Big Data is a chunk of raw data collected and stored to be analyzed by organizations to help devise more efficient and profitable decisions. Compared to the conventional relational database management systems where the data is strictly structured, Big Data can be in either form as structured or unstructured.
As we can assume, structured data can be analyzed more easily. But most of the times now, coming from various sources and in bulk, data tends to be in the unstructured form in various format, which also needed to be incorporated into the analysis.
Nowadays, more and more enterprises, irrespective of being large or small, now use big data analytics in order to gain a better insight towards the company's performance and to serve the customers better based on those insights.
Big data in its generic meaning is the collection, storing, processing of real-time data streams. Three V's are considered crucial in Big Data, which are Volume, Velocity, and Variety. The modern-day enterprises are combining the customer data, marketing, and sales data, social conversations, and other general data like stock market information, weather forecasts, political news everything to correlate and adopt statistical models to help in business decision making.
The problem with conventional databases was its inability to handle multiple, huge volume effectively, and real-time streams of data. Reporting tools also were lacking in the traditional databases, and these couldn't also handle something more than relational queries.
However, the current Big Data applications can offer huge volume cloud hosting of data, optimized database structures, automatically customized data extraction capabilities, as well as high-end reporting interfaces to make data interpretations easier and understandable to anyone. All these help the businesses using Big Data technologies to make better decisions.
As discussed above, Big Data is a fine combination of three V's:
- High Volume: Typical Big Data applications now effectively observer various sources and see what happens in each and every business transactions along with every single input from social media and market fluctuations. Through M2M (machine to machine) or sensor gathered data, this quickly creates a huge volume of data.
- High Velocity: Unlike the data management practices of old times, real-time data speeds are high, and it needed to be handled in a timely manner. Processing of these data and live analysis of it is needed to produce instant results too.
- High Variety: We can see that data, nowadays, come in all different formats, in a highly unstructured way. It could be texts, images, audio, video, or even the stock ticker information coming in every second.
The real importance of Big Data is not in terms of the volume of data a company gathers, but how effectively an organization is utilizing its data. There are different ways through which every industry and each organization using its data and the more efficiently an enterprise can use it, the more potential it has in terms of results. Big Data enables a business to take data from any source, in any format, and analyze it to get better insights. Here are a few significant advantages of Big Data:
- Saving cost: Many Big Data applications like Hadoop or cloud-based data analytics will offer significant cost advantages for businesses with the need to store huge volume data and processing it to help make efficient business decisions.
- Saving time: Hadoop and features like in-memory analytics are super fast and can instantly identify relevant data sources to help the businesses to analyze the inputs immediately and speed up the decision making to reap the first to market advantages.
- Product development: Big Data helps businesses to understand the changing market trends, and upcoming customer needs with the strong analytical tools and design product and services to match to the needs and want of the consumers.
- Knowing market conditions: Bit Data analysis helps you gain a better understanding of the market fluctuations and change in purchasing behaviors of potential customers. With this, a company can understand the specific market sectors they have to focus on and get ahead of the competition.
- Manage online reputation: One more effective application of Big Data is now to do sentiment analysis. You can get exact feedback about the consumer opinion about your brand and products. It is important to keep a close watch of it if you want to ensure the online presence of the business, for which Big Data tools can help.
The big question business administrator have in mind when the world is stepping into Big Data is 'Whether RDBMS may coexist with the Big Data platforms?' Let's look into the expert opinions about this matter next.
One unaltered rule in the technology industry is that the new technologies are coming in destroy or replaces the existing ones. We have seen how laptops replaced PCs, smartphones replaced cameras or flip phones, and online streaming is now replacing music and video rental, etc.
However, in contrary to this common notion, it seems that peaceful coexistence of relational database management and Big Data is proving out to be a norm in database administration. It seems that these two technologies are not contradictory, but complementary to each other. RDBMS are expected to stay for longer as the observers see the big data technologies are taking over the sector in future.
A unique way to look at RDBMS vs. big data conflict is the concept of data centralization vs. distributed data architecture. RDBMS can be instantly related to centralization. A server acts as the guard and owner of your data and ensures consistency. However, when it comes to too many queries at a time, the RDBMS will give up and say sorry. However, Big Data will act smart in such situations (which is expected to be the need of the future) and handle any volume and any number of queries simultaneously.
Still, we see Big Data as a shiny new object hitting the world by storm. However, in light of the need of the industry, it is not fully disruptive. In every possibility, as of now, we can expect there is no immediate replacement of the entire transactional spa, and relational databases are also here to stay for a significant time being.
