What is the difference between HBASE and HDFS in Hadoop?

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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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HDFS is for scalable storage of large files; HBase is a NoSQL database for real-time, random access to data.
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**HDFS (Hadoop Distributed File System):** - **Purpose:** Stores large volumes of data across a distributed cluster. - **Structure:** Provides high-throughput access to data with a write-once, read-many model. - **Data Type:** Handles large files and is optimized for sequential read/write operations. ...
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**HDFS (Hadoop Distributed File System):** - **Purpose:** Stores large volumes of data across a distributed cluster. - **Structure:** Provides high-throughput access to data with a write-once, read-many model. - **Data Type:** Handles large files and is optimized for sequential read/write operations. **HBase:** - **Purpose:** A distributed, scalable NoSQL database that runs on top of HDFS. - **Structure:** Provides random, real-time read/write access to large amounts of sparse data. - **Data Type:** Handles structured data with flexible schemas and supports fast lookups and updates. In summary, HDFS is designed for large-scale data storage, while HBase offers a real-time database layer for structured data with quick access needs. read less
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I want to take online classes on database/ ETL testing.

 

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Both are co-related to each other but compare to DBA Jobs, ETL job is more demanding hence you take class for informatica tools and others.
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