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Mondrian is an OLAP (online analytical processing) engine written in Java. It reads from JDBC data sources, aggregates data in a memory cache, and implements the MDX language and the olap4j and XML/A APIs.
LucidDB is a DBMS optimized for business intelligence. Besides architectural innovations such as column-store, it supports many advanced features from SQL:2003, including SQL/MED and user-defined transformations written in Java.
Palo ETL Server is a Java based Tool for Extraction, Transformation and Loading of mass data into the Palo OLAP Server. Palo ETL Server is one part of the Palo Suite.
Deploy in 115+ regions with the modern database for every enterprise.
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
jdbc4olap is a ready-to-use JDBC 3.0 driver that provides access to OLAP databases. Relying on a relational representation of multidimensional environments, it includes a SQL-MDX converter and uses XMLA to dialog with OLAP servers.
Google Web Toolkit-based tool for publishing user-friendly OLAP views, reports and dashboards. Uses Mondrian OLAP and a relational database as the back-end.
jIntersect is an OLAP like Java algorithm that takes a JDBC ResultSet as input and returns a data structure with all possible intersections, combinations and permutations of the data, pre-summarized at multiple levels for quick access.
BIOLAP - OLAP for Genome and Biology Data Sets. This project extends Mondrian OLAP to biology data sets. We provide tools to slice and dice genomic sequence similarities and more easily explore large genomic data sets.
OpenRolap: an implementation of OLAP in a relational database. Conceptually simple, the current version is in MySQL, with a Java API. The API aims to avoid complex MDX-type language, instead to use a simple "filter path" structure.
Firewater is an open source scaleout RDBMS based on column store technology. It allows a cluster of commodity servers or virtualized cloud nodes to be used for fast response times on SQL and OLAP queries over massive datasets.