How does the Google Cloud Storage to Amazon S3 replication work?

Kondado connects directly to your Google Cloud Storage buckets and monitors for CSV file changes based on your configured schedule. When new or modified files are detected, the platform extracts metadata including file paths and modification timestamps, then loads this information into your specified Amazon S3 destination. The process maintains the original file structure while adding tracking fields like __kdd_insert_time to help you monitor data freshness.

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

The CSV pipeline replicates eight distinct fields including __file_basename for the original filename, __file_path for the bucket location, and __kdd_insert_time marking when Kondado processed the file. These fields enable precise tracking of file origins and modification history within your data lake. You can use this metadata to build lineage tracking or monitor file arrival patterns in your analytics workflows.

How often can I schedule updates from Google Cloud Storage to Amazon S3?

Kondado offers configurable scheduling options ranging from every 5 minutes to daily intervals, allowing you to balance data freshness with processing costs. You can set different schedules for different pipelines based on how frequently your source files change. Near-real-time updates are available for time-sensitive workflows, while hourly or daily schedules work well for batch processing scenarios.

What file formats does Kondado support when replicating from Google Cloud Storage to S3?

Currently, Kondado supports CSV file replication from Google Cloud Storage to Amazon S3, capturing both the file content and comprehensive metadata. The platform handles CSV files stored in your GCS buckets and transfers them to S3 while preserving the original data structure. This format support enables seamless integration with analytics tools like Athena and Presto that commonly query CSV data in S3.

Can I combine Google Cloud Storage data with other sources once it is in Amazon S3?

Yes, once your Google Cloud Storage data resides in Amazon S3, you can join it with data from other sources such as PostgreSQL, BigQuery, or Google Sheets that you also replicate to Amazon S3. This unified storage approach enables comprehensive analytics across multiple platforms without data silos. You can create consolidated reports that correlate file metadata from GCS with transactional data from databases or marketing metrics from spreadsheets.

How is the data structured when it arrives in Amazon S3 from Google Cloud Storage?

Data arrives in Amazon S3 maintaining the original CSV file structure while Kondado appends system fields for tracking purposes. The files retain their original content and formatting, with additional columns like __file_path and __kdd_insert_time providing operational metadata. This structure allows you to query both the original business data and the ingestion metadata using standard SQL engines like Athena or Presto.

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Send Google Cloud Storage Data to Amazon S3

How to send Google Cloud Storage data to Amazon S3? Start by creating a direct integration between your cloud storage environments using Kondado’s no-code platform. Connect your Google Cloud Storage account as the data source, authenticate your Amazon S3 destination, and choose which file pipelines to replicate. The platform automatically extracts your CSV files and loads them into your S3 buckets according to your business requirements, maintaining file tracking fields throughout the replication process.

Kondado replicates Google Cloud Storage CSV file metadata including __file_basename, __file_path, and __kdd_insert_time to Amazon S3 on a configurable schedule ranging from every 5 minutes to daily, enabling automated data lake ingestion without manual file transfers.

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The CSV pipeline captures comprehensive file information including __file_basename, __file_path, and __kdd_insert_time fields, allowing you to track when each file was processed and where it originated. Once your Google Cloud Storage data lands in Amazon S3, you can leverage the separation of storage and compute to query files directly with Athena, Presto, or Dremio without moving data between services.

This setup enables you to build custom analytics workflows that combine cloud storage metadata with other business data sources. You can create automated reporting pipelines that monitor file arrival patterns, track data lake growth, or trigger downstream processing when specific CSV files appear in your S3 buckets, all while maintaining a complete history of file modifications and ingestion timestamps.

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Google Cloud Storage data available for Amazon S3

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 Amazon S3

Sync data automatically — no code, no manual exports.

Connect Google Cloud Storage

Authenticate your Google Cloud Storage account by providing the necessary credentials and bucket details to establish the data source connection. Select the specific buckets containing your CSV files that you want to replicate to Amazon S3.

Configure Amazon S3

Enter your Amazon S3 destination credentials including the bucket name and region where you want your Google Cloud Storage data to land. Define the folder structure and file organization preferences for your replicated CSV files and metadata.

Select data and schedule

Choose the CSV pipeline from the available options and specify which file fields to include in the replication process. Set your preferred update frequency ranging from 5 minutes to daily intervals to keep your Amazon S3 data lake current without manual intervention.

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