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Hadoop > Learn Hadoop > What is the difference between HBASE and HDFS in Hadoop...
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What is the difference between HBASE and HDFS in Hadoop?

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HDFS is for scalable storage of large files; HBase is a NoSQL database for real-time, random access to data.
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HBase and HDFS (Hadoop Distributed File System) are both components of the Hadoop ecosystem, but they serve different purposes: HDFS: 1. _Distributed File System_: HDFS is a scalable, fault-tolerant storage system for big data. 2. _Block-level storage_: HDFS stores data in fixed-size blocks (typically...
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HBase and HDFS (Hadoop Distributed File System) are both components of the Hadoop ecosystem, but they serve different purposes: HDFS: 1. _Distributed File System_: HDFS is a scalable, fault-tolerant storage system for big data. 2. _Block-level storage_: HDFS stores data in fixed-size blocks (typically 64 MB or 128 MB). 3. _Schema-less_: HDFS does not impose a schema on stored data. 4. _Batch processing_: HDFS is optimized for batch processing and high-throughput data access. HBase: 1. _NoSQL Database_: HBase is a distributed, column-oriented NoSQL database built on top of HDFS. 2. _Key-value storage_: HBase stores data as key-value pairs, with support for columns and versioning. 3. _Schema-flexible_: HBase has a flexible schema, allowing for dynamic addition of columns. 4. _Real-time access_: HBase is optimized for real-time, random read/write access to data. Key differences: - Storage model: HDFS is a file system, while HBase is a database. - Data access: HDFS is optimized for batch processing, while HBase is optimized for real-time access. - Schema: HDFS has no schema, while HBase has a flexible schema. When to use: - HDFS: For storing large amounts of raw, unprocessed data, or for batch processing and data warehousing. - HBase: For real-time data access, key-value storage, and flexible schema requirements, such as in IoT, financial, or social media applications. read less
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