How does Kondado handle CSV files with different column structures from FTP?

The CSV pipeline includes column_number and row_number fields that preserve the positional structure of your source files. This allows BigQuery to ingest files with varying schemas without requiring manual column mapping for each variation. You can then use SQL to normalize or pivot this data according to your analytical needs.

What metadata is available when replicating FTP files to BigQuery?

Each replicated row includes the __file_basename field, which stores the original filename from your FTP server. This metadata enables you to trace data lineage back to specific uploads and filter queries by file source. You can also identify when particular files were processed based on your configured replication schedule.

Can I combine FTP data with other sources in BigQuery?

Yes, once your FTP data resides in BigQuery, you can join it with data from Google Sheets , PostgreSQL , or Power BI exports. This cross-source analysis enables unified reporting that connects file-based exports with live database records. Create comprehensive datasets that blend historical FTP archives with current transactional data.

How often can I schedule FTP to BigQuery updates?

Kondado offers flexible scheduling options ranging from every five minutes to daily intervals, depending on your business requirements. You can configure different frequencies for different file types or directories within the same FTP server. This ensures near-real-time availability for critical files while managing costs for less urgent data.

Do I need to manually map CSV columns when setting up the pipeline?

No, Kondado automatically detects and replicates the structure of your CSV files during the initial connection. The pipeline preserves the original column positions as data enters BigQuery, allowing you to handle schema variations through SQL transformations after replication. This approach eliminates setup complexity for files with changing headers.

How do I query FTP data alongside my existing BigQuery tables?

After replication, your FTP data appears as standard BigQuery datasets that you can reference directly in SQL queries using standard JOIN operations. Reference these tables in Looker Studio reports or analytical workflows just like any other BigQuery source. The seamless connection means no additional syntax is required to blend file-based data with existing warehouse tables.

Send data from FTP to BigQuery

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

Send FTP Data to BigQuery Automatically

Connecting your FTP server to BigQuery enables powerful analytics on file-based data without manual intervention. Kondado provides a direct integration between FTP and BigQuery, allowing you to replicate CSV files and other formats directly into your serverless data warehouse. Configure your data source once, set your preferred update frequency from every five minutes to daily, and watch as your files transform into queryable datasets ready for complex SQL analysis and business intelligence.

Once your FTP data lands in BigQuery, you can combine it with data from Google Sheets, PostgreSQL, or other business applications to create comprehensive reports and unified dashboards. Marketing teams can analyze campaign exports, finance departments can process transaction logs, and operations managers can monitor inventory files, all within BigQuery’s scalable environment that handles large volumes efficiently.

Kondado replicates FTP files to BigQuery on a configurable schedule, automatically parsing CSV data with metadata fields including row_number, column_number, and __file_basename to support files with varying schemas.

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

The CSV pipeline captures every row from your FTP files while preserving essential metadata through fields like __file_basename, row_number, and column_number. This structure enables you to track which source file each record came from and handle files with inconsistent column counts, making it ideal for processing daily exports from legacy systems or third-party vendors that may change formats over time.

With this data in BigQuery, you can build custom dashboards that monitor file arrival patterns, track data quality across uploads, and join CSV contents with transactional databases. Create automated quality checks that flag when expected columns are missing, or build historical archives that maintain complete lineage back to the original FTP filename for audit compliance.

Try out all the features for free for 14 days

FTP data available for BigQuery

available pipeline

extractable fields

Available integrations

Integration	Description
CSV	Table includes fields such as row_number, column_number, and __file_basename, enabling analysis of data from files with varying columns.

CSV

Table includes fields such as row_number, column_number, and __file_basename, enabling analysis of data from files with varying columns.

See all available data →

Try out all the features for free for 14 days

How to send FTP data to BigQuery

Sync data automatically — no code, no manual exports.

Connect Your FTP Server

Enter your FTP server credentials and path details in Kondado to establish the data source connection. Specify the directory containing your CSV files and authentication parameters to enable automated file detection.

Configure BigQuery Destination

Select BigQuery as your destination and provide your Google Cloud project details, dataset name, and service account credentials. Kondado will create the necessary tables to receive your FTP file data.

Select Files and Schedule

Choose which CSV files or directories to replicate and set your preferred update frequency from five minutes to daily intervals. Activate the pipeline to begin automated replication of your FTP data to BigQuery.

Try out all the features for free for 14 days