How does Kondado handle CSV files with different delimiters from Amazon S3 to SQL Server?

The CSV Files pipeline includes a Column delimiter field that lets you specify commas, semicolons, tabs, or custom separators for each data source configuration. You can create multiple pipelines from the same Amazon S3 bucket with different delimiter settings to handle various file formats. This ensures that whether your files use standard CSV formatting or regional variants, the data parses correctly into SQL Server columns.

Can I replicate only specific files from my S3 bucket to SQL Server using file prefixes?

Yes, the File prefix parameter allows you to filter which objects get replicated by matching specific folder paths or naming patterns within your S3 bucket. For example, you can set prefixes like "sales/2024/" or "daily_exports_" to ingest only relevant datasets while ignoring temporary or archived files. This targeted approach keeps your SQL Server storage optimized with only the data you need for analysis.

How often can I schedule updates when sending S3 data to SQL Server?

Kondado offers flexible scheduling options ranging from near-real-time intervals of 5 minutes to hourly, daily, or custom cron-based schedules. You can configure different frequencies for different pipelines, such as updating sales data every 15 minutes while refreshing inventory files once per day. This granularity ensures your SQL Server remains current without overwhelming your system with unnecessary processing.

What data format does the replicated data take in SQL Server?

Data from your CSV files lands as structured relational tables within your SQL Server database, with columns mapped according to the detected schema and delimiter configuration. Each pipeline creates dedicated tables that you can query using standard T-SQL, join with existing datasets, or connect to visualization tools like Power BI . The tabular format supports indexing, stored procedures, and complex analytics workflows directly on your replicated S3 content.

Can I combine Amazon S3 data with other sources in the same SQL Server database?

Absolutely, you can replicate data from BigQuery , PostgreSQL , Google Ads, and other platforms into the same SQL Server instance alongside your S3 files. This unified approach enables cross-source analytics, allowing you to join CSV exports from S3 with transactional data from MySQL or marketing metrics from Looker Studio -compatible sources. Your SQL Server becomes a centralized hub for comprehensive business intelligence.

Does Kondado support incremental updates for large CSV files in Amazon S3?

The pipeline uses the Start reading date parameter to identify new files or modifications since your last scheduled update, enabling efficient incremental replication. Rather than reloading entire datasets, Kondado appends or updates only the rows corresponding to recent S3 objects that match your file prefix criteria. This minimizes processing time and keeps your SQL Server performance optimized even with growing data volumes.

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Send Amazon S3 Data to SQL Server Automatically

To send Amazon S3 data to SQL Server, you need a solution that bridges cloud storage with your relational database. Kondado provides a direct pipeline that replicates CSV files and other structured data from your S3 buckets directly into SQL Server. The process requires no coding: you simply configure your Amazon S3 data source, specify which files to read using prefixes and delimiters, and map the destination to your SQL Server instance. Data flows automatically on your chosen schedule, whether you need updates every few minutes or daily batches.

Kondado replicates Amazon S3 CSV files to SQL Server on a configurable schedule, allowing you to specify file prefixes, column delimiters, and start reading dates to control exactly which data enters your database.

Once your data lands in SQL Server, you can build custom analytics, feed business intelligence tools, or power operational applications without manual file handling. This automated pipeline eliminates the need to write custom scripts for S3 file ingestion, letting analysts focus on deriving insights rather than data wrangling.

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Available Pipelines

The CSV Files pipeline enables you to replicate structured data from Amazon S3 into SQL Server with precise control over file selection. By configuring file prefixes, you can isolate specific datasets such as daily transaction logs, customer exports, or inventory snapshots into separate ingestion streams. Each pipeline instance can use different column delimiters and start reading dates, allowing you to handle varying CSV formats from different S3 folders within the same automated workflow. Once in SQL Server, this data supports custom reporting in Power BI, Looker Studio, or internal analytics applications.

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Dynamic data

Kondado automatically reads the schema of your Amazon S3. All tables, views, and fields available in your account are extracted without manual configuration.

available pipeline

What Kondado extracts

CSV Files

Includes fields such as Start reading date, Column delimiter, and File prefix, enabling efficient data reading and organization.

Integration	Description
CSV Files	Includes fields such as Start reading date, Column delimiter, and File prefix, enabling efficient data reading and organization.

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How to send Amazon S3 data to SQL Server

Sync data automatically — no code, no manual exports.

Connect Your Amazon S3 Bucket

Create a new data source in Kondado using your Amazon S3 credentials and bucket details, then test the connection to ensure access to your stored CSV files.

Configure SQL Server Destination

Enter your SQL Server connection string and database credentials in Kondado's destination settings, specifying the target database where your S3 data will land as structured tables.

Select CSV Files and Schedule

Choose the CSV Files pipeline, define your file prefix and column delimiter preferences, then set your update schedule to replicate data automatically every 5 minutes, hourly, or daily.

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