Covering Disruptive Technology Powering Business in The Digital Age

We live in a world that is driven by information. There is literally a flood of information flowing into our organizations today. It is known as big data. Traditional database software no longer has the capacity to manage this immense volume of data. Thankfully, innovators have come up with new database software that can store, manage and disseminate this data. This software makes it necessary for database administrators(DBAs) and application developers to learn new skills so that they can manage them.

What is this new database software technology?
Traditionally, we used relational databases to store and manage data. These database systems relied on a Structured Query Language (SQL) framework to accomplish this. Examples of these database software are Microsoft Access, MySQL and Oracle RAC.

Since the emergence of big data, relational database software’s are getting phased out. This is because it is inefficient to organize big data into the structured tables used in this type of database software. Only small and medium amounts of data can be organized into this structured format. The immense volume of big data would take forever to organize in this way. Therefore, traditional database software is not scalable enough to handle big data. For this purpose, NoSQL database software was invented.

What is NoSQL?
NoSQL stands for Not Only SQL. It is a database framework that is built to support high-speed data storage and access. It is also capable of lightning-fast processing of massive amounts of data. It can be thought of as a high-performance database software technology.

The primary objective of NoSQL database software is to promote efficiency. As such, these databases are not structured. Unlike relational database software, data is not organized into rigid tables. By keeping it in a free form, the data can be stored or accessed much faster.

NoSQL database software technology is applied according to the format of distributed databases. In this format, unstructured data is stored in many nodes that are also capable of processing. In most cases, you will find it distributed in many different servers. By storing it in this way, The NoSQL databases can easily be scaled horizontally. If the amount of big data increases, all that is needed is simply more hardware to store it. There is no reduction in the speed of data access or processing performance. This distributed storage format is used to store some of the largest information repositories in the world for companies such as Amazon, CIA and Google. This database technology has inspired the creation of storage architectures such as Hadoop.

What is Hadoop?
Despite popular opinion, Hadoop is not a type of NoSQL database. It is actually a software-based ecosystem that allows you to perform parallel processing on a massive scale. Hadoop enables the integration of NoSQL database software, for example, HBase. Hadoop facilitate the spread of data across as many servers as you desire- with no lag in performance.

MapReduce is a model of computing that is found in Hadoop integrations. In this model, heavy data processing is spread across a multitude of servers. These servers are known as Hadoop clusters. The Hadoop ecosystem has played a significant role in solving the processing requirements of big data. It is so efficient that a processing task that would normally take 20 hours on a centralized relational database software system will take 3 minutes in a Hadoop cluster performing parallel processing.

Big data is growing with no signs of slowing down. Innovations such as the Hadoop processing ecosystem and NoSQL databases are instrumental in managing this data. Companies can invest in these innovations and use the big data as competitive advantage. Moreover, the acceptance of these technologies in our business environments has given rise to new job descriptions, responsibilities and even business opportunities. Infrastructure for Hadoop and other NoSQL database software is always a good investment. One installation of the core technology is enough to get Hadoop up and running. All that you will have to do over time is purchase more servers to store the big data.

Hadoop has three outstanding characteristics:
1. Scalability
2. Cost effectiveness
3. Flexibility

It is scalable
This is a significant characteristic of this storage platform. It is designed such that it can store massive sets of data over hundreds of individual nodes. These perform parallel processing and present data at high speeds. Hadoop can be scaled up to accommodate data sets of any size. It can be improved to cover thousands of nodes and hold more than a thousand terabytes of information. This is a big advantage over traditional relational database software.

It is cost effective
Compared to relational database systems,Hadoop is more cost effective. It would cost much more to host a thousand terabytes of data with a relational database than it would with Hadoop. The infinite scalability of Hadoop means that you can store as much data as you desire at low costs.

It is flexible
Hadoop allows you to access new sources and types of data. It allows you to access structured and unstructured data. In this way, it allows businesses to collect and leverage data from sources such as social media, clickstreams and conversations over email. In addition to that, the Hadoop ecosystem can be utilized for processing logs, warehousing data and analyzing your company’s marketing campaign.

Hadoop is one of the reliable methods of handling big data today. It is a wide, capable ecosystem that is ideal for storing, processing and retrieving this type of data. Data that is stored in this type of ecosystem can be accessed much faster than that which is stored in traditional databases. It can be streamed in real time and optimizes many business processes. In the long-run, Hadoop is far more cost effective than traditional methods of data storage and is therefore a good investment.

This article was originally published on www.ciol.com and can be viewed in full

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