Hadoop Storage Types Guide

Hadoop is an open-source framework for processing large-scale data, offering various storage types for storing data. When selecting the appropriate storage type, factors to consider include data access patterns, data size, consistency, and reliability requirements. Here we introduce some common Hadoop storage types and their use cases.

1. HDFS (Hadoop Distributed File System): HDFS, the default storage type in Hadoop, is designed for storing large-scale data files. It divides large files into multiple blocks and stores them across multiple nodes for increased reliability and fault tolerance. HDFS is ideal for scenarios involving batch processing of large-scale data, such as log analysis and data mining.
2. HBase is a distributed columnar storage database in the Hadoop ecosystem, ideal for storing large amounts of structured data. It offers fast random read and write capabilities, along with support for real-time data access. HBase is suitable for scenarios that require real-time querying and analyzing massive data sets, such as real-time monitoring systems and online ad placements.
3. Hive is a data warehousing tool for Hadoop that offers a SQL-like query language for querying and analyzing data stored on HDFS. It is ideal for scenarios requiring complex queries and analysis, allowing users to easily manipulate data using SQL statements.
4. Spark is a fast general-purpose data processing engine that can perform data calculations in memory. It supports various types of data storage such as HDFS, HBase, S3, and is ideal for scenarios requiring high-performance computing and real-time processing, such as machine learning and graph processing.

In addition to the mentioned types of storage, there are also other storage engines that can be integrated with Hadoop, such as Cassandra, MongoDB, and others. When choosing a storage type, it is necessary to consider specific business requirements and data characteristics in order to achieve optimal storage and processing efficiency.