Big data is a collection of very large sets of data that stream into a business every day. These sets can be structured or unstructured. Big data can be analyzed to reveal trends, relations or patterns in the behavior of a company’s customers. The analysis is conducted with the assistance of computer systems.
Big data has a number of characteristics. They include:
Large volumes: The amount of big data that arrives in a business is tremendous. This is because it comes from many sources all at the same time. Examples of these sources are transactions, social media sites, data from one machine to another and the information provided by sensors installed in the organization’s infrastructure. Due to the size of big data, special databases have been created to store it.
- High velocity: Big data streams into a business at astonishing rates. Due to the high entry speed, special technology is needed to measure it in real-time. Examples of these are RFID tags, smart meters and high-response sensors.
- Wide variety: This type of data arrives in a business in all types of formats. Some of it is structured while some is not. An example of structured data is numerical information while unstructured data is documented information. Other formats include video footage, audio files, email transcripts, stock data and financial information.
- Variable nature: The flow of big data into a business normally changes according to conditions in the sources. Sometimes it can be low and at other times it can peak. Events such as viral content in social media can trigger massive peaks of big data. This can be hard to manage, especially when it is unstructured.
- Highly complex: Thanks to its multiple sources, big data is highly complex. It is difficult to match, link and cleanse before being transformed for various systems. Substantial computing power is required to create relationships in big data, organize it into hierarchies and link data sets. Without the proper technologies in place, big data can become uncontrollable.
- Databases are used to store big data. They are collections of data that is properly arranged such that it is easy to access, maintain and update. Special databases are required for big data.
Traditionally, we used relational database software to store data. They were built for Structured Query Language (SQL). Examples of these are Microsoft Access, Oracle RAC and MySQL database software. When big data entered the business scene, they were no longer powerful enough to handle the demands of this type of data. Therefore, computer scientists in conjunction with Remote DBA Experts created the NoSQL database software.
What is it?
NoSQL database software supports dynamic schemas. They are flexible, scalable and customizable too. They use four main methods to store big data. These include:
- Document storage
- Key-value storage
- Graph databases
- Column family storage
Examples of NoQSL database software include MongoDB, Couchbase Server, MarkLogic Server, RavenDB, Apache Jena and Hadoop NoSQL database software.
It is important to note that you cannot simply install a NoSQL database to replace your relational one and store traditional data in it. NoSQL database software is made specifically for big data. This is because it does not have complete compliance with ACID (Atomicity, Consistency, Isolation and Durability). This compliance normally guarantees the integrity of transactions and consistency of data. Due to the nature of big data, ACID compliance was relaxed. This maximizes the ability of NoSQL database software to collect and manage big data.
Why is NoSQL database software used for big data?
This type of database software does not utilize the usual elements that we are used to seeing in relational databases. There are no tables, columns or rows. Moreover, you do not need a schema to design and create a NoSQL database. This type of database software is designed in this way so that it can provide you with rapid access to real-time data. This sort of access empowers you to run real-time programs for your business processes. An example of this is in the stock market. NoSQL database software organizes data using new formats that were not utilized traditionally. The lack of a schema allows you to interact directly with massive amounts of data, saving you time and money.
Impact of big data in business
It has rejuvenated traditional industries
This type of data has transformed various aspects of traditional businesses. By injecting the power of information, it has refreshed them. Big data affects various departments of a business. Examples are the customer service and supply chain departments.
Quite a number of traditional enterprises have gained substantial benefit from implementing big data. An example is the Rolls Royce Corporation.
Well known for their automobiles, Rolls Royce also manufactures aircraft engines. Traditionally, these engines needed to be inspected in hangars. Today, the engines have hundreds of sensors that monitor their performance in real time. The sensors send engine data to Rolls Royce through big data infrastructure. In this way, the company has leveraged big data in its operations and increased revenue by selling engines and engine-monitoring services in one package. These packages account for over 70% of the company’s annual revenues.
It has created a brand new industry
Traditionally, data was collected simply for reference. Managers and accountants referred to it for the purpose of justifying investment or knowing the progress of the company. Today, big data can be collected for the purpose of profit. Whoever is able to collect and cleanse as much big data as possible can sell it to other companies for a fortune. Stakeholders in the IT industry have discovered this and many are establishing startups whose primary objective is the collection and sale of big data.
Big data is here to stay. By investing in the database software indicated above, companies can reap the benefits of using big data in their business processes. It has even created an industry that is attractive and lucrative.