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In this article, we are trying to cover all the basic fundamentals of big data technology, its types, and its characteristics.
In the modern world of technologies, there are many technologies that are helping companies to grow like Artificial Intelligence, Machine Learning, Data Science, BlockChain, etc. So big data is also one of these mentioned technologies. These technologies are somehow connecting with each other.
The role of big data in organisations for delivering priceless insights, unfolding the strengths and weaknesses. Additionally, it enables companies to improve their businesses with the help of available data.
Definitely, many of the companies understand the importance of the data and some are seeing the influence of it. We are living in the digital world of immediate expectations and data sets are growing rapidly and applications are generating more real-time, streaming data. Big data helps to analyse this data and how a company can use this data to make the right decisions.
What is Big Data? Its types and characteristics Gartner Definition
As per Gartner Definition, the big data definition is:
“Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”
Gartner Definition clearly says that big data refers to the collection of a large amount of data that is growing rapidly. Moreover, it can be in the form of structured and unstructured. As a result, the available traditional data management tools cannot collect and process this type of data.
But the amount of data is not an important thing here. It is what companies are doing with this data that is important. Definitely, Companies are using data to analyse that leads to better ideas and strategies.
Big data is really a revolution in the IT field. The usage of big data analytics is growing every year. There is a huge demand in the market for big data experts. Many training institutes providing courses on big data analytics which helps to promote the skills needed to manage and analyse big data.
Sources of data
Following are some of the sources of data:
Social Media
Around 500+ terabytes of data stored in the databases of social media sites like Facebook, Twitter, Instagram, etc. Obviously, this data is only generated due to uploading photos and videos, messages, and comments.
Flights
A single flight can create around 20+ terabytes of data in 1 hour of flight time.
Transactional Data
A huge amount of data generated by daily transactions done online or offline. On the other hand, payments, invoices, storage data, delivery orders, etc- all are types of transactional data.
Streaming Data
Streaming data can be generated from the Internet of things (IoT), or other devices that come into IT systems from smart vehicles, medical instruments, and industrial pieces of equipment, and many more.
HealthCare
Big data is extremely used in Health Care, it includes collecting, analysing it, and benefits it for customers. There is also huge amount of patient clinical data that is very complex to solve using traditional systems. This huge data is manageable from big data as it is processed through Machine Learning (ML) algorithms and data scientists.
Following are three types of big data:
- Structured
- Un-Structured
- Semi-Structured
Structured Data
Structured data is already stored in databases in the proper format of rows and columns which can be easily processed. For example, any table stored in the company’s database containing employee’s related information.
Structured data also has the predefined data model which helps how the data will store, accessed, and processed.
UN-Structured Data
UN-Structured data is not organised in a proper manner or does not have the predefined data model. Also this type of data does not have a clear format. UN-structured data is like a heavy text which contains dates, numbers, and facts too. Generally, this type of data includes Audio, videos, or the No SQL database.
Semi-Structured Data
This type of data does not store in the traditional format of the database as structured data, though it has some properties by which it can be easily processed. Indeed, JSON and XML formats are considered as semi-structured data. Many big data solutions or tools have the capability to read and process data from JSON or XML. As a result, it decreases the complicacy of analysing big data.
Big Data Characteristics
Following are the big data characteristics:
- Volume
- Variety
- Velocity
- Variability
- Veracity
Volume:
As big data already reveal huge “Volumes” of data that companies collect data on the daily basis from multiple sources like social media, machines, and internet of things (IoT), Financial transactions, human interactions, etc. Indeed, this huge data stored in the data warehouses.
Variety:
Variety is also an important characteristic of big data. Data comes from multiple sources in the form of structured, unstructured, and semi-structured. A while ago, the data only be collected from databases or excel sheets, nowadays data comes like emails, photos, PDFs, videos, audios, and more.
Velocity:
Velocity refers to the speed of data generation in real-time and it should be handled appropriately. Particularly, it bargains with the speed at which data flows in from many sources like logs, networks, social media, sites, mobile devices, etc. This flow of data is vast and sustained.
Variability:
Besides the growth of velocity and variety of data, the data flow is inconsistent. Thus, interrupting the process of being able to manage and handle the data effectively.
Veracity:
Veracity means the quality of data as data comes from many sources, so it is difficult to match, clean and transform the data across systems. Importantly, data needs to be garbage in and out appropriately that makes sure the data is correct and clean.
What are the main advantages of Big Data?
The following list is the advantages of big data:
One of the main advantages of big data is predictive analytics. It can predict the outcomes correctly, thus, it helps businesses or companies to make better decisions, and at the same time, it optimises the operational efficiency and decreases the risks.
As big data generates accurate results, so it combines the relevant data from many sources to produce large actionable insights. Absolutely, there are many companies that don’t have the necessary tools to filter irrelevant data, which expense those millions of dollars to have useful data.
Compete with big businesses: Using the same tools that big businesses do allows you to be on the same playing field. Obviously, your business becomes more sophisticated by taking advantage of the tools that are available for your use.
Big data ensures us to hire the right employees: Recruiting companies can scan candidate’s resumes and LinkedIn profiles for keywords that would match the job description. The hiring process is no longer based on what the candidate looks like on paper and how they are perceived in person.
It helps to identify the failures, causes, and problems in real-time.
Full understanding of the potential of data-driven marketing.
Create offers for customers as per their purchasing habits.
Improvement of the commitment of the client and increase of his loyalty.
Re-evaluation of the risk portfolio quickly.
Customisation of the customer experience.
Adding value to the interactions with online and offline customers.
There are many tools available in the market for big data which helps in time management into data analytics tasks.
The following are some of the common tools for big data analytics tasks.
Hadoop
The Apache Hadoop is the most useful tool in big data analytics with its huge capability which can process large-scale data. Particularly, Apache Hadoop is the open-source framework and works on commodity hardware in the existing data centre.
Download link: https://hadoop.apache.org/releases.html
Apache Storm
The Apache Storm is also a free open source computation system of big data developed in Clojure and java. This is a cross-platform, distributed real-time and fault tolerated computational framework. This framework can support any programming language.
Download link: http://storm.apache.org/downloads.html
Cassandra
The Apache Cassandra is the widely used distributed type database to manage a large amount of data across the servers. This big data tool is suitable for applications that are not able to afford data loss. Especially, this tool processes structured data sets.
Download link: http://cassandra.apache.org/download/
Qubole
This is also the open-source big data tool which is a self-manageable and optimisation tool. Especially, It allows the data team to focus only on the business outcomes, not on managing the platform. It also provides alerts, insights, and recommendations to optimise performance, reliability, and costs.
Download link: https://www.qubole.com/
CouchDB
CouchDB is an open-source, cross-platform NoSQL database. It stores data in JSON documents and JavaScript queries help to access this data. CouchDB is written in Concurrency-Oriented language Erlang.
Download link: http://couchdb.apache.org/
Conclusion
Thanks for reading this article. Finally, we have covered what is Big Data technology, its types, and characteristics, advantages of Big Data. Additionally, what are all the common tools available in the market of big data?. If you like this article please provide or leave your valuable feedback to improve this article.
