How to Choose MariaDB over BigQuery in PostgreSQL

Galaxy Glossary

Why choose MariaDB over BigQuery?

Explains when and why a team should run transactional workloads on MariaDB instead of Google BigQuery.

Welcome to the Galaxy, Guardian!
You'll be receiving a confirmation email

Follow us on twitter :)

Oops! Something went wrong while submitting the form.

Description

Example H2

Example H3

Why choose MariaDB over BigQuery for OLTP?

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.

Which deployment scenarios favor MariaDB?

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.

How does cost compare for steady workloads?

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.

What are the performance trade-offs?

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.

How do I model ecommerce data in MariaDB?

Use normalized tables—Customers, Orders, Products, OrderItems—with foreign keys. Transactions ensure an order write touches every table atomically, something BigQuery cannot guarantee.

How is syntax different from BigQuery Standard SQL?

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.

How to migrate small datasets from BigQuery to MariaDB?

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.

Best practices for running MariaDB in production

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.

When should I still use BigQuery?

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.

Why How to Choose MariaDB over BigQuery in PostgreSQL is important

How to Choose MariaDB over BigQuery in PostgreSQL Example Usage


-- Find customers who spent more than $1,000 in lifetime orders
SELECT c.id, c.name, SUM(o.total_amount) AS lifetime_value
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
GROUP BY c.id, c.name
HAVING SUM(o.total_amount) > 1000
ORDER BY lifetime_value DESC;

How to Choose MariaDB over BigQuery in PostgreSQL Syntax


-- Create transactional tables in MariaDB
CREATE TABLE Customers (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(100),
  email VARCHAR(255) UNIQUE,
  created_at DATETIME DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE Orders (
  id INT AUTO_INCREMENT PRIMARY KEY,
  customer_id INT NOT NULL,
  order_date DATETIME DEFAULT CURRENT_TIMESTAMP,
  total_amount DECIMAL(12,2),
  FOREIGN KEY (customer_id) REFERENCES Customers(id)
);

CREATE TABLE Products (
  id INT AUTO_INCREMENT PRIMARY KEY,
  name VARCHAR(100),
  price DECIMAL(10,2),
  stock INT
);

CREATE TABLE OrderItems (
  id INT AUTO_INCREMENT PRIMARY KEY,
  order_id INT NOT NULL,
  product_id INT NOT NULL,
  quantity INT,
  FOREIGN KEY (order_id) REFERENCES Orders(id),
  FOREIGN KEY (product_id) REFERENCES Products(id)
);

-- Load CSV data quickly
'tLOAD DATA INFILE '/tmp/customers.csv' INTO TABLE Customers
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '\n'
(id, name, email, created_at);

-- Transactional insert example
START TRANSACTION;
INSERT INTO Orders (customer_id, total_amount) VALUES (42, 199.99);
INSERT INTO OrderItems (order_id, product_id, quantity) VALUES (LAST_INSERT_ID(), 12, 1);
COMMIT;

Common Mistakes

Treating BigQuery like an OLTP store. Its columnar design and batch inserts make single-row writes slow. Fix by keeping OLTP workloads on MariaDB and exporting analytics data to BigQuery.
Ignoring indexing in MariaDB after migration. Without proper secondary indexes, query latency skyrockets. Always add indexes on foreign keys and frequent filter columns.