Explains when and why a team should run transactional workloads on MariaDB instead of Google BigQuery.
MariaDB is row-oriented and designed for high-frequency inserts, updates, and deletes. BigQuery optimizes for large, read-only analytics. For order processing, cart updates, and user sessions, MariaDB delivers sub-millisecond writes where BigQuery incurs seconds-long batch latency.
Teams needing on-premise, hybrid, or multi-cloud can install MariaDB anywhere: bare metal, VMs, Docker, or Kubernetes. BigQuery is cloud-locked to GCP, adding vendor dependence and possible compliance roadblocks.
BigQuery bills by data scanned and storage tiering, making unpredictable spikes expensive. MariaDB’s instance-based pricing (cloud) or self-hosted model gives fixed costs, ideal for constant OLTP traffic.
MariaDB provides ACID transactions, clustered indexes, and replication for microsecond-level queries on small row sets. BigQuery shines at petabyte-scale aggregations but lags on single-row lookups.
Use normalized tables—Customers, Orders, Products, OrderItems—with foreign keys. Transactions ensure an order write touches every table atomically, something BigQuery cannot guarantee.
MariaDB follows MySQL syntax—AUTO_INCREMENT keys, LIMIT for pagination, and ENGINE options. BigQuery uses ARRAYs, STRUCTs, and LIMIT/OFFSET but forces fully qualified project.dataset.table names.
Export BigQuery tables to Cloud Storage as CSV or Parquet, download, then LOAD DATA INFILE
into MariaDB. Verify types—TIMESTAMP to DATETIME and NUMERIC to DECIMAL.
Enable slow query log, use InnoDB with proper buffer pool, replicate to read replicas, and back up with mariabackup
. Schedule index maintenance and keep auto-increment ids within INT ranges.
Choose BigQuery for ad-hoc analytics over terabytes, federated queries on GCS, and ML integrations. Keep MariaDB for OLTP and stream data into BigQuery for analytics using Dataflow or Fivetran.
Yes, MariaDB Server is GPL-licensed and community driven. Enterprise add-ons exist but the core engine remains open source.
Yes. Use BigQuery External Connections or schedule Dataflow jobs to replicate MariaDB tables into BigQuery for analytics.
Not natively. You scale MariaDB by vertical resizing, read replicas, or sharding with tools like Spider or Vitess.