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What is the difference among BigData, Hadoop, Cassandra, Hive?

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Big Data, Hadoop, Cassandra, and Hive are all related to handling and processing large volumes of data, but they serve different purposes and have distinct characteristics: 1. **Big Data**: Big Data refers to the vast volume, variety, and velocity of data that organizations collect and analyze to...
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Big Data, Hadoop, Cassandra, and Hive are all related to handling and processing large volumes of data, but they serve different purposes and have distinct characteristics: 1. **Big Data**: Big Data refers to the vast volume, variety, and velocity of data that organizations collect and analyze to gain insights, make informed decisions, and improve operations. It encompasses the entire ecosystem of tools, technologies, and techniques used to manage, store, process, and analyze large datasets. 2. **Hadoop**: Hadoop is an open-source framework for distributed storage and processing of Big Data. It consists of two main components: the Hadoop Distributed File System (HDFS) for storing data across multiple machines, and MapReduce for processing and analyzing data in parallel. Hadoop is designed to handle large-scale batch processing tasks and is particularly well-suited for processing unstructured or semi-structured data. 3. **Cassandra**: Cassandra is a distributed NoSQL database designed for handling large volumes of data across multiple nodes while providing high availability and scalability. It is optimized for write-heavy workloads and offers linear scalability by distributing data across a cluster of commodity hardware. Cassandra is well-suited for real-time, high-throughput applications that require low-latency access to data. 4. **Hive**: Hive is a data warehouse infrastructure built on top of Hadoop that provides a SQL-like query language called HiveQL for querying and analyzing data stored in Hadoop's HDFS. Hive enables users to perform ad-hoc queries, data summarization, and analysis using familiar SQL syntax, making it easier for non-programmers to work with Big Data. Under the hood, Hive translates HiveQL queries into MapReduce jobs or, more recently, Apache Spark jobs for execution on the Hadoop cluster. In summary: - Big Data is a concept encompassing the handling and processing of large volumes of data. - Hadoop is a distributed storage and processing framework for Big Data, consisting of HDFS and MapReduce. - Cassandra is a distributed NoSQL database optimized for high availability and scalability. - Hive is a data warehouse infrastructure built on Hadoop, providing a SQL-like interface for querying and analyzing data stored in HDFS. read less
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Hadoop is a big data processing framework based on the famous MapReduce programming model. Cassandra is mainly used for real-time data processing. Hadoop supports a variety of formats. Cassandra does not support images.
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Hadoop is a big data processing framework based on the famous MapReduce programming model. Cassandra is mainly used for real-time data processing. Hadoop supports a variety of formats. Cassandra does not support images.
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