Hadoop - Official website - Source code - Document - Download address - Score()

Hadoop is an open-source distributed computing framework used to process large datasets. It is based on Google's MapReduce algorithm and Google File System (GFS), and offers high scalability and fault tolerance. Hadoop consists of a master node and multiple worker nodes, where users can store Data in the Hadoop Distributed File System (HDFS) and process it using the MapReduce model. Hadoop also offers rich APIs and tools such as Pig, Hive, and Spark, enabling users to perform more advanced Data analytics and processing.

Spark - Official website - Source code - Document - Download address - Score()

Spark is a large-scale Data processing engine that uses memory computing technology to speed up Data processing and is one of the most popular tools in the Hadoop ecosystem. Spark supports a variety of Data processing tasks, including batch processing, stream processing, machine learning, and graphics processing. The advantage of Spark is that it can reduce disk I/O and network transports through efficient memory computing, thereby processing larger Data sizes. Spark's programming model is flexible, easy to use, and supports multiple languages, databases, and Data sources.

Flink - Official website - Source code - Document - Download address - Score()

Flink is an open-source stream processing framework developed by the Apache Software Foundation. It provides a unified API and a powerful engine that supports various Data processing scenarios, including batch processing, stream processing, graph processing, and machine learning. Flink leverages stream processing technology to seamlessly integrate real-time Data processing and batch processing, bringing together the best of both worlds. Flink is characterized by high reliability, scalability, and performance, and supports multiple cluster environments such as Hadoop, Kubernetes, and Mesos.

Apache Storm - Official website - Source code - Document - Download address - Score()

Apache Storm is an open-source distributed real-time computation system for processing stream data. It provides highly reliable real-time processing with high scalability and the ability to handle high throughput data. Storm uses "topologies" as a computing model, dividing computing tasks into nodes with defined input and output streams, which can be either Spouts (data sources) or Bolts (data processors). Storm offers rich APIs and an extensible architecture that supports Data processing, real-time computation, and machine learning in a variety of scenarios.

HBase - Official website - Source code - Document - Download address - Score()

HBase is an open-source, distributed database built on top of HDFS (Hadoop Distributed File System) of the Apache Hadoop project. Unlike relational databases, HBase uses a column family storage model and can support billions of rows of records and hundreds of column families. HBase can store, query and process large-scale unstructured data, such as web applications, new media, and IoT. HBase is designed to support horizontal scalability and can handle large-scale Data storage and concurrent access. HBase provides Hadoop's MapReduce interface and supports efficient Data processing and analysis.

Hive - Official website - Source code - Document - Download address - Score()

Hive is a Data warehouse tool based on Hadoop, which can map structured Data files to a database table and provide SQL-like query capabilities. Hive supports various Data types, Data formats, and processing methods, including complex Data types (such as arrays and structures), batch Data import and export, complex query support, and built-in aggregate functions. The query language of Hive is similar to SQL and is called HQL (Hive Query Language), which can be used to query and analyze large-scale data. Hive can also be integrated with other tools through ODBC and JDBC interfaces.

Cassandra - Official website - Source code - Document - Download address - Score()

Cassandra is a highly scalable, distributed NoSQL database developed and open-sourced by Facebook. Cassandra supports high availability, fault tolerance, and distributed Data storage, and can be used in public cloud, private cloud or hybrid cloud environments. Cassandra uses a SQL-like language called CQL (Cassandra Query Language) for querying and managing data, while also supporting features such as transactions, indexing, partitioning, replication, and Data backup. Cassandra is suitable for large-scale distributed Data storage and processing scenarios, such as IoT and real-time Data analysis.