Transactions
MariaDB Enterprise Platform is the leading production-grade open source database solution for transaction processing – and at any scale, from replicated or clustered deployments for read scaling and high availability. Oracle Database compatibility simplifies migration while temporal tables and JSON functions, along with Node.js, Python and reactive Java connectors make modern application development easier and faster than ever.
Enterprise reliability
Advanced features such as zero-interruption failover and online disaster recovery ensure the database is always available and its data always protected.
Easy development
The database proxy abstracts away the underlying database infrastructure so developers don’t have to worry about handling failures or changes in the topology (planned or not).
Modern SQL
JSON functions, temporal tables, window functions, common table expressions (CTEs) and user-defined aggregate functions are examples of what modern SQL developers can leverage.
Analytics
MariaDB Enterprise Platform can be deployed as a data warehouse or analytics database using its storage engine ColumnStore, for columnar storage and massively parallel processing (MPP) to perform interactive, ad hoc queries on hundreds of billions of rows without creating indexes – and with standard SQL (including joins). Further, MariaDB Enterprise Platform implements a cloud-native storage architecture, and can optionally use Amazon S3 compatible object storage to lower costs and take advantage of unlimited capacity.
Ad hoc analytics
Analyze massive amounts of data in new, unforeseen ways without having to create an index for every potential query.
Real-time visualization
Create interactive data visualizations and customizable or dynamic reports for real-time exploration of massive datasets.
Data marts for Hadoop
Import data from Hadoop data lakes to create fresh data marts on a regular basis for iterative analysis using standard BI/reporting tools.
Smart Transactions
MariaDB Enterprise Platform supports smart transactions (i.e., hybrid transactional/analytical processing or HTAP) by combining row storage optimized for fast transactions with columnar storage optimized for fast analytics. With smart transactions, developers can enrich web and mobile applications with real-time analytics and historical data to create more insightful customer experiences driven by compelling opportunities – and for SaaS customers, to deliver self-service analytics.
Self-service analytics
SaaS providers can now deliver self-service analytics to customers so they can analyze the data generated by the service.
Access to historical data
Provide customers (and app developers) with access to historical data that would otherwise be offloaded to a separate data warehouse.
Real-time insight
Provide customers with current and relevant insight using the latest data rather than waiting for batch updates from offline analytics jobs.
