- About Us
MariaDB Enterprise Server is an enhanced, hardened and secured version of MariaDB Community Server created to provide customers with enterprise reliability, stability and long-term support as well as greater operational efficiency when it comes to managing large database deployments for business- and mission-critical applications.
Improvements to auditing, backups, clustering, federation, replication and storage allow DBAs to fully secure databases and avoid downtime.
Further testing and enterprise documentation incorporates fixes and workarounds to bugs and issues found in MariaDB Community Server.
The default configuration maximizes security and durability to ensure production deployments are safe, and all non-GA plugins are removed.
Scale out with distributed SQL using fully distributed tables and queries to increase throughput and/or storage capacity.
Collect detailed connection information and create granular audit rules and templates using JSON documents stored in system tables.
Take backups on large databases without blocking writes and interrupting applications, or blocking important schema changes.
Deploy secure multi-master clusters with encrypted transaction buffers and default configurations optimized for production.
Consolidate data access by using MariaDB Platform to access tables in other databases using standard ODBC connections.
Manage encryption keys outside of the database for maximum security and easier, more sophisticated key management.
MariaDB MaxScale is an advanced database proxy for MariaDB Enterprise Server, providing it with enterprise high availability, scalability, security and integration services while at the same time abstracting away the underlying database infrastructure to simplify application development and database administration.
Intelligent query routing and adaptive load balancing simplify application development and improve query performance.
Automatic failover with transaction replay ensures applications are not interrupted, or even aware of, an infrastructure failure.
Redis can be used to cache query results to improve performance while Kafka can be used to publish data changes to external systems.
The database firewall, dynamic data masking and query throttling features protect databases from attacks and prevent data breaches.
MariaDB ColumnStore extends MariaDB Enterprise Server with distributed, columnar storage and massively parallel processing (MPP), transforming it into a standalone or distributed data warehouse for interactive, ad hoc analytics on billions of rows without the need to create indexes – and with standard SQL.
Data is stored in a columnar format for fast analytics and with up to 90% compression, reducing disk I/O and size of data on disk.
Data can be distributed across multiple database instances and queried in parallel to increase query performance and support massive datasets.
Data can be stored on Amazon S3 compatible object storage, on premises or in the cloud, to benefit from low-cost, unlimited storage.
Analyze billions of rows and hundreds of terabytes of data with standard SQL, including joins with other tables (row or columnar).
Use Kafka and Spark connectors to ingest streaming data and publish machine learning results for interactive, ad hoc analysis.
CLI import tools as well as C, Java and Python connectors bypass the SQL layer to load data directly to storage from anywhere.
Xpand extends MariaDB Enterprise Server with distributed data and transaction processing, transforming it into a distributed SQL database capable of scaling to millions of transactions per second with a shared-nothing architecture. However, Xpand is not all or nothing. DBAs can choose to use both replicated and distributed tables.
Xpand distributes both table data and primary/secondary indexes across all database instances to maximize query parallelization.
Xpand uses distributed transactions with parallel writes, multi-version concurrency control (MVCC) and consensus to enforce strong consistency.
MariaDB Enterprise Server instances with Xpand can be added or removed on demand to scale out or scale in as workloads change.