No-code pipeline · Amazon S3 → Amazon S3

Send data from Amazon S3 to Amazon S3

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From Amazon S3 to Amazon S3: managed, scheduled, no code.

Kondado automatically replicates files from your source Amazon S3 buckets to destination Amazon S3 buckets on a configurable schedule, supporting CSV Files pipelines with customizable delimiters and file prefixes to organize your data efficiently.

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Send Amazon S3 Data to Amazon S3 Automatically

How do you consolidate files from multiple Amazon S3 buckets or maintain synchronized copies across regions? With Kondado, you connect your source Amazon S3 data source, configure your destination bucket settings, and define how frequently the synchronization occurs. The platform handles the automated replication of your CSV Files and other supported formats without requiring engineering resources or complex ETL scripting.

Once your data arrives in the destination Amazon S3, you can query it directly with Amazon Athena, process it through Presto or Dremio for virtualization, or combine it with other sources to build comprehensive analytics workflows. This automated approach ensures your storage layers remain synchronized for backup, archival, or cross-functional analysis purposes.

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The CSV Files pipeline enables you to replicate structured data exports from your source buckets with precise control over column delimiters and file naming conventions. This is particularly valuable when consolidating monthly sales reports from distributed systems or aggregating log files from multiple application instances into a centralized Amazon S3 repository for unified analysis.

By maintaining organized file prefixes and consistent formatting through Kondado’s automated replication, your teams can immediately query this data using Amazon Athena or feed it into Presto clusters without manual preprocessing. The pipeline supports configurable start reading dates, ensuring you capture historical data exactly when needed while maintaining near-real-time synchronization for ongoing operations.

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Replicated to Amazon S3

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

Amazon S3

Destination

What Kondado extracts

CSV Files

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

See all available data → Send Amazon S3 to Amazon S3 →

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How to send Amazon S3 data to Amazon S3

Sync data automatically — no code, no manual exports.

Connecting Amazon S3 on Kondado

Enter your AWS access keys and source bucket details to authenticate your data source, enabling automated replication that can subsequently feed Power BI, BigQuery, or Redshift from your destination bucket.

Configuring Amazon S3

Specify your destination bucket, file prefixes for logical folder structures, and column delimiters to ensure CSV Files are organized for optimal querying with Athena and Presto.

Selecting data and setting the update schedule

Choose the CSV Files pipeline, set your start reading date for historical data inclusion, and configure your update interval so files remain synchronized for use in Google Sheets, Looker Studio, or MySQL workflows.

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Send data from Amazon S3 to other destinations

Amazon S3 > Looker Studio

Amazon S3 > Power BI

Other visualizations

Amazon S3 > Amazon S3

Amazon S3 > BigQuery

Amazon S3 > Excel

Amazon S3 > Google Sheets

Amazon S3 > MySQL

Amazon S3 > PostgreSQL

Amazon S3 > Redshift

Amazon S3 > SQL Server

Choose a tool to visualize your Amazon S3 data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Amazon S3 > Airtable

Amazon S3 > Alteryx

Amazon S3 > Amazon Quicksight

Amazon S3 > BIMachine

Amazon S3 > Cortex

Amazon S3 > Data Studio

Amazon S3 > Geckoboard

Amazon S3 > Grafana

Amazon S3 > IBM Cognos Analytics

Amazon S3 > Klipfolio

Amazon S3 > Looker Studio

Amazon S3 > Looker

Amazon S3 > Looqbox

Amazon S3 > Metabase

Amazon S3 > Microstrategy

Amazon S3 > Pentaho

Amazon S3 > Power BI

Amazon S3 > Qlik Cloud Analytics

Amazon S3 > Qlik Sense

Amazon S3 > Qlik

Amazon S3 > QlikView

Amazon S3 > Redash

Amazon S3 > Superset

Amazon S3 > Tableau

Amazon S3 > BI TOTVS

Amazon S3 > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending Amazon S3 data to Amazon S3 automatically

How does Kondado replicate data between Amazon S3 buckets? ▼

Kondado connects to your source bucket using your AWS credentials, then automatically transfers files to your destination bucket based on the schedule you configure. The platform manages file organization through customizable prefixes and handles CSV parsing with your specified delimiters to ensure data lands in the correct structure for immediate use.

What file formats can I replicate from Amazon S3 to Amazon S3? ▼

Currently, Kondado supports the CSV Files pipeline, which handles comma-separated and custom-delimited text files stored in your source buckets. You can specify column delimiters and file prefixes during setup to ensure the replicated data maintains consistent formatting compatible with analytics tools like Athena and Presto.

How often can I schedule Amazon S3 to Amazon S3 replication updates? ▼

You can configure automated updates to run every 5 minutes, 15 minutes, hourly, or daily depending on your business requirements and data velocity needs. This flexible scheduling ensures your destination buckets stay synchronized with source changes without manual intervention or complex cron jobs.

Can I combine Amazon S3 data with other sources after replication? ▼

Yes, once your data resides in Amazon S3, you can blend it with information from Amazon S3 connections to other buckets or send consolidated datasets to BigQuery, PostgreSQL, or Power BI for unified reporting across your entire data ecosystem.

What data organization options are available for CSV Files in Amazon S3? ▼

The CSV Files pipeline allows you to define file prefixes for logical folder structures, choose specific column delimiters for parsing compatibility, and set start reading dates to control which historical files get included. These options help maintain clean data lakes that integrate seamlessly with Dremio and other virtualization layers.

How do I handle historical data when starting a new Amazon S3 pipeline? ▼

When configuring your pipeline, you can specify a start reading date to capture existing files from a particular point in time or begin with only new files going forward. This flexibility allows you to backfill historical exports or start fresh depending on your current data warehouse strategy and storage optimization goals.

Can I send Amazon S3 data to destinations other than Amazon S3? ▼

Absolutely, Kondado enables you to route the same Amazon S3 data to multiple endpoints including Google Sheets for quick sharing, Looker Studio for visualization, or Redshift and MySQL for data warehouse consolidation alongside your Amazon S3 storage.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days