What Is Big Data and Why Is It Important?

Overview of Big Data

  • Big Data definition: For simple understanding, Big Data has been defined as massive data and is under consistent growth with time.
  • The examples for Big Data analytics include stock exchanges, jet engines, social media sites, etc.
  • Big Data is divided into three types: 1) Structured, 2) Unstructured, 3) Semi-structured
  • The significant characteristics of Big Data are Volume, Variety, Velocity, and Variability
  • The significant advantages of Big Data are Improved customer service, better operational efficiency, and Better Decision

Definition of Big Data

Big Data is a massive amalgamation of data, which is growing progressively in time despite its volume. It is essential to mention that no traditional data management tool can work on its great size and importance.

More and more companies are swathing to the use of Big Data these days to outperform their peers. There are plenty of industries where both the likes of existing competitors and new entrants employ similar tactics to compete, innovate and capture value.

Examples of Big Data

Here’s a breakdown of notable examples for Big Data.

  • The New York Stock Exchange (NYSE) collects about a terabyte of new trade data and information daily.
  • The industrial analysis shows that a rough 500+ terabytes of new data are routinely ingested into social media databases such as Facebook. It’s essential to mention that uh voluminous data primarily contain a photo and video uploads, putting comments, message exchanges n much more.
  • One jet engine can amass 10+ terabytes of data in just thirty minutes of flight time. Because thousands of flights travel per day, they can collectively gather many petabytes of data.

Types of Big Data

Here’s a breakdown of the three types of Big Data:

  • Structured

It’s essential to mention that any particular data that allows the user to store, access, and process within a structured format is typically termed ‘structured’ data. Computer science’s rapid progression has managed to achieve successful developing techniques for working in the processing of Big Data over time. However, the modern-day industry experts and observers are experiencing issues when a Big Data size grows out of bounds, their general size being in the range of multiple zettabytes.

  • Unstructured

There’s no denying that any data that is unformed or without any structure should be considered unstructured data. Furthermore, apart from the size of the data being vast, it also the non-structural formation brings various challenges against the processing. One fine example of unstructured data is a heterogeneous data source, which comes with a combination of text files, videos, images, etc.

  • Semi-Structured

Arguably the best of both, semi-structured data combines the best of both data forms. Even though the user can identify semi-structured data as a structured form, it’s not defined with a table definition in relational DBMS, per se.

Characteristics of Big Data

Here’s a detailed breakdown of the significant characteristics of Big Data.

  • Volume
  • Variety
  • Velocity
  • Variability

Volume

It’s essential to highlight that the name Big Data means enormous. Naturally, the data size holds a key role in identifying the value out of the data. The data volume virtually determines if a particular data can be considered Big Data or not. Hence, it’s only safe to state that ‘Volume’ is a significant characteristic that demands consideration when dealing with Big Data.

Variety

The following important characteristic of Big Data is its variety. It refers directly to heterogeneous sources and the nature of data- both structured and unstructured. It’s essential to mention that only spreadsheets and databases were deemed fit data sources by most of the applications back in the earlier days. But in the modern era, any analysis of applications will always involve data in the form of emails, monitoring devices, photos, videos, PDFs. Unstructured data can often cause various unstructured data- posed issues for storage, mining and analyzing data.

Velocity

When it comes to discussing the characteristic of Big Data, velocity always pops into the mind. Here, the term “velocity” means the generational speed of data. Fast data processing and generational demands can effectively help in understanding the true potential of the data. Big Data’s velocity helps deal with the data speed from sources like business processes, networks, application logs, social media sites, sensors, Mobile devices, etc. There’s no denying that the data flow is vast and forever running.

Variability

Finally, variability is another characteristic that refers to the inconsistency that is sometimes available in data, effectively hampering handling and managing the data effectively.

Benefits of Big Data Processing

Here’s a detailed breakdown of the significant benefits that Big Data promises.

  • Businesses Can Utilize Outside Intelligence While Taking Decisions

Exposure to social media data from social media sites and search engines like Facebook and Twitter empowers online business outfits to improve their business strategies.

  • Improved Customer Service

Big Data technologies are paving ways for new systems to replace the more traditional customer feedback systems effectively. It’s essential to mention that these new systems will feature Big Data and natural language processing technologies to process consumer responses.

  • Early Identification of Risk to the Product/Services, If Any
  • Better Operational Efficiency

Lastly, Big Data technologies hold the potential for building a staging area or landing zone for new data before determining the necessary data for a location shift to the data warehouse. Additionally, it is only noteworthy to mention that any integration of Big Data technologies with a data warehouse effectively benefits an organization to drop unnecessary data.…

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What is Data Science?

We all know that this is the time of big data. And from the processing of this data to the storage of this data, all are very important for us. Before the introduction of different frameworks like Hadoop, data storage was a big concern. However, it has been resolved now with the help of different types of frameworks. Data Science is somewhere responsible and helpful for both storage and processing of the data. You will see many people around, who are trying to become a Data Scientist. They are also trying to gain knowledge related to different types of frameworks related to Data Science. But before one start learning about data science or its framework, they should try to know about the data science and why it is required or why it is important. Here, we will read about all this.

Data Science

To discover the raw data’s hidden patterns, different types of machine learning principles, algorithms, and different tools are used. This blend of all of these algorithms and principles which is used for this purpose is known as Data Science. Data Science uses different techniques like machine learning, prescriptive analytics, and predictive causal analytics to make predictions and decisions. A data scientist needs to analyze and look at the data from different angles.

For helping the organization make better decisions, the data scientists try to uncover and gather all the findings from the data that is present in the repository of the organization. To find out the meaningful trends, insights, and inferences, from the unstructured raw data, data science is required. The information and findings that are gathered are then processed using business skills and analytical skills, and programming. In the past few years, the data science has evolved and it continues to evolve to provide organizations with success using proper information, better predictions, and right decisions. Earlier, the need for a data scientist was not discovered and no one took it very seriously. But slowly and gradually with the time, the organizations started realizing that they do have the need for the data science skilled professionals who can handle the big amount of data and can organize it properly. There is no organization which is not working with facts & figures or data. To mine or to interpret or to analyse the required information from the complex data is what data science is all about.

Importance of Data Science in the organizations and businesses

In this competitive world, where most of the organizations are giving tough competition to each other, it is very important to make use of Data Science. The organizations need better predictions and better decisions, and for that, a data scientist is very important to evaluate and analyse the organization’s data. There are different ways in which data science is important for organizations and businesses. Let’s know the importance of Data Science.

Helps in defining the organization’s goals

As a data scientist, one tries to examine the data of the organization. After examining the data properly, the data scientists recommend certain actions and steps which are important for the organization. Based on the data trend, the data scientists help in defining the goals of the organization for helping the organization to improve its performance and profitability. When we say an organization’s goals, we did not just mean the one certain goal. But we also mean that it helps various departments by letting them know their individual goals also which will contribute to the profitability of the organization.

Helps to identify the opportunities

The data scientists do not just examine the data. But they also question the assumptions and the processes that are being used for the development of the organization. Be it the questioning about the development of the analytical algorithms or any other such tools or methods, the data scientists check them carefully. They try to find out new opportunities and ways to improve organizational value.

Helps in finding the right talent

We know that recruiting the new talents is the job of the recruiters. But data scientists also help in recruiting by checking the data available about the candidate on the website. They gather information through corporate databases, social media sites, and job portals. They work on this data to find out the best talent for the organization.…

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3 types of Data Formats Explained

Data appears in different sizes and shapes, it can be numerical data, text, multimedia, research data, or a few other types of data. The data format is said to be a kind of format which is used for coding the data. The data is coded in different ways. It is being coded, so that it can be read, recognized, and used by the different applications and programs.

In the information technology, the data format may be referred in different ways. It can be termed as the data type, which is a constraint in the type system which was positioned after the interpretation of the data. It is also termed as a file format which is being used for storing the encoding data in a computer file. Or it can also be termed as Content Format, where the media data is represented in the particular format, that is a video format and audio format.

When it comes to choosing a data format, there are several things which one need to check like the characteristics of the data or the size of the data, infrastructure of the projects, and the use case scenarios. Certain tests are performed in order to choose the right data format by checking the speed of writing and reading the data file. Mainly there are three main types of data formats which are also called as GIS Data formats. All of these data formats are handled in a different way. They are being used for different purposes. The three data formats are: 

  • File-Based Data Format
  • Directory-Based Data Format
  • Database Connections 

Below, we have explained these three types of data formats:

File-Based Data Format – This type of data format includes either one file or more than one file. These files are then stored in any of the arbitrary folders. In most of the cases, it uses the single file only for example DGN. But then there are cases, which even includes at least three files. The filename extension of all these three files is different from each other. That is SHX, SHP, and DBF. All three files are important and are required here. As different tasks are performed by all these three files internally. One uses the filename as the name of the data source. There are many layers present in it, and it is not possible to know about them just with the help of the filename. Like in shapefile, there is only one data source for every shapefile. And there is only one layer, which is named similarly as the name of the file. Some of the examples of file-based data format are Microstation Design Files, Shapefiles, and GeoTIFF images.

Directory-Based Data Format – In this type of data format, whether there is one file or there is more than one file, they are all stored in the parent folder in a particular manner. There are some cases where the requirement of an additional folder is there in the file tree in some other location so that it can be accessed easily. It is a possibility that data source is the directory itself. There are many files present in the directory, which are represented at the available data’s layers. For example, the Polygon Data is represented by the PAL.ADF. As there is more than one file in the folder with the ADF file extension which is included in the ESRI ArcInfo Coverages. The ADF file extension includes the line string data or the arc string data. All the ADF files serve as a layer which is present in the data source inside the folder. Some of the examples of Directory-Based Data Format are US Census TIGER and ESRI ArcInfo Coverages.

Database Connections – In one respect, the database connections are quite similar to the above-mentioned data formats that are file and directory-based data format. For interpreting, for MapServer, they give geographic coordinate data. One need to access the coordinates inside the MapServer, that are creating the vector datasets. The stream of coordinates that are provided by the database connections is stored temporarily in the memory. The MapServer then reads these coordinates for making the map. Coordinate Data is the most important part and most of the focus is on it only. However, one may also require tabular data and attributes. The database connection generally consists of the following information like Host that is the server’s direction, Database Name, the Username and Passwords, Geographic Column name, and the table name or the view name. A few examples of Database Connections are MySQL, ESRI, PostGIS, and ArcSDE.

Benefits of data format types

With data format types being in place, it becomes easy for the user to carry out multiple operations and make the most of it. some of the benefits of having data format types has been listed below:

  • Calculations: Calculations have never been easy before the introduction of data format types. With these formats, all you have to do is punch in the values and within no time all the calculation is done and at your disposal.
  • Formatted: The data if kept well formatted and organized is presentable and understandable by the users. Thus individuals referring to such data can make the most of it. if a user has to make a similar presentation at different points of time, they can simply pick up a format and keep using it for drafting presentations.
  • Consistency: Data types helps the user to have variable that is consistent throughout the program. So you can simply rely on the variable to make presentations, or calculations.
  • Readable: The data is readable and accessible to users all the time without any hassle. Hence any job can be done at the earliest with maximum output produced.

Conclusion

From the facts mentioned above, it is well evident to the users that what are different data format types, its benefits and how these can be used for producing more efficiency and results. Companies are leaning on data more and more in order to improve sales …

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