How does Kondado replicate FTP CSV files to SQL Server?

Kondado connects to your FTP server and automatically extracts CSV files based on your configured schedule. The platform parses each file and loads the data into SQL Server tables while preserving metadata like row numbers and file names. This process requires no manual coding or file handling, allowing you to focus on analysis rather than data preparation.

What metadata fields are available when replicating FTP data to SQL Server?

The CSV pipeline includes eight fields such as row_number, column_number, and __file_basename that help you track data lineage and structure. These fields enable analysis of files with varying column counts and allow you to identify which specific FTP file each record came from. You can use these metadata columns to filter queries, track file versions, or handle schema variations in your SQL Server database.

How often can I update FTP data in SQL Server?

You can configure replication to run every 5 minutes, 15 minutes, hourly, daily, or on any custom schedule that fits your business needs. This near-real-time capability ensures your SQL Server database stays current with the latest files uploaded to your FTP server. You maintain full control over the frequency to balance data freshness with system resources.

Can I combine FTP data with other sources in SQL Server?

Yes, once your FTP data resides in SQL Server, you can join it with data from BigQuery , PostgreSQL , or other connected platforms. This allows you to create comprehensive reports that correlate file-based data with transactional records or marketing analytics. Build unified dashboards in Power BI or Looker Studio that blend FTP exports with your existing business intelligence.

What happens if my CSV files have different column structures?

The CSV pipeline handles varying column structures by using metadata fields like column_number to track positional data and __file_basename to identify source files. This flexibility allows you to store and analyze CSV files with different schemas within the same SQL Server environment. You can query across these variations using the metadata fields to filter or categorize data by file source.

How do I query FTP file data once it is in SQL Server?

Your replicated FTP data appears as standard SQL Server tables that you can query using T-SQL, stored procedures, or any business intelligence tool that connects to SQL Server. Use the metadata columns to filter specific file batches or track data lineage across multiple uploads. Connect SQL Server to Power BI or Looker Studio to visualize trends from your FTP files alongside other corporate data sources.

Send data from FTP to SQL Server

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 SQL Server Automatically

You can send FTP data to SQL Server by using Kondado’s automated data replication platform. Simply connect your FTP server as a data source, select SQL Server as your destination, and configure which files you want to replicate. Kondado handles the extraction, transformation, and loading of your CSV files into structured database tables without requiring any coding or manual file handling. The platform manages all technical complexities including file parsing and schema detection.

Once your data arrives in SQL Server, you can build custom analytics, create automated reports, and combine FTP file data with other business systems for comprehensive analysis. This enables your team to transform raw file storage into actionable business intelligence using familiar SQL queries and reporting tools. Analysts can immediately begin querying replicated data alongside existing corporate databases to uncover trends and generate insights.

Kondado provides a direct integration between FTP and SQL Server that automatically replicates CSV files from your FTP server into SQL Server tables on a configurable schedule, including metadata fields like row_number and __file_basename to handle files with varying column structures.

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 replicates your FTP files into SQL Server with eight specialized fields including row_number, column_number, and __file_basename. This structure allows you to analyze data from files with varying column counts and track which source file each row originated from, making it ideal for processing exported reports, transaction logs, or third-party data feeds that land in your FTP server.

With your CSV data now in SQL Server, you can join these files with CRM records, financial databases, or operational systems to create unified dashboards. Build custom reports that track file arrival patterns, monitor data quality across different uploads, or aggregate historical trends from multiple CSV exports without manual Excel processing.

Try out all the features for free for 14 days

FTP data available for SQL Server

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 SQL Server

Sync data automatically — no code, no manual exports.

Connect Your FTP Server

Enter your FTP server credentials in Kondado to establish the data source connection and authenticate access to your file directories.

Configure SQL Server Destination

Set up SQL Server as your destination by providing connection details so Kondado knows where to create tables and replicate your CSV data.

Select CSV Files and Schedule

Choose which CSV files to replicate from your FTP server and define your update frequency, whether every 5 minutes for near-real-time analysis or daily for batch processing.

Try out all the features for free for 14 days