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Developer(s) | Apache Software Foundation |
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Initial release | June 27, 2014 [1] |
Stable release | 1.37.0 (May 6, 2024 [2] ) [±] |
Repository | Calcite Repository |
Written in | Java |
Operating system | Cross-platform |
Type | SQL database |
License | Apache License 2.0 |
Website | calcite |
Apache Calcite is an open source framework for building databases and data management systems. It includes a SQL parser, an API for building expressions in relational algebra, and a query planning engine. As a framework, Calcite does not store its own data or metadata, but instead allows external data and metadata to be accessed by means of plug-ins.
Several other Apache projects use Calcite. [3] Hive uses Calcite for cost-based query optimization; [4] Drill and Kylin use Calcite for SQL parsing and optimization; Samza and Storm use Calcite for streaming SQL. As of August 2016 [update] , Apex, Phoenix and Flink have projects under development that use Calcite.
iBATIS is a persistence framework which automates the mapping between SQL databases and objects in Java, .NET, and Ruby on Rails. In Java, the objects are POJOs. The mappings are decoupled from the application logic by packaging the SQL statements in XML configuration files. The result is a significant reduction in the amount of code that a developer needs to access a relational database using lower level APIs like JDBC and ODBC.
An entity–attribute–value model (EAV) is a data model optimized for the space-efficient storage of sparse—or ad-hoc—property or data values, intended for situations where runtime usage patterns are arbitrary, subject to user variation, or otherwise unforeseeable using a fixed design. The use-case targets applications which offer a large or rich system of defined property types, which are in turn appropriate to a wide set of entities, but where typically only a small, specific selection of these are instantiated for a given entity. Therefore, this type of data model relates to the mathematical notion of a sparse matrix. EAV is also known as object–attribute–value model, vertical database model, and open schema.
Apache Jena is an open source Semantic Web framework for Java. It provides an API to extract data from and write to RDF graphs. The graphs are represented as an abstract "model". A model can be sourced with data from files, databases, URLs or a combination of these. A model can also be queried through SPARQL 1.1.
Entity Framework (EF) is an open source object–relational mapping (ORM) framework for ADO.NET. It was originally shipped as an integral part of .NET Framework, however starting with Entity Framework version 6.0 it has been delivered separately from the .NET Framework.
Greenplum is a big data technology based on MPP architecture and the Postgres open source database technology. The technology was created by a company of the same name headquartered in San Mateo, California around 2005. Greenplum was acquired by EMC Corporation in July 2010.
Apache Empire-db is a Java library that provides a high level object-oriented API for accessing relational database management systems (RDBMS) through JDBC. Apache Empire-db is open source and provided under the Apache License 2.0 from the Apache Software Foundation.
LucidDB is an open-source database purpose-built to power data warehouses, OLAP servers and business intelligence systems. According to the product website, its architecture is based on column-store, bitmap indexing, hash join/aggregation, and page-level multiversioning.
Apache Hive is a data warehouse software project. It is built on top of Apache Hadoop for providing data query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data.
Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016.
Apache Impala is an open source massively parallel processing (MPP) SQL query engine for data stored in a computer cluster running Apache Hadoop. Impala has been described as the open-source equivalent of Google F1, which inspired its development in 2012.
Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since.
Apache Flink is an open-source, unified stream-processing and batch-processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming data-flow engine written in Java and Scala. Flink executes arbitrary dataflow programs in a data-parallel and pipelined manner. Flink's pipelined runtime system enables the execution of bulk/batch and stream processing programs. Furthermore, Flink's runtime supports the execution of iterative algorithms natively.
Presto is a distributed query engine for big data using the SQL query language. Its architecture allows users to query data sources such as Hadoop, Cassandra, Kafka, AWS S3, Alluxio, MySQL, MongoDB and Teradata, and allows use of multiple data sources within a query. Presto is community-driven open-source software released under the Apache License.
Apache Kylin is an open source distributed analytics engine designed to provide a SQL interface and multi-dimensional analysis (OLAP) on Hadoop and Alluxio supporting extremely large datasets.
Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. It is similar to RCFile and ORC, the other columnar-storage file formats in Hadoop, and is compatible with most of the data processing frameworks around Hadoop. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
Apache ORC is a free and open-source column-oriented data storage format. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is used by most of the data processing frameworks Apache Spark, Apache Hive, Apache Flink and Apache Hadoop.
Apache CarbonData is a free and open-source column-oriented data storage format of the Apache Hadoop ecosystem. It is similar to the other columnar-storage file formats available in Hadoop namely RCFile and ORC. It is compatible with most of the data processing frameworks in the Hadoop environment. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk.
Trino is an open-source distributed SQL query engine designed to query large data sets distributed over one or more heterogeneous data sources. Trino can query data lakes that contain open column-oriented data file formats like ORC or Parquet residing on different storage systems like HDFS, AWS S3, Google Cloud Storage, or Azure Blob Storage using the Hive and Iceberg table formats. Trino also has the ability to run federated queries that query tables in different data sources such as MySQL, PostgreSQL, Cassandra, Kafka, MongoDB and Elasticsearch. Trino is released under the Apache License.
Apache IoTDB is a column-oriented open-source, time-series database (TSDB) management system written in Java. It has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and TSDB with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, downsampling and sub-sequence similarity search. The name IoTDB comes from Internet of Things (IoT) Database, which means it was designed as an IoT-native TSDB that resolves the pain points of the typical IoT scenarios, including massive data generation, high frequency sampling, out-of-order data, specific analytics requirements, high costs of storage and operation & maintenance, low computational power of IoT devices.