* Standards-Compliant ODBC: ODBC 3.8 compliant native protocol based drivers. * Unicode, 32-bit and 64-bit support: Support 32-bit and 64-bit applications and unicode ODBC APIs. * Certified Compatibility: Our drivers undergo extensive testing and are certified to be compatible with leading analytics and reporting applications like Tableau, Microsoft Excel, and many more. * ODBC Remoting: Our exclusive remoting feature allows hosting the ODBC connection on a server to enable connections from various clients on any platform (Java, .NET, C++, PHP, Python, etc.), using any standards based technology (ODBC, JDBC, etc.). ODBC Remoting is enabled using the popular MySQL wire protocol server. * Replication and Caching: Our replication and caching commands make it easy to copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, etc. The replication commands include many features that allow for intelligent incremental updates to cached data. * String, Date, Numeric SQL Functions: The driver includes a library of 50 plus functions that can manipulate column values into the desired result. Popular examples include Regex, JSON, and XML processing functions. * Collaborative Query Processing: Our drivers enhance the data source's capabilities by additional client-side processing, when needed, to enable analytic summaries of data such as SUM, AVG, MAX, MIN, etc. * Easily Customizable and Configurable: The data model exposed by our ODBC Drivers can easily be customized to add or remove tables/columns, change data types, etc. without requiring a new build. These customizations are supported at runtime using human-readable schema files that are easy to edit. * Secure Connectivity: Includes standard Enterprise-class security features such as TLS/ SSL data encryption for all client-server communications.

* An industrial strength ORM, built from the core on the identity map, unit of work, and data mapper patterns. These patterns allow transparent persistence of objects using a declarative configuration system. Domain models can be constructed and manipulated naturally, and changes are synchronized with the current transaction automatically. * A relationally-oriented query system, exposing the full range of SQL’s capabilities explicitly, including joins, subqueries, correlation, and most everything else, in terms of the object model. Writing queries with the ORM uses the same techniques of relational composition you use when writing SQL. While you can drop into literal SQL at any time, it’s virtually never needed. * A comprehensive and flexible system of eager loading for related collections and objects. Collections are cached within a session, and can be loaded on individual access, all at once using joins, or by query per collection across the full result set. * A Core SQL construction system and DBAPI interaction layer. The SQLAlchemy Core is separate from the ORM and is a full database abstraction layer in its own right, and includes an extensible Python-based SQL expression language, schema metadata, connection pooling, type coercion, and custom types. * All primary and foreign key constraints are assumed to be composite and natural. Surrogate integer primary keys are of course still the norm, but SQLAlchemy never assumes or hardcodes to this model.