An Expert Understanding of Big Data Databases for a Fresher

To understand the concept of Big Data, we should first know what data specifies. When it comes to database administration for business applications, data is primarily the quantities, symbols, or characters on which the computer operations are performed, which is stored and transmitted as electrical signals.

Big Data is data but of huge size. As the name suggestions, it is the collection of data which keeps on growing with time. Such a collection of data is unimaginably huge and complex for the traditional database management tools to store or process.

Forms of Big Data

Big data can be primarily found in three different forms as:

1. Structured

2. Semi-structured

3. Unstructured

Structured Data

Data which can be easily stored in a fixed format can be updated or processed easily is known as ‘structured’ data. Over the course of time, changes in computer science technology have achieved some bigger success in terms of developing unique data management techniques to optimize database performance. However, as the size of data grows to a huge extent lately, we may foresee some issues as the data rage is to multiple zettabytes (one billion terabytes is a zettabyte).

On looking at this huge growth in data, one can imagine what the name ‘Big Data’ implies and the real challenges involved in terms of this huge data storage and processing. The data stored in conventional relational database systems are examples of structured data.

Semi-structured Data

Semi-structured data consist of both data forms as structured and unstructured. However, semi-structured data is also some sort of structured data form, but not defined properly as per the table definitions in relational databases.

Unstructured Data

Any data in an unknown structure is known as unstructured. In addition, the huge size of data to manage, the un-structured database also poses challenges for deriving some real value out of it. The heterogeneous sources of data now contain a huge amount of text, images videos, etc. Nowadays, even smaller organizations have a wealth of data which is available to them in different formats, but they don’t fully know how to derive actual value out of it since such data is in unstructured form.

Big Data Benefits

As the organizations are largely looking for Big Data to deliver actionable insights for businesses, it has become clearer that the relational DBMS which had been into practice for more than three decades now is not capable of handling these huge data requirements. As a result of it, plenty of big data applications and options have emerged lately. Even though the technologies differ in various aspects, reiterates the fact that all of these are primarily designed to overcome the shortfalls of the conventional RDBMS and enable the organizations to reap results from their data.

To understand why there is a demand for innovative database options for handling big data, you must first understand the three major characteristics distinguishing big data as volume, variety, and velocity.

  • Volume: As we have seen above, the corresponding is measured in terms of petabytes, exabytes, or zettabytes. even unstructured relational databases were used to scale up by the time-series space and storage capacity as these systems are not primarily designed to function on any commodity hardware. They also require complex sharding techniques to distribute the data across different servers effectively. All these makes scaling up a very complicated, disruptive, and expensive procedure.

Say, for example; a typical Oracle system may cost many millions in order to store a fine 20 terabytes of data for an organization. However, the big data applications will considerably minimize the cost in terms of scaling approaches which makes it easier to add data quickly or reduce the capacity by using some inexpensive commodity hardware. There is also no need for any manual interventions for these tasks.

  • Variety: In the past, most of the available data were already structured to fit the specific model of the corresponding RDBMS. However, with the introduction of big data, even unstructured data like social media posts, video, images, or to the time-series IoT data is growing rapidly compared to structured data with a need to store these.

The only possible way through which RDBMS may handle heterogeneous data which doesn’t fit the predefined format is through a very complex and tiresome workaround. However, big data databases effectively cover up this problem too by using flexible storage models to make sure that all types of data can be stored and retrieved easily using various innovative methods.

  • Velocity: Speed is crucial in the current market scenario and consumer perception. As we can see, massive heterogeneous data is being generated every minute and databases are supposed to ingest, store, and process these in real-time without fail. This is very important with some information like time-series IoT etc.

Without the capability to handle such a huge volume and variety, the typical RDBMS performance may largely suffer and cause severe downtime. However, the latest big data databases are meant to keep up with the unrelenting demands of capturing and storing the troves of disorganized data without compromising on performance and availability.

Big data systems are known lately as NoSQL databases as there is no need for the SQL queries as needed in the RDBMS. The different NoSQL database categories are; key/value, document, big table, graph, and the time series, etc. Each of these has their own benefits. Some common benefits in terms of big data use cases are:

  • Scalability: Prohibitive complexity is almost eradicated in case of NoSQL databases, and the cost involved in traditional RDBMS is also lessened. Scaling up can be quickly and effectively done at any time to embrace the big data initiatives.
  • Flexibility: When an organization is introducing web, mobile, or future generation IoT applications, fixed data RDBMS models will prevent or slow down its ability to get adapted to increasing big data requirements. On the other hand, NoSQL lets the developers use any sort of data types and querying options for agile and faster development.
  • Performance: As discussed above, increasing performance is ensured with NoSQL compared to RDBMS by avoiding the overhead of manual sharding. In terms of resources, when computing resources are added to any NoSQL database, performance will increase proportionally to the organization’s growth.

As of late, the high availability of a variety of flexible and user-friendly big data applications make it more adaptable to the startup businesses too.

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