What specific FTP data can I extract with Kondado?

Kondado extracts CSV file contents through a dedicated pipeline containing 8 fields including row_number, column_number, and __file_basename. This captures both the actual data values within your files and structural metadata that helps you track data lineage, identify schema variations, and monitor which specific files generated each record in your analytics environment.

How do I connect my FTP server to Kondado?

Add the FTP data source in your Kondado dashboard by providing your server credentials and connection details. Once authenticated, you can immediately begin selecting which CSV files to replicate and configure your preferred destination for the extracted data.

What update frequencies are available for FTP data replication?

Kondado updates FTP data on a configurable schedule ranging from every 5 minutes to daily intervals. This allows you to choose near-real-time synchronization for time-sensitive operations or less frequent updates for batch-processed reporting workloads.

Can I analyze FTP data in Power BI and Google Sheets?

Yes, you can send FTP data directly to Power BI for enterprise dashboarding or Google Sheets for collaborative spreadsheet analysis. Both destinations receive automatically structured CSV data that refreshes on your configured schedule without manual file imports.

What types of analysis can I perform on CSV files from FTP?

You can perform row-level transactional analysis, track file delivery patterns through basename metadata, monitor schema consistency using column_number fields, and aggregate data across multiple file drops. The pipeline structure enables quality assurance checks by comparing expected versus actual column counts and data volumes over time.

Does Kondado support FTP data to BigQuery and PostgreSQL?

Yes, replicate FTP data directly to BigQuery for large-scale data warehousing analytics or PostgreSQL for operational database applications. These destinations enable SQL querying across historical file archives and integration with existing database schemas.

How does the CSV pipeline handle files with varying column structures?

The CSV pipeline captures column_number and row_number metadata for every record, allowing you to analyze files with inconsistent schemas within a single dataset. This structure preserves positional information about where data appeared in source files, enabling you to track schema drift and normalize varying formats during your analysis.

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Connect FTP Data to Dashboards and Databases

Kondado enables you to replicate FTP data from your CSV files directly into analytics environments, eliminating the need to build custom FTP API connections. Extract comprehensive row-level information alongside column structures and file metadata including row numbers, column positions, and file basenames. This allows you to analyze transactional data, inventory feeds, or marketing campaign results that arrive via FTP without manual preprocessing. Transform static file transfers into dynamic datasets ready for dashboards, spreadsheets, and database analytics.

Kondado’s FTP data source provides 1 pipeline with 8 fields, capturing CSV content with row_number, column_number, and __file_basename identifiers. This enables analysis of data from files with varying columns and structures, automatically standardizing inconsistent file formats for immediate querying.

Marketing teams can analyze campaign performance data uploaded to FTP servers, tracking conversion metrics and audience segments across multiple file drops to optimize spend. Data analysts benefit from automated structuring of inconsistent CSV formats, enabling cross-file trend analysis and historical comparisons without manual normalization work. Operations teams monitor file delivery patterns, data completeness, and upstream system health through metadata fields, building automated reporting workflows that alert when expected files arrive late or contain incomplete records.

The Kondado platform takes care of refreshing FTP data, allowing you to stop wasting time with manual work and complex workflows, and focus on analyzing FTP data in your report, spreadsheet, data warehouse, data lake, or database

Available FTP Pipelines

Below you will find the complete list of available pipelines for FTP data extraction and the specific fields available for analysis.

The CSV pipeline captures individual row values alongside positional metadata, enabling you to track data lineage through row_number identifiers and understand schema variations via column_number fields. Analyze file distribution patterns using __file_basename to monitor which upstream systems generated specific datasets, or aggregate row counts across multiple CSV drops to measure daily data volume trends and delivery consistency. Examine column structures to detect schema drift between file versions, and use file basename tracking to correlate specific uploads with business events or processing timestamps. This structure supports quality assurance checks by comparing expected column counts against actual delivered data, immediately flagging structural changes in your recurring FTP feeds before they impact downstream reporting.

While FTP provides foundational file data, combining these pipelines with CRM, ERP, or marketing platform data sources creates comprehensive analytics environments where file metadata correlates with operational events and business outcomes across your technology stack.

Configure updates to run every 5 minutes or daily, ensuring your FTP analytics reflect the latest file arrivals and enabling continuous monitoring of upload KPIs, file size trends, and data completeness metrics as new information lands throughout your business day.

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Available FTP data

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 →

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How to visualize FTP data in 3 steps

Connect FTP to dashboards, spreadsheets, or databases — no code required.

Add FTP data source

Access your Kondado dashboard and add the FTP data source by entering your server connection details and authentication credentials. This establishes the connection between your file server and Kondado's replication engine.

Select pipelines and destination

Select the CSV pipeline and configure which files to replicate, then choose your destination such as Power BI, Google Sheets, or BigQuery. You can also send data to PostgreSQL, MySQL, or Amazon S3 depending on your analytics needs.

Visualize and analyze data

Build custom dashboards in Looker Studio or Power BI, perform ad-hoc analysis in Google Sheets or Excel, and run SQL queries against your data in BigQuery, PostgreSQL, or Redshift. Your FTP data refreshes automatically on the schedule you configured, keeping all analytics current.

Try out all the features for free for 14 days