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Guilherme Mazui

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What is the Difference Between Big Data and Hadoop?

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Big Data and Hadoop are related but distinct concepts. Here are the main differences between them:

  1. Definition: Big Data refers to a large volume of both structured and unstructured data, while Hadoop is a framework designed to handle and process this large volume of data.
  2. Significance: Big Data has no significance until it is processed and utilized to generate revenue, while Hadoop is a tool that makes Big Data more meaningful by processing the data.
  3. Storage: It is very difficult to store Big Data because it comes in structured and unstructured forms, while Apache Hadoop HDFS is capable of storing Big Data.
  4. Accessibility: Accessing Big Data is difficult, but the Hadoop framework allows for fast access and processing of data compared to other tools.
  5. Developers: Big Data developers focus on developing applications in Pig, Hive, Spark, MapReduce, etc., while Hadoop developers are responsible for coding to process the data.
  6. Type: Big Data is a problem that has no meaning or value until it is processed, while Hadoop is a solution that solves the complex processing of vast amounts of data.
  7. Speed: The speed of Big Data is slow, especially when compared to Hadoop, which can process data faster.

In summary, Big Data refers to the large volumes of data that need to be processed and analyzed, while Hadoop is a framework that provides a solution for handling and processing this vast amount of data.

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  • What is the Difference Between Big Data and Hadoop?
  • Comparative Table: Big Data vs Hadoop

    • Comparative Table: Big Data vs Hadoop

    Here is a table comparing the differences between Big Data and Hadoop:

    Feature Big Data Hadoop
    Definition Big Data refers to the large, complex data sets that are difficult to process using traditional data processing tools and techniques. Hadoop is an open-source framework designed to store and process large data sets in a distributed computing environment.
    Scope Big Data encompasses a wide range of applications in fields such as telecommunications, banking, and healthcare. Hadoop is primarily used for cluster resource management, parallel processing, and data storage.
    Data Storage Big Data utilizes various data storage methods, including data warehouses and data lakes. Hadoop implements the Hadoop Distributed File System (HDFS) for storing various types of data.
    Data Processing Big Data processing involves discovering patterns and trends and making decisions related to human behavior and other factors. Hadoop consists of three components: HDFS, MapReduce, and YARN, which work together to process large data sets.
    Table Creation In Hive (a popular component of Hadoop), there are two ways to create tables: Managed Tables and External Tables. In Hive, the CREATE TABLE statement is used to create a managed table, while the CREATE EXTERNAL TABLE statement is used to create an external table.

    In summary, Big Data refers to the large and complex data sets that require specialized processing techniques, while Hadoop is a framework designed to store and process such large data sets in a distributed computing environment.

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    Guilherme Mazui

    Guilherme Mazui is graduated in journalism from the Federal University of Minas Gerais (UFMG) and a master's degree in Communication from the University of São Paulo (USP). In addition, he has experience in advertising writing and has worked as a content editor in several companies.