What Is Big Data? | A Big Data Tutorial for Beginners

Writer :Angle Marque EG

This Tutorial explains everything you need to know about Big Data Tutorial . What is Big Data? What are its advantages? What are its disadvantages? And what are its applications?

The amount of data being exchanged on a daily basis in today's digital world is measured in Terabytes or Petabytes.

If we're exchanging that much data every day, we'll need a place to keep it. To deal with large amounts of data moving at high speeds and in a wide variety of formats, Big Data is the answer.

It's capable of handling data that comes from a variety of places, including databases, websites, widgets, and so on. Also, it is able to connect and match data from a variety of sources. In fact, it speeds up the retrieval of data (For Example, social media).

What Is Big Data?

The word "huge" is insufficient to describe BigData; certain characteristics distinguish the data as BigData.

There are three main characteristics of BigData, and any data that meets these criteria will be considered BigData. It's the result of combining the three V's listed below:

  • Volume
  • Velocity
  • Variety

Volume: The data should be massive in size. Big Data provides a solution for managing large amounts of data in the Terabyte or Petabyte range. We can easily and effectively perform CRUD (Create, Read, Update, and Delete) operations on BigData.

Velocity: It is in charge of facilitating data access. For instance, today's social media requires a rapid exchange of data in a short amount of time, and BigData is the best solution. As a result, another characteristic is velocity, which is the rate at which data is processed.

Variety: We're dealing with unstructured data in social media, such as audio or video recordings, images, and so on. In addition, various industries, such as banking, require structured and semi-structured data. BigData is the answer to storing both types of data in one location.

Variety refers to the various types of data available, such as structured and unstructured data from various sources.

Structured Data: Structured data is data that has a defined structure or that can be easily stored in a tabular format in relational databases such as Oracle, SQL Server, or MySQL. We can easily and quickly process or analyze it.

The data stored in a Relational Database, which can be managed using SQL, is an example of Structured Data (Structured Query Language). Employee information (name, ID, designation, and salary, for example) can be stored in a tabular format.

We can only perform operations or process unstructured or semi-structured data after it has been formatted or fit into a relational database in a traditional database. ERP, CRM, and other types of structured data are examples.

Semi-Structured Data: Semi-Structured Data refers to information that hasn't been fully formatted. It isn't saved in any database or data tables. However, because this data contains Tags or comma-separated-values, we can easily prepare and process it. XML files, CSV files, and other semi-structured data are examples.

Unstructured Data: Unstructured data is information that lacks structure. There is no pre-defined data model, so it can take any shape. We can't put it in traditional databases because it's too big. It is difficult to find and process it.

In addition, the amount of unstructured data is enormous. Email bodies, audio, video, images, completed documents, and so on are examples of unstructured data.

Challenges Of Traditional Databases

  • Traditional databases do not support a wide range of data types, such as unstructured and semi-structured data.
  • When dealing with large amounts of data, a traditional database is slow.
  • Processing or analyzing a large amount of data in traditional databases is extremely difficult.
  • A traditional database can store data in the terabytes or petabytes range.
  • Historical data and reports cannot be handled by a traditional database.
  • After a certain amount of time, a database clean-up is required.
  • With a traditional database, the cost of maintaining a large amount of data is extremely high.
  • Because full historical data is not kept in a traditional database, data accuracy suffers.

The Advantages of Big Data Over Traditional Databases

  • Big Data is in charge of dealing with, managing, and processing various types of data, including structured, semi-structured, and unstructured data.
  • In terms of maintaining a large amount of data, it is cost-effective. It relies on a distributed database system to function.
  • Using BigData techniques, we can save large amounts of data for a long time. As a result, it's simple to work with historical data and generate accurate reports.
  • Because of the high speed of data processing, social media employs Big Data techniques.
  • Big Data has a lot of advantages, one of which is data accuracy.
  • It enables users to make informed business decisions based on current and historical data.
  • In BigData, error handling, version control, and customer experience are all highly effective.

BigData's Challenges and Risks

Challenges:

  1. Managing large amounts of data is one of the most difficult aspects of Big Data. Nowadays, data is ingested into a system from a variety of sources. As a result, properly managing it is a huge challenge for businesses. To generate a report containing the last 20 years of data, for example, a system must save and maintain the previous 20 years of data. Only relevant data should be entered into the system in order to produce an accurate report. It should not contain any irrelevant or unnecessary data; otherwise, companies will face a significant challenge in maintaining such a large amount of data.
  2. The synchronization of various types of data is another challenge with this technology. As we all know, Big Data supports structured, unstructured, and semi-structured data from a variety of sources, making synchronization and data consistency difficult.
  3. The next issue that businesses are confronted with is a scarcity of experts who can assist them and implement solutions to the problems they are encountering in the system. In this field, there is a significant talent shortage.
  4. Managing the compliance aspect is costly.
  5. The cost of BigData collection, aggregation, storage, analysis, and reporting is enormous. All of these costs should be manageable by the organization.

Risks:

  1. It can handle a wide range of data, but it will produce faulty results if companies do not properly understand requirements and control the data source. As a result, investigating and correcting the results will take a significant amount of time and money.
  2. Another danger associated with BigData is data security. When there is a large amount of data, there is a greater chance that it will be stolen. Hackers may steal and sell sensitive company information (including historical data).
  3. BigData also faces the threat of data privacy. If we want to protect personal and sensitive data from hackers, we must protect it and ensure that it complies with all privacy policies.

Big Data Technologies

The technologies that can be used to manage Big Data are as follows:

  1. Apache Hadoop
  2. Microsoft HDInsight
  3. No SQL
  4. Hive
  5. Sqoop
  6. BigData in Excel

Our upcoming tutorials will provide a detailed description of these technologies.

Tools To Use Big Data Concepts

The following are some open-source tools that can assist with Big Data concepts:

#1) Apache Hadoop

#2) Lumify

#3) Apache Storm

#4) Apache Samoa

#5) Elasticsearch

#6) MongoDB

#7) HPCC System BigData

Applications of Big Data

The domains where it is used are as follows:

  1. Banking
  2. Media and Entertainment
  3. Healthcare Providers
  4. Insurance
  5. Education
  6. Retail
  7. Manufacturing
  8. Government

BigData And Data Warehouse

Before we talk about Hadoop or BigData Testing, we need to understand what a data warehouse is.

Let's look at a real-life example of a Data Warehouse. Consider a company that has branches in three different countries, such as India, Australia, and Japan.

The entire customer data is stored in the Local Database in each branch. These local databases can be traditional RDBMSs like Oracle, MySQL, or SQL Server, and they will store all of the customer data on a daily basis.

The organization now wants to analyze this data on a quarterly, half-yearly, or annual basis for business development. To do so, the organization will collect data from various sources and combine it in a single location, which will be referred to as a "Data Warehouse."

A data warehouse is a type of database that stores all of the data extracted from multiple sources or database types using the "ETL" (Extract, Transform, and Load) process. We can use the data in the Data Warehouse for analytical purposes once it is ready.

As a result, we can generate reports from the data in the Data Warehouse for analysis. Business Intelligence Tools can create a variety of charts and reports.

We need a Data Warehouse for analytical purposes so that we can grow our business and make the best decisions possible.

This process involves three steps. First, we have gathered data from various sources and stored it in a single location known as the Data Warehouse.

We'll use the "ETL" process here, so we'll apply it in Transformation roots while loading data from multiple sources to one location, and then we'll be able to use a variety of ETL tools.

Once the data is in the Data Warehouse, we can use Business Intelligence (BI) Tools, also known as Reporting Tools, to generate various reports to analyze the business data. Reports and DashBoards can be generated using tools like Tableau or Cognos, and data can be analyzed using tools like Tableau or Cognos.

OLTP And OLAP

Before we talk about Hadoop or BigData Testing, we must first understand what a data warehouse is.

Let's look at a real-world example to better understand Data Warehouse. Consider a company that has branches in three countries: India, Australia, and Japan, for example.

The Local Database stores all of the customer data in each branch. These local databases can be traditional RDBMSs such as Oracle, MySQL, or SQL Server, and they will store all of the customer data on a daily basis.

The organization now wants to analyze this data for business development on a quarterly, half-yearly, or annual basis. To accomplish this, the organization will gather data from various sources and combine it in a single location, which will be referred to as a "Data Warehouse."

The "ETL" (Extract, Transform, and Load) process pulls data from various sources or database types into a data warehouse. We can use the data for analytical purposes once it is ready in the Data Warehouse.

So, using the data in the Data Warehouse, we can generate reports for analysis. Business Intelligence Tools allow you to create a variety of charts and reports.

Data Warehouse is required for analytical purposes in order to grow the business and make appropriate organizational decisions.

The first step involves gathering data from various sources and storing it in a Data Warehouse.

We'll use the "ETL" process here, which means we'll apply it in Transformation roots while loading data from multiple sources into one place, and then we'll be able to use various ETL tools.

After the data has been loaded into the Data Warehouse, we can use Business Intelligence (BI) Tools, also known as Reporting Tools, to create various reports to analyze the business data. Reports and DashBoards can be generated using tools like Tableau or Cognos, and data can be analyzed using tools like Tableau and Cognos.

When it comes to BigData, where does it fit in?

BigData is data that is too large for traditional databases to store and process, and it is in a structured or unstructured format to be handled by local RDBMS systems.

This type of data is generated in TeraBytes (TB), PetaBytes (PB), and beyond, and it is growing at a rapid rate. There are a variety of places to get this information, including Facebook and WhatsApp (both of which are related to social networking); Amazon and Flipkart (both of which are related to e-commerce); Gmail, Yahoo, and Rediff (all of which are related to email); and Google and other search engines. We also get bigdata from mobile phones, such as SMS data, call recordings, and call logs, among other things.

Conclusion

Big data is the solution for efficiently and securely handling large amounts of data. It is also in charge of preserving historical data. There are numerous benefits to this technology, which is why every business wants to make the switch to Big Data.


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