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What is the differences between RDBMS Data Warehouse and big data Hadoop?

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RDBMS Data Warehouses and Hadoop address data storage and processing needs, but differ in key aspects. RDBMS Data Warehouses are structured, optimized for specific queries, and often used for business intelligence, while Hadoop excels at handling massive, diverse datasets, including unstructured data,...
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RDBMS Data Warehouses and Hadoop address data storage and processing needs, but differ in key aspects. RDBMS Data Warehouses are structured, optimized for specific queries, and often used for business intelligence, while Hadoop excels at handling massive, diverse datasets, including unstructured data, and is better suited for large-scale analytics. read less
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RDBMS vs Data Warehouse vs Big Data (Hadoop): Summary: RDBMS: Best for transactions and small structured data. Data Warehouse: Best for analytical queries on structured data. Hadoop: Best for huge, diverse data with scalable, parallel processing.
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RDBMS Data Warehouses and Big Data Hadoop serve different purposes. RDBMS Data Warehouses, built on RDBMS, are optimized for storing and analyzing structured data, often in a centralized repository for business intelligence. They excel at handling transactional data and generating reports. Hadoop, on...
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RDBMS Data Warehouses and Big Data Hadoop serve different purposes. RDBMS Data Warehouses, built on RDBMS, are optimized for storing and analyzing structured data, often in a centralized repository for business intelligence. They excel at handling transactional data and generating reports. Hadoop, on the other hand, is a distributed file system and processing framework designed for handling massive, diverse, and often unstructured datasets. It focuses on large-scale data analytics and storage. read less
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