The solution to BigData Bubble

The most valuable commodity I know of is information

What is big data?

“Big data” is a high volume, speed, and various information assets that require inexpensive costs, new methods of information processing advanced understanding and decision making.

  • It mentions a large number of details that eventually grow in size over time.
  • It is so huge in size that it cannot be processed or analyzed using standard data processing techniques.
  • It includes data mining, data storage, data analysis, data sharing, and data identification.
  • This term refers to all data, data structures, and tools and techniques used to process and analyze data.

Information is the oil of the 21st century, and analytics is the combustion engine


History of big data?

Although the concept of big data itself is relatively new, the origins of big data sets go back to the 1960s and 70s when the world of data was just beginning with the first data centres and the creation of database databases.
About 2005, people began to notice that data users were producing Facebook, YouTube, and other online services. Hadoop (an open-source framework designed specifically to store and analyze large data sets) was developed in the same year. NoSQL also began to gain popularity during this time.

So what are types of Big Data?

  • Structured

Structured is one of the largest forms of data and With structured data, we mean data that can be processed, stored, and available in a modified format. Highly organized data can be stored easily and seamlessly and retrieve from a database with simple search engine algorithms. For example, an employee table in the company database will be organized as job details, their job positions, their salaries, etc., will be present in an orderly manner.

  • Unstructured

Unstructured data or Informal data refers to data that does not contain any specific type of structure. This makes it very difficult and time-consuming to process and analyze random data. Email is an example of random data. Systematic and random are two important types of big data.

  • Semi-structured

Semi-structured is part of the third type of big data. Incomplete data is concerned with data containing both of the above formats, i.e., structured and unstructured data. Specifically, refers to data that although not yet categorized under a specific database, but contains important information or tags that distinguish individual items within the data. So we come to the end of the data types. Let’s talk about data features.

Processed data is information. The processed information is knowledge, Processed knowledge is Wisdom

So what is the use of big data and who uses it?

The amount of data available to companies is growing rapidly. With the increase in volume, variability, and reliability of data, conventional analysis techniques are no longer in the picture. That’s where Big Data jumps. Big Data analytics allows the analysis of this huge amount of data to provide insight that was not previously understood. All of our online activities are the sites we visit, the posts we like, the things we share, the purchases we make, the videos we watch almost everything is recorded, reviewed and analyzed. With this large amount of data comes many benefits as well as difficulties. All industries are trying to take advantage of the opportunities offered by this data. By the time we acquired the use of Big Data, many industries have advanced for miles from their competitors. The use of Big Data differs between theory and practice. In theory, what we thought would come to pass but we are moving forward. Here we summarize the list of big data usage that can be included across the industry.

A company like Facebook, Google, etc. It uses the data we create every day to earn money by filtering our data and extracting useful information from it.

So what is the problem while storing bigdata?

As we all know, there is data, much of it: historical data, of course, but also new data generated from social media apps, click-through data from web applications, IoT sensor data, and so on. The amount of data is greater than ever before, coming in ever-increasing amounts, and many different ways

Two major problems are encountered during bigdata storage:

  • Data Volume: People are more connected than ever before, and this integration leads to more data sources, leading to more data than ever before (and ever-growing). The increased volume of data requires increasing computing power to obtain value (meaning) from data. Traditional computer systems do not work in the amount of data that accumulates today.
  • Data velocity: TThe speed and direction at which data enters a business increases due to connectivity and advances in network technology, so it enters faster than we can comprehend. And as data flows faster and different sources vary, it becomes harder to get value (meaning) from data. Traditional computer systems do not work on data coming at today’s speed.

The world is one big data problem

So what the solution for Big Data?

Most companies use a concept called Shared Storage to address this issue

Nowadays, a wide set of programs and applications, especially for high computer performance, depend on distributed areas to process and analyze large amounts of data. As we know, the amount of data is increasing dramatically, and the goal of providing and developing high-performance, reliable and reliable storage solutions has become one of the major computer problems. The storage solution used by large data systems is Distributed File Systems (DFS), where DFS is used to create a consistent and integrated view of multiple file servers and network sharing. In this paper, we will present Hadoop Distributed File System (HDFS) as DFS in big data systems and introduce Event-B as an official model of modelling, where Event-B is a mature and widely used method for many industrial projects in many domains, such as automotive, transportation, space, business details, etc…

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