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Hadoop > Learn Hadoop > What is the difference between big data and Hadoop?
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What is the difference between big data and Hadoop?

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Hadoop is used mainly to solve three types of components: HDFS to store, YARN for resource management, and MapReduce for parallel processing. Big Data is used in many businesses across all sectors, including IT, the retail industry, banking and finance, healthcare, transportation, telecommunication,...
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Hadoop is used mainly to solve three types of components: HDFS to store, YARN for resource management, and MapReduce for parallel processing. Big Data is used in many businesses across all sectors, including IT, the retail industry, banking and finance, healthcare, transportation, telecommunication, etc.

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Hadoop is used mainly to solve three types of components: HDFS to store, YARN for resource management, and MapReduce for parallel processing. Big Data is used in many businesses across all sectors, including IT, the retail industry, banking and finance, healthcare, transportation, telecommunication,...
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Here’s the difference between big data and Hadoop: ### 1. **Definition**: - **Big Data**: Refers to extremely large datasets that are difficult to process using traditional data processing tools. It encompasses the three V's: Volume, Velocity, and Variety. - **Hadoop**: An open-source framework...
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Here’s the difference between big data and Hadoop: ### 1. **Definition**: - **Big Data**: Refers to extremely large datasets that are difficult to process using traditional data processing tools. It encompasses the three V's: Volume, Velocity, and Variety. - **Hadoop**: An open-source framework designed to store and process big data across distributed computing environments. ### 2. **Scope**: - **Big Data**: A broad concept that includes various technologies, tools, and methodologies for handling large datasets. - **Hadoop**: A specific technology within the big data ecosystem, focusing on storage (HDFS) and processing (MapReduce). ### 3. **Components**: - **Big Data**: Includes various tools and technologies (like Spark, NoSQL databases, data warehouses, etc.) to manage and analyze large datasets. - **Hadoop**: Consists primarily of HDFS, MapReduce, and YARN, with its own ecosystem of tools (like Hive and Pig). ### 4. **Use Cases**: - **Big Data**: Applicable in various fields such as healthcare, finance, marketing, and social media for analytics and insights. - **Hadoop**: Specifically used for batch processing, data warehousing, and ETL processes in big data scenarios. ### Summary: Big data is the overarching concept of managing large datasets, while Hadoop is a specific framework designed to handle those datasets effectively. read less
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