No-code pipeline · Google Cloud Storage → BigQuery

Send data from Google Cloud Storage to BigQuery

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

Kondado provides a direct integration between Google Cloud Storage and BigQuery, replicating CSV file metadata on a configurable schedule to keep your cloud data warehouse updated without manual intervention.

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Send Google Cloud Storage Data to BigQuery

To send Google Cloud Storage data to BigQuery, start by connecting your Google Cloud Storage account to Kondado and selecting the CSV pipeline that tracks your file metadata. Configure BigQuery as your destination and set your preferred replication schedule, whether every 5 minutes, 15 minutes, hourly, or daily. Kondado automatically extracts file information including basenames, paths, and modification timestamps, then loads this structured data into your BigQuery dataset for immediate analysis and reporting.

Once your data lands in BigQuery, you can combine file tracking information with other business data to monitor storage usage, analyze file modification patterns, or trigger downstream workflows based on file arrival events. This automated pipeline eliminates the need for manual file tracking scripts while ensuring your analytics environment always has current metadata about your cloud storage assets.

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The CSV pipeline captures essential file metadata from your Google Cloud Storage buckets, including __file_basename, __file_path, and __kdd_insert_time fields that enable precise tracking of when files were added or modified. In BigQuery, this data becomes the foundation for building custom monitoring systems that track document workflows, audit file changes across departments, or automate inventory management for data lakes. You can join this file tracking information with CRM data, transaction records, or marketing analytics to create comprehensive Looker Studio dashboards that visualize storage patterns alongside business performance metrics.

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Replicated to BigQuery

Google Cloud Storage data available for BigQuery

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

available pipeline

extractable fields

BigQuery

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 BigQuery →

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How to send Google Cloud Storage data to BigQuery

Sync data automatically — no code, no manual exports.

Connect Google Cloud Storage

Authenticate your Google Cloud Storage account in Kondado and grant access to the specific buckets containing your CSV files. Select the CSV pipeline to begin extracting file metadata including basenames and paths.

Configure BigQuery Destination

Set up BigQuery as your destination by specifying the target dataset and project where the file tracking data should land. Verify your credentials have write permissions to create and update the necessary datasets.

Select Data and Schedule

Choose which buckets and file prefixes to monitor, then set your replication schedule to run every 5 minutes, hourly, or daily based on your analysis needs. Activate the pipeline to begin automated replication of file metadata to your data warehouse.

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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

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Frequently Asked Questions (FAQ)

Answers about sending Google Cloud Storage data to BigQuery automatically

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

Kondado connects directly to your specified buckets and automatically detects CSV files, extracting metadata such as filenames, paths, and timestamps. This information is then structured and loaded into your BigQuery dataset on your chosen schedule, creating a queryable history of file activity without requiring manual exports or custom scripts.

What specific file metadata fields are available in the CSV pipeline? ▼

The pipeline includes eight fields such as __file_basename for the filename, __file_path for the full storage location, and __kdd_insert_time marking when Kondado processed the file. These fields enable precise tracking of file modifications, allowing you to identify when specific documents were added or updated in your storage buckets.

How often can I schedule updates from Google Cloud Storage to BigQuery? ▼

You can configure replication to run every 5 minutes, 15 minutes, hourly, or daily depending on your workflow requirements. This configurable schedule ensures your BigQuery dataset reflects recent file activity with the frequency that matches your business needs, whether for near-real-time monitoring or daily batch reporting.

What data format does the file information arrive in within BigQuery? ▼

The replicated data arrives as structured records in BigQuery, with each row representing a file and columns corresponding to metadata fields like path and modification time. This format allows you to write standard SQL queries to filter files by date ranges, specific buckets, or naming patterns directly within your data warehouse.

Can I combine Google Cloud Storage file tracking data with other data sources in BigQuery? ▼

Yes, once your file metadata is in BigQuery, you can join it with data from other sources such as PostgreSQL, MySQL, or Amazon S3 that you also replicate through Kondado. This enables comprehensive analysis that correlates file uploads with transaction records, customer activities, or system events stored in the same data warehouse.

Do I need to manually upload files or does Kondado detect new files automatically? ▼

Kondado automatically scans your configured buckets and detects new or modified CSV files based on your selected schedule. Once detected, the file metadata is extracted and replicated to BigQuery without requiring manual intervention or trigger configurations on your part.

Can I use this pipeline to monitor specific folders or buckets only? ▼

Yes, during setup you can specify which buckets and optional path prefixes Kondado should monitor, allowing you to focus on specific directories or file patterns. This targeted approach ensures you only replicate relevant file metadata to BigQuery, keeping your datasets focused and query performance optimized.

How do I query file modification history once data is in BigQuery? ▼

You can query the __kdd_insert_time and file path fields using standard SQL to track when files were processed and identify changes over time. These queries can be visualized in Looker Studio or Power BI to create timelines of file activity and monitor storage patterns across your organization.

Try out all the features for free for 14 days

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