MariaDB to BigQuery: 2 Reliable Methods for Seamless Data Replication

Got data in MariaDB but need the power of BigQuery to analyze it? You’re not alone. MariaDB works well for storing day-to-day information, but when it comes to crunching massive datasets and running lightning-fast queries, BigQuery takes the crown. That’s why moving your data from MariaDB to BigQuery can be such a game changer.

In this guide, we’ll break down what both tools do and then walk you through how to get your data from MariaDB into BigQuery, step by step. By the end, you’ll know exactly how to set things up so your data is not just stored but also ready for deep analysis and smarter decisions.

Method 1: MariaDB to BigQuery Migration Using Hevo Data

Hevo Data makes it simple to move data from MariaDB to BigQuery without writing any code. Once set up, the pipeline runs automatically and transfers data in real time.

Here’s how you can do it step by step:

Step 1: Set up MariaDB as the Source

1. Log in to Hevo and click Add Source.
   
2. Choose MariaDB.
   
3. Enter your connection details: Host or IP, Port, Database, Username, and Password.
   
4. Choose the ingestion method you prefer, such as CDC (log-based) for near real time or batch for periodic loads.
   
5. Pick the schemas and tables you want to replicate. You can include or exclude objects here.
   
6. Click Test & Run to validate connectivity and start the source configuration. Hevo will verify credentials and fetch a sample.

Step 2: Set up BigQuery as the Destination

1. Click Add Destination and select BigQuery.
   
2. Authenticate with a Google Service Account or your Google account. If using a service account, upload the JSON key.
   
3. Specify Project, Dataset, Location, and any staging bucket if prompted.
   
4. Choose write behavior, for example Append or Overwrite, and set partitioning or clustering if needed.
   
5. Click Test & Run to validate access to BigQuery and confirm Hevo can create or write to tables.

Step 3: Review and Run the Pipeline

1. Hevo creates a pipeline linking your MariaDB source to your BigQuery destination.
   
2. Review table mappings, data types, and optional transformations. Enable auto schema evolution if you want Hevo to handle column changes.
   
3. Click Activate or Run to start the initial load. After the first load, Hevo continues with CDC if you select it.

Step 4: Monitor and Troubleshoot

1. Open the pipeline Overview to watch events, throughput, and lag.
   
2. Check Logs for any errors and use Retry where available.
   
3. Set up alerts so you are notified about failures or schema changes.

Notes that help

• If MariaDB is behind a firewall, whitelist Hevo’s IPs and ensure the MariaDB user has the required privileges.
  
• For BigQuery, the service account should have roles like BigQuery Data Editor and BigQuery Job User.
  
• Start with a small table to validate, then expand to full scope once Test & Run passes.

That’s it! Within a few minutes, your data will be live in BigQuery, and Hevo will continue syncing it automatically without any manual effort.

Method 2: Manual MariaDB to BigQuery Migration (Updated for 2025)

You can move data manually from MariaDB to Google BigQuery by following two main steps: exporting your data as a CSV file and then loading it into BigQuery.

Step 1: Export data from MariaDB into CSV

First, you need to get your data out of MariaDB in a format BigQuery can understand. The most common way is to export it as a CSV file.

You can do this directly from the command line using the mysql client:

mysql -h [INSTANCE_IP] -u [USER_NAME] -p[YOUR_PASSWORD] --database=[DATABASE] \-e "SELECT * FROM mytable" \--batch --quick --skip-column-names | sed 's/\t/,/g' > myfile.csv

Here’s what this command does:

• -h, -u, and -p let you connect to your MariaDB instance.
  
• The -e “SELECT * FROM mytable” part runs your SQL query.
  
• The –batch and –quick options make sure the data is exported efficiently.
  
• The sed command replaces tab characters with commas, giving you a proper CSV file.
  
• Finally, the output is saved to myfile.csv.

At the end of this step, you’ll have your table data in a CSV file on your local machine.

Step 2: Load the CSV file into BigQuery

Now that you have a CSV, the next step is to load it into BigQuery. You can do this using Python and the BigQuery client library.

Here’s an example script:

from google.cloud import bigquery
# Create a BigQuery clientclient = bigquery.Client()
# Replace this with your project, dataset, and table nametable_id = "your-project.your_dataset.your_table_name"
# Configure the load jobjob_config = bigquery.LoadJobConfig(    source_format=bigquery.SourceFormat.CSV,    skip_leading_rows=1,  # Skip the header row    autodetect=True       # Let BigQuery figure out the schema)
# Open the CSV file and load it into BigQuerywith open("myfile.csv", "rb") as source_file:    job = client.load_table_from_file(source_file, table_id, job_config=job_config)
# Wait for the load to completejob.result()
# Print confirmationtable = client.get_table(table_id)print(f"Loaded {table.num_rows} rows and {len(table.schema)} columns to {table_id}")

Here’s what this script does:

• It creates a BigQuery client to connect to your Google Cloud project.
  
• It sets up the job configuration, telling BigQuery to expect a CSV file.
  
• It opens your exported CSV and uploads it into the specified BigQuery table.
  
• It waits until the job is complete and then confirms how many rows and columns were loaded.

When the script finishes, your MariaDB data will be available inside BigQuery, ready for analysis.

Curious about other migration paths? Here’s more:

• MariaDB to Databricks

• MariaDB to Redshift

• MariaDB to Snowflake

• Oracle to MariaDB

Limitations of Manual MariaDB to BigQuery Migration

• Consistency challenges: BigQuery doesn’t always guarantee consistent results if data is changing during a query. This can lead to unpredictable outputs. On top of that, you’ll usually need to write extra scripts to clean and transform your data before loading it.
• No real-time updates: BigQuery doesn’t automatically sync changes from MariaDB. If your database is frequently updated, keeping BigQuery in sync becomes tough. You could try using Change Data Capture (CDC), but setting it up is often complicated and not very efficient.
  
• Row and file size limits: While BigQuery handles very large datasets (up to 5 TB per CSV and 15 TB per load job), it has strict limits at the row level. Each row or cell can’t exceed 100 MB, which can cause issues if you have large text fields or JSON data.
  

Introduction to MariaDB

MariaDB is a popular Open-source Relational Database Management System (RDBMS). It was developed as a software fork of another popular Open-source database, MySQL, by the developers who played key roles in developing the original database. MariaDB was devised in 2009 when MySQL was acquired by Oracle. It was designed to ensure ease of use, speed, and reliability for all its users. 

Like all other Relational Database Management Systems (RDBMS), MariaDB houses support for ACID-Compliant Data Processing. Along with that, it also supports parallel Data Replication, JSON APIs, and multiple storage engines, including InnoDB, Spider, MyRocks, Aria, Cassandra, TokuDB, and MariaDB ColumnStore.

More information on MariaDB can be found here.

Understanding the Key Features of MariaDB

The key features of MariaDB are as follows:

• Robust Transactional Support: Implementation of ACID (Atomicity, Consistency, Isolation, Durability) properties ensures no data loss or inconsistency.
• Ease of Use: Considering that it makes use of SQL for querying data, anyone with basic knowledge of SQL can perform the required tasks with ease.
• Security: It implements a complex data security layer that ensures that only authorized users can access sensitive data.
• Scalable: MariaDB is considered to be highly scalable due to its support for multi-threading. 
• Roll-back Support: MariaDB houses support for roll-backs, commits, and crash recovery for all transactions.
• High Performance: MariaDB houses various fast load utilities along with Table Index Partitioning and Distinct Memory Caches that can help ensure high performance.

Introduction to Google BigQuery

BigQuery is Google’s cloud enterprise data warehouse that primarily serves business agility in running complex SQL queries and performing analysis on huge datasets efficiently. It is based on Google technology called Dremel, using columnar storage and tree architecture to support high-speed scanning of data for querying efficiency.

BigQuery is serverless, highly scalable, and enlists Google’s infrastructure in the cloud for management. It uses access patterns to maintain efficiency in storage by dynamically optimising data structures for the handling of data in a changing environment for optimal performance.

Understanding the Key Features of Google BigQuery

• Fully Managed by Google: All databases or Data Warehouses built on Google BigQuery are deployed, maintained, and managed directly by Google. 
• Integrations: Google BigQuery, being a part of the Google Cloud Platform (GCP) supports seamless integration with all Google products and services. Google also provides a wide variety of integrations with numerous third-party services along with functionality to integrate with the APIs of applications that are not directly supported by Google.
• High Data Processing Speed: Google BigQuery was designed to enable users to perform real-time analysis of their data.
• Serverless: In Google BigQuery’s Serverless model, the processing is automatically distributed over a vast number of machines that are working in parallel. Hence, any business making use of Google BigQuery can focus on gaining insights from data rather than on setting up and maintaining the infrastructure.
• Google BigQuery ML: Google BigQuery houses a functionality called BigQuery Machine Learning (BQML) that gives users the ability to create and execute Machine Learning models using standard SQL queries.

Understanding the Need for MariaDB to BigQuery Migration

 Businesses feel the need to set up MariaDB to BigQuery Migration because of the following reasons:

• Powerful Analytics: There are numerous analytics workloads that businesses can run on a Data Warehouse. Since Google BigQuery houses an SQL engine, businesses can use Business Intelligence tools like Google Data Studio, Looker, and Tableau to create descriptive data visualizations and reports.
• Machine Learning Capabilities: Google BigQuery goes beyond conventional Data Warehousing. It can be used to create robust Machine Learning models to carry out batch predictions without having to export data out of Google BigQuery.
• Simplified Workflows: Google BigQuery by design is meant to encourage customers to focus on gathering insights as opposed to managing infrastructure. This approach allows teams to innovate faster with fewer resources. DBAs are not needed to provision and maintain servers, and this enables them to work with a lean team of Data Analysts.
• Scale-out Architecture: From an architectural point of view, the only limit on speed and scale in Google BigQuery is the amount of hardware in the Availability Zone (AZ). Queries are automatically scaled to thousands of machines and executed in parallel. This is the same infrastructure used on other Google products like AdWords, YouTube, Gmail, G-Suite, Google Search, etc.
• Rich Product Ecosystem: Google BigQuery is part of the GCP ecosystem, and it integrates tightly with Cloud AI, Cloud DLP, AutoML, Data Studio, Cloud Scheduler, Cloud Functions, Cloud Composer, etc.

Learn More:

• MariaDB to Databricks
• MariaDB to Redshift
• MariaDB to Snowflake
• Oracle to MariaDB

Conclusion

In this article, we walked you through a step-by-step guide to migrate data from MariaDB to BigQuery, either manually or using Hevo. However, the manual method works well only if its limitations are not a concern. For example, if you have small datasets, only need a one-time migration, or can tolerate occasional delays, then it can be simple and cost-effective.

On the other hand, if you handle large datasets, need real-time updates, or want continuous synchronization, Hevo makes the process much easier. Moreover, it transfers data from any source to a Data Warehouse, BI tool, or other destinations in a fully automated and secure way. Hence, it reduces errors, handles complex transformations, and ensures reliable data flow.If you want to see it in action, sign up for a 14-day free trial and explore Hevo’s features firsthand. Additionally, we would love to hear about your experience setting up MariaDB to BigQuery integration in the comments below.

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FAQs to load data from MariaDB to BigQuery