A Concise Introduction to Big Data and Big Data Analytics
Table of Content
Big data is a phrase we come across often in computer technology. Though big data is essentially just data, it is however a little different from traditional data. Big data is associated with greater and constantly increasing volume and variety. The data on the Internet expands at a constantly accelerating pace. Variety, volume and velocity or the three Vs are constantly soaring when it comes to big data.
To get further in the discussion about big data and analytics, we need to understand a few key concepts.
What is data?
Data forms the foundation blocks of computation. These are strings of characters and symbols that may look like gibberish to the untrained eye, but are, in fact, the instructions or commands on which the computer operates. The data is then stored and transmitted as electrical signals. They are recorded on optical or magnetic recording devices.
What is big data?
As we mentioned earlier, big data is data, but it grows exponentially as time progresses and it is immensely complex. Hence, it cannot be managed by conventional tools that handle regular data.
So, what are the first things that pop in your mind when you think of impossibly large amounts of data? To help you understand the concept a little better, let’s explore a few big data examples.
We know that traders at stock exchange are rushed off their feet by the sheer volume of work that needs to be done. This work, however, is electronic, which means that at the core is data. Terabytes of data per day across the globe need to be processed at lightning speed and any glitch could trigger a devastating economic crash. Stock markets are being drastically impacted by big data.
Currently, there are 3.8 billion social media users globally. It roughly translates to 48% of the world population. Almost every day, social media users sign in to use Facebook, Instagram, Twitter, YouTube, WhatsApp, etc. And, every day new users are creating accounts. Can you guess how much data is generated on the Internet on a day-to-day basis? Facebook alone ingests more than 500 terabytes of information every day.
Types of big data
There are three types of big data:
- Structured data
- Unstructured big data
- Semi-structured big data
Structured data has a specific format and it can be stored, processed and extracted in the pre-established format. Innovations in computer science have made great leaps in structured big data possible and a lot of value can be derived from this type of big data. But, if the size of the structured big data expands to zettabytes (1 billion terabyte= 1 zettabyte), then problems crop up.
Structured big data is generally used by companies to maintain employee databases.
Big data can be classified as unstructured data when it does not have a specific structure. This type of data can accommodate enormous volumes but can be difficult to process and derive value from unless managed the right way.
A classical example of unstructured big data is a Google search. It contains text, audio files, pictures and videos and does not fit into the traditional template of rows or columns used in structured data. The same holds true for e-mails and social media messages. According to an estimate, 95% of all data is unstructured.
This is an amalgamation of structured and unstructured big data. The data is not stored in a relational database. But, unlike unstructured data, this kind of data has a few organizational features that allow it to be analysed. XML data is an example of semi-structured big data.
Characteristics of big data
Big data deals in terabytes.
The nature and sources of big data are heterogeneous. Once upon a time, databases or spreadsheets were the only sources of data. Nowadays, data disseminated through websites, e-mails, audio or video files, doc or pdf files, etc., too are valid sources of data.
This is the speed at which data is generated. The true worth and potential of data depend on how quickly it can be created and utilized to meet rising demands. Businesses, stock markets, our hand-held mobile devices, shopping websites, and social media pages are all reliant of big data being processed and generated in fractions of a second.
What is big data analytics?
Big data analytics is the activity of examining and assessing large volumes of data to gain insights into the data and decipher patterns and correlations. Why is big data analysis important?
- Allows for more robust marketing
- Opens up new avenues of revenue generation
- Makes it Possible to offer better customer satisfaction
- Enhances the efficiency of business operations
- Cloud-based big data analytics or Hadoop-like technologies slash costs of storing and analysing vast amounts of data
- In-memory data analytics and speedy analysis of new data sources help with faster and more accurate decision making in businesses
How it is done?
Unlike traditional data, big data cannot be stored in conventional data warehouses chiefly because they cannot handle the load of processing and analysing such immense volumes of data. And, this data needs to be continuously updated, such as data in a stock exchange or analysis of data regarding website visits or purchases.
That is why agencies that engage in big data analytics use Hadoop or NoSQL data warehouses as well as analytics tools, such as:
- YARN – This is a tool for cluster management.
- MapReduce – This is a kind of software that lets developers incorporate programs to process and analyse large volumes of unstructured data parallelly across a cluster of distributed processors or even stand-alone devices.
- Spark – This is another parallel open-source framework where large-scale big data analytics can be run using clustered processors.
- HBase – This is a column-based data store that is run alongside Hadoop Distributed File System.
- Hive – The sets of data stored in Hadoop can be analyzed with Hive.
- Kafka – This is a messaging technology (publish and subscribe model) and it is meant to supplant conventional messaging brokers.
- Pig – It allows high-speed parallel programming that can be executed on Hadoop files.
Hadoop and NoSQL act as landing platforms for big data before the sets of data are passed on to data warehouses aka analytical databases where the big data will be analysed and summarized to make it compatible with relational structures.
The actual big data analytics happens through complex analytical procedures and can be implemented through:
- Data mining – Rummages through sets of data, sometimes using web scraping APIs to assist in extraction, to establish patterns.
- Predictive analytics – Create models that foresee market trends or behaviour of customers.
- Machine learning – Use advanced algorithms to analyse big data.
- Deep learning – It is an even more complex machine learning tool that analyses data.
- Text mining – This is a statistical tool that functions the same way as BI (Business Intelligence) software and can carry out big data analytics for businesses.
A drawback of big data analytics
The only shortcoming of a technology that is revolutionizing the supply chain is the astronomical cost of running big data analytics. It requires highly trained and skilled data engineers.
Integration and management of big data along with the big data analytics is a hefty investment but it promises attractive dividends. It helps your business make newer and newer discoveries that will further your goals of expansion.
That was our simple and concise blog to make it easier for beginners to understand the basics of big data. If you want to learn more about big data or just want general knowledge regarding other business related subjects, we suggest heading over to our Academy. We upload free business related courses every week. Our latest was a free App Developer Certificaton course. Good Luck!
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