged by using various technologies in storage, processing, and recovering huge datasets effectively. Even though each of the big data applications may differ in terms of technologies and other aspects, the primary goal of all Big Data DBs is to store and process huge data stores. You may take the support of providers like for remote database administration related queries.

The major characteristics of modern-day data are:


As we discussed, Big Data is usually measured in exabytes or zettabytes. Relational databases can scale up only by increasing server capacity storage, and these are not designed to function on the commodity hardware. These also require complex sharding methodologies to distribute data across various servers. All these will make scaling up much difficult and disruptive.

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For example, Oracle DBMS may cost many million dollars in storing about 20-terabytes of data volume. Big data applications are meant to considerably minimize the cost and with innovative scaling approaches, making it easier to add data more quickly and reduce the capacity by using inexpensive commodity hardware.


Back in the past, most of the data available were structured to fit the specific data model of relational databases. With big data stores, it is also possible to store unstructured data from various sources like social media, images, video, or time-series data.

Previously, the only possibility for relational DBS to handle heterogeneous and unstructured data was by converting the data to fit the predefined format through very complex and time-taking procedures. However, now big data covers it very effectively by using flexible data models to store data in various formats and easily retrieve it.


Velocity of data interactions is critical in the highly competitive market scenario to take the first to market advantage for businesses. Quick decision making is also critical in terms of business success. With very massive volumes of heterogeneous data in hand, it becomes highly challenging for the RDBMS to process these in real-time.

Big data, however, can handle this challenge also well. These tend to keep up with the unrelenting demands of capturing a huge volume of data in various formats and process them at lightning speed to ensure the data's availability and performance anytime.

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When it comes to NoSQL datastores, which effectively handle big data, the challenge of prohibitive complexity is well addressed. So, in terms of scaling up needs, the huge cost involved in relational databases are eradicated on NoSQL. Scaling up or down can be done quickly based on the organization's need anytime at an affordable cost.


When organizations try to introduce new mobile, web, or IoT applications, the old-school relational database models may tend to slow down or prevent the application's speed and performance with inability as the data storage size increases. Fixed data relational models may hardly do multi-tasking. On the other hand, the big data models or NoSQL will let the developers use any data types and querying approach for faster and agile development.


As discussed above, increasing performance is ensured with NoSQL compared to RDBMS by avoiding manual sharding overhead. In terms of resources, when computing resources are added to any NoSQL database, performance will increase proportionally to the organization's growth.

As all organizations of our time are looking for gaining actionable insight from data, big data is becoming an inevitable need to succeed. However, while adopting big data, it has some drawbacks, too, compared to the structured data models in relational DBs. There is also a mid-point between these, as many organizations tend to acquire as hybrid DBs. These databases offer the benefits of relational databases as more reliable and structure. In contrast, they can accommodate the needs for handling data in huge volumes and higher-level data processing needs.

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