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Sybase IQ is a column-based, relational database software system used for business intelligence, data warehousing, and data marts. Produced by Sybase Inc., an SAP company, its primary function is to analyze large amounts of data in a low-cost, high-availability environment. It can be deployed on Windows, Unix and Linux Operating Systems.[1] Sybase IQ is often credited[2] with pioneering the commercialization of column-store technology. Since SAP AG's acquisition of Sybase in July 2010, Sybase IQ is now part of the broader database technology portfolio.

Sybase IQ is optimized for data warehouse type applications, where data is added to databases but typically not modified much if at all, and the typical access is to search through the data. The column-based storage, which enables good performance for reading through data, somewhat slows down writing data (instead of just the last data object requiring updating with a row object, one data object per table column must be updated). Large volume data imports can still be reasonably efficient. Prior to version 15, Sybase IQ didn't have automatic queues for requests of changing its objects, like other RDBMSes (SQL Server, Oracle, ASE, ASA, MYSQL, DB2, etc.) do. Therefore, a DDL/DML statement may return an error if the target object is in use. Sybase IQ provides explicit lock table statement support that will allow behavior similar to traditional RDBMS, although the locking scheme is less granular.[19] Since version 15, this behavior is configurable. A DDL/DML operation can wait until other active operations on the same object are complete.

To a user, Sybase IQ looks just like any relational DBMS with a SQL-based language layer accessible via ODBC/JDBC drivers. However, inside, Sybase IQ is a column-oriented DBMS, which stores data tables as sections of columns of data rather than as rows of data like most transactional databases.
Column-orientation has a number of advantages.[10] If a search is being done for items matching a particular value in a column of data, only the storage objects corresponding to that data column within the table need to be accessed. A traditional row-based database would have to read the whole table, top to bottom. Another advantage is that when indexed correctly, a value that would have to be stored once in each row of data in a traditional database is stored only once, and a bitwise index is used to access the data.[1]

Additionally, the column-based storage enables IQ to compress data efficiently on the fly; as each column is made up of a number of records of the same data type and size, compression can be very efficient and rapid. [11]
Sybase IQ has a massively parallel processing (MPP) framework based on a shared-everything environment called PlexQ®. Most other products capable of MPP tend to be based on shared-nothing environments. The benefit of shared-everything is that it's more flexible in terms of the variety of queries that can be optimized-especially for balancing the needs of many concurrent users. The downside is that in extreme cases, competition among processors to access a shared pool of storage (usually a storage-area network), can lead to I/O contention, which affects query performance.[12] However, the PlexQ® framework of Sybase IQ allows compute and storage layers to scale out independently of each other and also allows these resources to be provisioned on-demand for better utilization.

In July 2007, at Sun Microsystems' request, InfoSizing verified the population and performance[13] of the world's largest data warehouse implemented in history, consisting of over one petabyte (1,000 terabytes) of structured and unstructured data, designed and implemented by BMMSoft using Solaris 10 OS and Sybase IQ. Sun Microsystems and Sybase claim that the system architecture used in this benchmark is highly efficient, yielding a reduction in equipment and processing needed and thus a reduction in electric energy consumption.[14][15]
Since databases with limited domains (e.g., that contain state names like VA, MD, WV, NJ, etc.) and high row counts are incidentally compressed by storage in column form (as only one example of each value in the domain is stored), those types of databases can be stored using less space than those employing more traditional row storage. Sybase claimed that in the case of the BMMSoft data warehouse, their one petabyte of raw transactional data (or 500 terabytes of transactional and 72 terabytes of multimedia) was represented using only 260 terabytes of storage.
Sybase IQ has an open interface approach towards its ecosystem. Most of the popular commercial and open source business intelligence and data integration tools are certified to work with Sybase IQ. Moreover, now as part of SAP, Sybase IQ is also integrated with SAP's Business Intelligence portfolio of products to form an end-to-end business analytics software stack.
Sybase IQ also supports plugging in external algorithms written in C++ and Java. SQL queries can call these algorithms, allowing for the execution of in-database analytics, which provides better performance and scalability. Vendors such as Fuzzy Logix and Zementis have written pre-built C++ and JAVA modules for this purpose. Additionally, Sybase IQ also provides drivers for access via languages such as PHP, Perl, Python, and Ruby on Rails.