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Industry 4.0 - Big data

In today's smart world where industries are adopting different ways to progress with the current technology, Big data plays an important role in Industry 4.0. Firstly, let's have a look at what exactly is Industrial Revolution

Ever thought about transforming your data into value??

Talking about Industry 4.0 there are always connected sensors embedded in machines, components, and works-in-progress that will transmit real-time data to networked IT systems. These, in turn, apply machine learning and artificial intelligence algorithms to analyze and gain insights from this big data and adjust processes automatically as needed. Once your big data is effectively analyzed using necessary frameworks your breakdowns and unscheduled downtime will be reduced by 25 percent and that adds a significant value to your collected data.

What is Big Data?

A form of data that is huge. On average 50 exabytes of data is generated from a single smartphone every month. In a population of more than 5million the total data being generated is known as big data which is really big on a larger platform.

How this Big Data actually work?

For a Smart check-in check-out management system, back in 2017 Hilton hotel rolled out its mobile key and service technology to its 10 branches within the U.K. Here the customer's details and hospitality experience are stored on a cloud platform indicating the 'VOLUME' of Big data being captured.
The ‘VARIETY’ of this structured data helps in analyzing and monitoring the data for storage and mining purposes.
The rate at which this data is captured and processed to the customers indicates the ‘VELOCITY’ of Big data.
As the customers are able to access their check-in check-out related details, this helps in building the ‘VERACITY’ thereby adding a ‘VALUE’ to this Big data.

Types of Big Data:

Structured data - A fixed format is decided for storing, processing, and retrieving the data. This helps the data to be highly organized and allows seamless accessibility from a database by simple search engine algorithms. For instance, a player table in an ICC database will be structured as the player names, their countries, their scores and the number of matches played.
Unstructured data - Unlike structured data, this type of data does not have a fixed format which makes it difficult and time-consuming to analyze and monitor on a larger platform. Whatever data we humans encounter in our day-to-day lives in the form of text, audio, video, images, etc is a form of unstructured data.
Semi-structured data - This type of data occupies a stack between structured and unstructured data. Still, most of the semi-structured data appear to be unstructured at a glance as it does not contain a traditional database but does contains some organizational properties which make the process somewhat easier to be named semi-structured. For instance, an HTML enforces a certain hierarchy while maintaining a database of a customer's order details with elements like first name, last name, and order id.

Advantages of Big Data:

With the capturing and storing of information the biggest advantage of Big data is its predictive analysis thereby reducing risks and operational efficiencies.
By collecting different forms of data via various social media platforms the Big data analysis tools help the businesses in terms of analysis and give a marketing perspective and help in the generation of sales leads.

If a company can collect this Big data then eventually it is ahead of the 43% of companies that lack in collecting relevant data and in return they have to spend millions of dollars to hash out relevant data from the bulk.
Correct and exact information can always keep you a step ahead. Therefore, if the company has Big data insights it is always ahead of its competitors.

After learning the usefulness of Big data it’s important to know how this data is stored and processed through a platform??

Traditional approach: Firstly, the collected data from various organizations, banks, hospitals are sent to ETL (Extract, Transform, Load) platform. The ETL converts data to a standard format and sends it to the database. Now, this data is seen on the user end and hence the users can analyze and monitor this data. But as this data grows, it becomes very challenging to manage and process this data with the help of this approach.

traditional big data

Software Approach: There is a total of 5 frameworks that are used for Big data storage and processing. They are Hadoop, Spark, Flink, Storm, and Samza. Out of these Hadoop is the all-time classic and one of the top frameworks used today.

HADOOP FRAMEWORK:

Storage - Hadoop uses Hadoop distributed file system to store Big data. If you have a huge file your file will be broken into chunks and its instances will be created which go into different nodes to make sure if one machine fails, your data is stored in another. Hence by this way larger files are distributed and stored into various machines.

Processing - Map reducing technique is used to process the Big data. A lengthy task is broken into smaller tasks, now these smaller tasks are processed by different machines. Hence the process becomes easy and fast which is also called parallel processing

hadoop2

PARALLEL PROCESSING

With exabytes of data, the responsibility of managing that data becomes a huge task but with necessary software frameworks and parallel processing, it is possible to capture, store and analyze these huge chunks.