Introduction to Big Data

Big Data Tutorials

With the fruition of the online services through the extensive use of the Internet, the habits taken up by businesses, stock markets, economies, and by different organizations of governments. This has eventually changed the way people live and use technology. With the increase in all of these, there is a parallel increase in information flows and the collection of data that arises daily, which is more than ever.

Such outbreaks of data are relatively new. This is because now, each user and organization can store the information in digital form. So, for handling these exponential increases of data, there should be some mechanism and approach. Big Data is one way to handle such. In this lesson, you will learn about what is Big Data? Its importance and its contribution to large-scale data handling.

What is Big Data?

Big data can be defined as a concept used to describe a large volume of data, which are both structured as well as unstructured, and that gets increased day by day by any system or business. However, it is not the quantity of data, which is essential. The important part is what any firm or organization can do with the data matters a lot. Analysis can be performed on big data for insight and predictions, which can lead to a better decision and reliable strategy in business moves.

What Is the 3Vs of Big Data?

This conception theory gained thrust in the early 2000s when trade and business analyst Mr. Doug Laney expressed the mainstream explanation of the keyword big data over the pillars of 3v's:

  • Volume: Organizations and firms gather as well as pull together different data from different sources, which includes business transactions and data, data from social media, login data as well as information from the sensor as well as machine-to-machine data. Earlier, this storage of data would have been an issue - but because of the advent of new technologies for handling extensive data with tools like Apache Spark, Hadoop, the burden of enormous data got decreased.
  • Velocity: Data is now streaming at an exceptional speed, which has to be dealt with suitably. Sensors, smart metering, user data as well as RFID tags are lashing the need for dealing with an inundation of data in near real-time.
  • Variety: The releases of data from various systems have diverse types and formats. They range from structured to unstructured, numeric data of traditional databases to non-numeric or text documents, emails, audios and videos, stock ticker data, login data, Blockchains' encrypted data or even financial transactions.

Importance of Big Data

Big Data does not take care of how much data is there, but how it can be used. Data can be taken from various sources for analyzing it and finding answers which enable:

  • Reduction in cost.
  • Time reductions.
  • New product development with optimized offers.
  • Well-groomed decision making.

When you merge big data with high-powered data analytics, it is possible to achieve business-related tasks like:

  • Real-time determination of core causes of failures, problem, or faults.
  • Produce token and coupons as per the customer's buying behavior.
  • Risk-management can be done in minutes by calculating risk portfolios.
  • Detection of deceptive behavior before its influence.

Things That Comes Under Big Data (Examples of Big Data)

As you know, the concept of big data is a clustered management of different forms of data generated by various devices (Android, iOS, etc.), applications (music apps, web apps, game apps, etc) or actions (searching through SE, navigating through similar types of web pages, etc.). Here is the list of some commonly found fields of data that come under the umbrella of Big Data:

  • Black Box Data: Black box data is a type of data that is collected from private and government helicopters, airplanes, and jets. This data includes the capture of Flight Crew Sounds, separate recording of the microphone as well as earphones, etc.
  • Stock Exchange Data: Stock exchange data includes various data prepared about 'purchase' and 'selling' of different raw and well-made decisions.
  • Social Media Data: This type of data contains information about social media activities that include posts submitted by millions of people all over the world.
  • Transport Data: Transport data includes vehicle models, capacity, distance (from source to destination) as well as the availability of different vehicles.
  • Search Engine Data: Retrieve a wide variety of unprocessed information, that is stored in SE databases.

There are various other types of data that gets generated in bulk amount from applications and organizations.

Types of Big Data (Types of Data Handled by Big Data)

The data generated in bulk amount with high velocity can be categorized as:

  1. Structured Data: These are relational data.
  2. Semi-structured Data: example: XML, JSON data.
  3. Unstructured Data: Data of different formats: document files, multimedia files, images, backup files, etc.

Big Data Technologies

This technology is significant for presenting a more precise analysis that leads the business analyst to highly accurate decision-making; ensuing more considerable operational efficiencies, by reducing costs, and trade risks. Now to implement such analytics and holding such a wide variety of data, one must need an infrastructure which can facilitate and manage as well as process huge data volumes in real-time. This way, big data is classified into two subcategories:

  • Operational Big Data: comprises of data on systems such as MongoDB, Apache Cassandra, or CouchDB which offer equipped capabilities in real-time for large data operations.
  • Analytical Big Data: comprises of systems such as MapReduce, BigQuery, Apache Spark, or Massively Parallel Processing (MPP) database which offer analytical competence to process complex analysis on large datasets.

Challenges of Big Data

  • Rapid Data Growth: The growth velocity at such a high rate is creating a problem to look for insights using it. There no 100% efficient way to filter out relevant data.
  • Storage: Generation of such a massive amount of data needs space for storage and organizations are facing challenges to handle such extensive data without suitable tools and technologies.
  • Unreliable Data: It cannot be guaranteed that the big data collected and analyzed are totally (100%) accurate. Redundant data, contradicting data, or incomplete data are challenges that remain within it.
  • Data Security: Firms and organizations storing such massive data (of users) can be a target of cybercriminals, and there is a risk of data getting stolen. Hence, encrypting such colossal data is also a challenge for firms and organizations.

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