Yes. This is one of MariaDB’s strongest differentiators. MariaDB’s Exa columnar engine enables true HTAP (Hybrid Transactional/Analytical Processing) within a single database instance. Your OLTP workloads run on InnoDB as normal, while Exa handles analytical queries on the same data using columnar compression optimized for aggregations and full-table scans. On Google Cloud specifically, this means you can reduce reliance on a separate BigQuery pipeline for operational analytics: real-time dashboards query Exa while your application writes transactions to InnoDB, with no ETL lag, no data duplication and no extra GCP services to manage.

MariaDB Cloud on Google Cloud provides enterprise-grade support that competes directly with Oracle and Microsoft: 24/7 access to MariaDB engineering staff (not just a support tier), a 99.99% uptime SLA backed by MaxScale’s sub-10-second automatic failover and guaranteed response times tied to severity. Where Oracle and SQL Server charge substantial per-CPU licensing fees on top of infrastructure, MariaDB Cloud bundles engineering support into a single Marketplace subscription with no separate license cost. For migrations from Oracle or SQL Server, MariaDB’s team provides hands-on schema assessment, compatibility tooling and cutover planning, a level of engagement commercial vendors typically reserve for their largest accounts.

MariaDB Cloud on Google Cloud is purpose-built for the modern AI stack. The pgvector-compatible Vector Store in MariaDB 11.7 lets you store and query high-dimensional embeddings directly in your operational database, enabling retrieval-augmented generation (RAG) without a separate vector database. This integrates naturally with Vertex AI: your application can write embeddings to MariaDB, perform ANN (approximate nearest-neighbor) similarity searches at query time and feed results directly into Gemini or other Vertex AI models. For ML feature stores, Exa’s real-time columnar access means your training pipelines can read fresh features from the same database your application writes to, eliminating the stale-data problem common in batch-based feature pipelines.

MariaDB provides a structured Oracle migration path specifically for Google Cloud deployments. The process starts with a schema assessment that maps Oracle data types, identifies PL/SQL stored procedures with MariaDB-compatible equivalents and flags any compatibility gaps upfront. Data migration uses Google Database Migration Service alongside MariaDB’s CDC-based replication to stream changes live during cutover, keeping downtime to minutes rather than hours. MariaDB’s CONNECT storage engine can also federate queries against your existing Oracle instance during a parallel-run validation period, so you can verify data integrity before cutting traffic. Teams migrating from Oracle typically see 60–75% total cost savings by eliminating per-CPU Oracle licensing fees..