How does Kondado replicate FTP files to Redshift?

Kondado connects to your FTP server as a data source and monitors specified directories for CSV files. When new or updated files appear, the platform parses the contents and loads them into your Redshift cluster while preserving metadata about the original file structure. The process runs on your configured schedule without requiring manual intervention or custom extraction scripts.

What specific data fields are available when replicating FTP CSV files?

The CSV pipeline includes eight fields that capture both the data content and file metadata. You receive row_number and column_number fields that document the exact position of each value in the original file, plus __file_basename to identify the source filename. This structure enables you to handle files with varying column layouts and track data lineage back to specific uploads.

How often can I update FTP data in my Redshift warehouse?

Kondado offers configurable refresh intervals ranging from every 5 minutes to daily updates, allowing you to balance data freshness with processing costs. You select the frequency when setting up the pipeline based on how quickly your FTP files change and how current your analytics need to be. Near-real-time updates ensure your Redshift queries reflect the latest file uploads without overwhelming your warehouse resources.

Can I combine FTP data with other sources in Redshift?

Yes, once your FTP data resides in Redshift, you can join it with information from additional platforms using standard SQL joins. Many users combine FTP exports with BigQuery datasets or PostgreSQL records to create unified views across their business operations. This capability enables comprehensive reporting that connects file-based data with transactional information from other systems.

What file formats does Kondado support from FTP to Redshift?

Currently, Kondado provides a CSV pipeline that handles comma-separated value files stored on your FTP server. The system automatically detects delimiters and handles varying column structures while standardizing the data into Redshift-compatible formats. This focus on CSV ensures reliable parsing of exports from legacy systems, ERP platforms, and standard business applications that generate flat file reports.

How is the data structured when FTP files arrive in Redshift?

Each CSV file transforms into a structured dataset where individual values map to specific columns alongside positional metadata. The schema includes the original data values plus system-generated fields like row_number and column_number that document where each piece of information appeared in the source file. This structure supports complex SQL analysis while maintaining the ability to reconstruct the original file layout when needed.

Can I analyze FTP data in visualization tools after it reaches Redshift?

Absolutely, Redshift serves as a central hub that connects to business intelligence platforms like Power BI , Looker Studio , and Google Sheets . Once replicated, your FTP contents become queryable datasets that power custom dashboards and automated reports. Analysts can build visualizations that combine file-based metrics with other business data to reveal operational insights and trends.

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Send FTP Data to Amazon Redshift

Sending FTP data to Redshift enables teams to analyze file-based information alongside other business data in a centralized warehouse. Kondado connects directly to your FTP server as a data source, automatically detecting CSV file structures and replicating contents into your Redshift cluster without manual scripting. You configure the connection credentials once, select which files to process, and define how frequently the data should refresh based on your analytics requirements.

Kondado replicates CSV files from your FTP server to Amazon Redshift on a configurable schedule, parsing file contents into structured rows with metadata fields including row_number, column_number, and __file_basename for complete data lineage tracking.

Once your FTP data lands in Redshift, you can combine it with information from additional sources to build comprehensive reports in Power BI, Looker Studio, or Google Sheets. This unified approach allows analysts to query file contents using standard SQL alongside transactional data, creating dashboards that reveal operational trends and performance metrics across your entire data ecosystem.

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The CSV pipeline captures every row from your FTP files along with essential metadata that preserves the original file structure. Fields like row_number and column_number enable you to reconstruct file layouts even when column headers vary between uploads, while __file_basename tracks which source file each record originated from.

With this data in Redshift, analysts can perform time-series analysis on log files, reconcile inventory exports from legacy systems, or merge marketing campaign data with website analytics. The structured format supports complex SQL aggregations, allowing you to calculate metrics across multiple file versions and identify data quality issues using the positional metadata fields.

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FTP data available for Redshift

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.

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How to send FTP data to Redshift

Sync data automatically — no code, no manual exports.

Connect Your FTP Server

Enter your FTP server credentials including host, username, and password in the Kondado platform to establish the data source connection. Specify the directory paths containing your CSV files so Kondado knows which folders to monitor for new uploads.

Configure Redshift Destination

Provide your Amazon Redshift cluster details including the host endpoint, database name, and user credentials to authorize data loading. Kondado validates the connection to ensure your data will land in the correct warehouse schema with appropriate write permissions.

Select CSV Pipeline and Schedule

Choose the CSV pipeline and define how frequently Kondado checks your FTP server for new files, with options ranging from every 5 minutes to daily updates. Once configured, your structured data becomes available in Redshift for analysis alongside information from other business systems.

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