How does Kondado replicate Google Cloud Storage files to PostgreSQL?

Kondado connects directly to your Google Cloud Storage buckets and extracts file metadata and content through the available pipelines. The platform loads this information into your PostgreSQL database on a configurable schedule that you define, handling the data transformation and insertion automatically without requiring manual file downloads or import scripts.

What specific data fields are available from the CSV pipeline?

The CSV pipeline includes eight fields such as __file_basename, __file_path, and __kdd_insert_time that track your file names, storage locations, and modification timestamps. These fields enable you to monitor file arrivals, track organizational structures within your buckets, and maintain historical records of storage changes for operational analysis.

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

You can configure replication to run every 5 minutes, 15 minutes, hourly, or daily depending on your business requirements and data freshness needs. This flexible scheduling ensures your PostgreSQL database stays updated with the latest file metadata without overwhelming your system with unnecessary processing.

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

Yes, once your storage metadata resides in PostgreSQL , you can join it with data from other pipelines such as Salesforce, Google Ads, or Google Cloud Storage sources. This enables comprehensive analysis across your entire data ecosystem, allowing you to correlate file uploads with marketing campaigns or sales events.

What format does the data take when it arrives in PostgreSQL?

The replicated data loads as structured relational tables within your PostgreSQL database, with columns corresponding to the pipeline fields like file paths and timestamps. You can query this data using standard SQL, create views for specific file tracking needs, or connect it to Power BI and Looker Studio for visualization.

How do I track file modifications using the replicated data?

The __kdd_insert_time field captures when each record was added to your database, while file path information helps you identify specific storage locations. By querying these fields in PostgreSQL , you can build monitoring queries that detect new file arrivals, track bucket organization changes, or identify stale files that need archival.

Can I use this data to build dashboards in visualization tools?

Absolutely, you can connect your PostgreSQL database containing Google Cloud Storage metadata to Power BI , Looker Studio , or Google Sheets to create custom monitoring dashboards. These visualizations can display file upload trends, storage utilization patterns, and data pipeline health metrics tailored to your operational needs.

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Google Cloud Storage to PostgreSQL Data Replication

How to send Google Cloud Storage data to PostgreSQL? You can automate the replication of your cloud storage files to your database using Kondado’s no-code platform. Simply connect your Google Cloud Storage bucket as a data source, configure your PostgreSQL destination, and select which file metadata and content you want to replicate. The platform handles the extraction and loading automatically on your chosen schedule, eliminating manual CSV imports and complex scripting while ensuring your data remains current for analysis.

Kondado automatically replicates Google Cloud Storage files to PostgreSQL on a configurable schedule, extracting CSV metadata including file paths, basenames, and insertion timestamps directly into your database without requiring code or manual file handling.

Once your data arrives in PostgreSQL, you can combine it with other business data to build comprehensive analytics workflows. Whether you are tracking file modifications across multiple buckets or analyzing upload patterns, having structured storage metadata in your database enables deeper operational insights and automated reporting capabilities that support data-driven decision making across your organization.

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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. This enables you to track when files were added or modified, monitor storage organization patterns, and analyze file lifecycle events directly within PostgreSQL. You can join this storage metadata with transactional data from other sources to create unified views of your data pipeline health, automate inventory reporting, or trigger downstream processing workflows based on file arrival times. By having granular visibility into your cloud storage contents within your database, you empower analysts to query file histories and build custom monitoring solutions without accessing the storage console directly.

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Google Cloud Storage data available for PostgreSQL

available pipeline

extractable fields

Available integrations

Integration	Description
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.

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.

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How to send Google Cloud Storage data to PostgreSQL

Sync data automatically — no code, no manual exports.

Connect Google Cloud Storage

Authenticate your Google Cloud Storage bucket by providing the necessary access credentials and selecting the specific buckets you want to replicate. Kondado will scan your storage to identify available CSV files and prepare the metadata extraction.

Configure PostgreSQL destination

Enter your PostgreSQL connection details including host, database name, and credentials to establish the destination database. The platform will verify connectivity and prepare the target schema for receiving your Google Cloud Storage file metadata.

Select data and schedule updates

Choose the CSV pipeline and specify which file fields you want to replicate, then set your preferred update frequency from every 5 minutes to daily. Once activated, Kondado will automatically begin loading your storage metadata into PostgreSQL according to your configuration.

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