No-code pipeline · Google Cloud Storage → MySQL

Send data from Google Cloud Storage to MySQL

Get started for free

No credit card required | 14 days | 10 million records | 30 pipelines

or sign up with your email

By signing up, you agree to Kondado’s Terms of service and Privacy policy

From Google Cloud Storage to MySQL: managed, scheduled, no code.

Kondado replicates CSV file metadata from Google Cloud Storage to MySQL on a configurable schedule, automatically tracking file basenames, paths, and modification timestamps while loading structured data into your database for immediate SQL analysis.

Connect Google Cloud Storage now →

Send Google Cloud Storage Data to MySQL Automatically

Kondado provides a direct integration between Google Cloud Storage and MySQL, allowing you to replicate file metadata and contents without writing custom scripts or managing complex ETL processes. Simply connect your Google Cloud Storage account as a data source, configure your MySQL destination, and select the CSV pipeline to start automated data transfers immediately. The platform handles file detection, schema mapping, and scheduled updates based on your specific business requirements, running on a configurable schedule that you control.

Once your data arrives in MySQL, you can query file inventories, track data lake changes over time, and combine storage metrics with business data from other sources to build comprehensive analytics workflows. This automated pipeline eliminates manual CSV imports and ensures your database always reflects the current state of your cloud storage assets, enabling faster decision-making and operational efficiency.

Our prices start from $19 USD/month, and you can try Kondado for free for 14 days with no credit card required

Available Data Pipelines

The CSV pipeline captures comprehensive file metadata from your Google Cloud Storage buckets, including __file_basename for file identification, __file_path for location tracking, and __kdd_insert_time for modification monitoring. With this information flowing into MySQL, you can build automated inventory systems that track data lake changes, monitor file arrival patterns, and create custom reports showing storage utilization trends over time. Combine these storage insights with business metrics from Power BI, Looker Studio, or Google Sheets to visualize how your cloud storage assets correlate with operational performance and data processing workflows.

Try out all the features for free for 14 days

Replicated to MySQL

Google Cloud Storage data available for MySQL

Tables Kondado writes into your MySQL, on a schedule you control.

available pipeline

extractable fields

MySQL

Destination

Available integrations

CSV

Table includes information about CSV files, featuring fields such as __file_basename, __file_path, and __kdd_insert_time, enabling tracking of the modification date and value of each file.

See all available data → Send Google Cloud Storage to MySQL →

Try out all the features for free for 14 days

How to send Google Cloud Storage data to MySQL

Sync data automatically — no code, no manual exports.

Connect Google Cloud Storage Account

Authenticate your Google Cloud Storage buckets by providing access credentials through Kondado's interface, selecting the specific project and storage locations containing your CSV files.

Configure MySQL Destination

Enter your MySQL connection details including host, port, database name, and credentials to establish the target location where your Google Cloud Storage metadata will reside.

Select CSV Pipeline and Schedule

Choose the CSV pipeline from the available options and set your preferred update frequency, selecting from intervals such as every 5 minutes, hourly, or daily to match your data freshness requirements.

Try out all the features for free for 14 days

Hundreds of data-driven companies trust Kondado

Why Kondado?

Focus on extracting value from your data and leave the rest to us

Wherever you need

With the same platform, you send data to AI (Claude/ChatGPT via MCP), reports, spreadsheets, databases, data warehouses, and data lakes. ETL and ELT

Learn More About Our Platform →

From wherever you need

Data from over 80 sources for Marketing, Ecommerce, ERPs, Customer Service, Finance, Databases, and much more

Learn More About Our Data Sources →

Predictable pricing

No surprise invoices, pick a plan and change it anytime

Check out our plans →

Send data from Google Cloud Storage to other destinations

Google Cloud Storage > Looker Studio

Google Cloud Storage > Power BI

Other visualizations

Google Cloud Storage > Amazon S3

Google Cloud Storage > BigQuery

Google Cloud Storage > Excel

Google Cloud Storage > Google Sheets

Google Cloud Storage > MySQL

Google Cloud Storage > PostgreSQL

Google Cloud Storage > Redshift

Google Cloud Storage > SQL Server

Choose a tool to visualize your Google Cloud Storage data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Google Cloud Storage > Airtable

Google Cloud Storage > Alteryx

Google Cloud Storage > Amazon Quicksight

Google Cloud Storage > BIMachine

Google Cloud Storage > Cortex

Google Cloud Storage > Data Studio

Google Cloud Storage > Geckoboard

Google Cloud Storage > Grafana

Google Cloud Storage > IBM Cognos Analytics

Google Cloud Storage > Klipfolio

Google Cloud Storage > Looker Studio

Google Cloud Storage > Looker

Google Cloud Storage > Looqbox

Google Cloud Storage > Metabase

Google Cloud Storage > Microstrategy

Google Cloud Storage > Pentaho

Google Cloud Storage > Power BI

Google Cloud Storage > Qlik Cloud Analytics

Google Cloud Storage > Qlik Sense

Google Cloud Storage > Qlik

Google Cloud Storage > QlikView

Google Cloud Storage > Redash

Google Cloud Storage > Superset

Google Cloud Storage > Tableau

Google Cloud Storage > BI TOTVS

Google Cloud Storage > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending Google Cloud Storage data to MySQL automatically

How does Kondado replicate Google Cloud Storage files to MySQL? ▼

Kondado connects directly to your Google Cloud Storage buckets and detects CSV files automatically, extracting metadata such as filenames, paths, and timestamps. The platform loads this structured data into your MySQL database on your chosen schedule, handling schema creation and updates without requiring manual intervention or custom coding.

What specific data fields are available from Google Cloud Storage CSV files? ▼

The CSV pipeline includes eight fields such as __file_basename for the filename, __file_path for the full directory location, and __kdd_insert_time for tracking when records were processed. These fields enable you to monitor file locations, track data arrival times, and maintain complete visibility into your storage bucket contents within MySQL.

How often can I update my MySQL database with new Google Cloud Storage data? ▼

You can configure updates to run every 5 minutes, 15 minutes, hourly, or daily depending on your data freshness requirements and workflow needs. This flexible scheduling ensures your MySQL tables reflect the latest storage state without overwhelming your system with unnecessary processing.

Can I combine Google Cloud Storage data with other sources in MySQL? ▼

Yes, once your storage metadata resides in MySQL, you can join it with data from Google Cloud Storage and other platforms like BigQuery or PostgreSQL using standard SQL joins. This capability allows you to correlate file arrival patterns with business events, creating unified reports that span multiple data sources.

What format does the data take when it arrives in MySQL? ▼

The replicated data arrives as structured relational tables with clearly defined columns corresponding to the pipeline fields, ready for immediate SQL querying. Each row represents a file from your Google Cloud Storage bucket, with standard data types that support indexing, aggregation, and complex analytical queries.

Do I need to manually create tables in MySQL for the Google Cloud Storage data? ▼

No, Kondado automatically generates the appropriate table schema in MySQL when you activate the pipeline, mapping Google Cloud Storage fields to compatible column types. As your storage contents change, the schema adapts to accommodate new file patterns without requiring manual table modifications.

Can I track file modifications and deletions in MySQL using this pipeline? ▼

The pipeline captures file metadata including timestamps that indicate when files were processed, allowing you to identify new and changed files in your storage buckets. By comparing current data against historical records in MySQL, you can build queries that detect modifications, track file lifecycle events, and monitor data lake evolution over time.

How do I query the replicated Google Cloud Storage data in MySQL? ▼

Once replicated, you can query your storage data using standard SQL statements through any MySQL client, BI tool, or application connected to your database. Join the storage metadata with operational data to create custom dashboards in Looker Studio or Power BI, or export results to Google Sheets for sharing with stakeholders.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days