Data connector

Send Amazon S3 data to AI, reports, spreadsheets and ETL

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Amazon S3

Storage and Transfer

Centralize your Amazon S3 data to unlock insights from CSV files stored in your cloud object storage, whether you use S3 as a simple file repository or an Amazon S3 data lake. Whether you manage transaction logs, customer datasets, application exports, or IoT sensor files in your buckets, Kondado helps you transform static file storage into actionable business intelligence. Organizations use Amazon S3 analytics to analyze historical archives, track operational metrics from uploaded logs, and combine file-based data with other business systems for comprehensive reporting. By automating the replication of S3 data storage contents, you eliminate manual file downloads and enable consistent analysis of growing datasets without managing complex ETL scripts.

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Kondado connects to Amazon S3 using your AWS Access Key ID, Secret Access Key, Bucket name, and Region to access CSV files stored in your buckets. Through dynamic mapping, you select which specific datasets, schemas, or file collections to replicate to your chosen destination on a configurable schedule, enabling automated data flows from your cloud storage to warehouses and BI tools.

Data engineers and business analysts benefit immediately by automating manual CSV processing and enabling analysis of file-based datasets on schedules ranging from every five minutes to daily. Operations managers can track inventory levels from uploaded logistics files, while finance teams reconcile accounts using exported ledger data stored in S3. Marketing analysts combine customer behavior logs with advertising platform data to measure campaign effectiveness across channels. Product teams monitor application performance through error logs and usage statistics, turning raw storage files into strategic decision-making tools that drive operational efficiency.

The Kondado platform takes care of refreshing Amazon S3 data, allowing you to stop wasting time with manual work and complex workflows, and focus on analyzing Amazon S3 data with AI (Claude, ChatGPT and MCP) or in your report, spreadsheet, data warehouse, data lake, or database

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Once you configure your Amazon S3 data source below, your CSV files flow automatically into your preferred analytics environment. Finance teams can build cash flow dashboards tracking monthly reconciliations, while operations managers monitor supply chain KPIs from inventory logs and shipment records. Marketing departments create customer segmentation analyses combining S3 behavioral data with advertising metrics, and product teams visualize application performance trends from error logs and usage statistics.

Combine Amazon S3 data with CRM systems, advertising platforms, and database sources in Kondado to create unified cross-platform views of business performance. Merge customer transaction histories from S3 with Salesforce records to calculate lifetime value, or combine IoT sensor data with manufacturing schedules for predictive maintenance insights.

With automated updates running on your chosen schedule, these analyses stay current without manual intervention, ensuring your reports always reflect the latest uploaded files and business conditions.

Connector schema

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.

See all available data → Use this connector in chat (AI + MCP) →

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How to visualize Amazon S3 data in 3 steps

Connect Amazon S3 to AI (Claude/ChatGPT via MCP), dashboards, spreadsheets, or databases — no code required.

Configure Your Amazon S3 Credentials

Enter your AWS Access Key ID, Secret Access Key, Bucket name, and Region in Kondado to establish access to your S3 storage. This connection enables the platform to read CSV files from your specified bucket for replication.

Select Data and Destination

Use dynamic mapping to choose which CSV files, datasets, or schemas to replicate from your S3 bucket, then select where to send your data such as Power BI, BigQuery, Google Sheets, or PostgreSQL. You can configure multiple pipelines to different destinations from the same S3 source.

Analyze in Dashboards, Sheets, or Databases

Visualize your Amazon S3 data in Power BI or Looker Studio dashboards, explore datasets in Google Sheets or Excel spreadsheets, or query structured tables in BigQuery, PostgreSQL, MySQL, Redshift, or SQL Server databases. Set your update schedule to keep all analyses current with automated data refreshes.

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Hundreds of data-driven companies trust Kondado

Why Kondado?

Focus on extracting value from your data and leave the rest to us

Wherever you need

With the same platform, you send data to AI (Claude/ChatGPT via MCP), reports, spreadsheets, databases, data warehouses, and data lakes. ETL and ELT

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From wherever you need

Data from over 80 sources for Marketing, Ecommerce, ERPs, Customer Service, Finance, Databases, and much more

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Predictable pricing

No surprise invoices, pick a plan and change it anytime

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Pick an AI, Spreadsheet, Database, Data Warehouse or Data Lake to use Amazon S3 data

Amazon S3 > AI (Claude, ChatGPT, MCP)

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

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Amazon S3 > BI TOTVS

Amazon S3 > Zoho Analytics

Frequently Asked Questions (FAQ)

Find answers to common questions about connecting Amazon S3 to AI (Claude/ChatGPT via MCP), dashboards, spreadsheets, and databases

What credentials do I need to connect Amazon S3 to Kondado? ▼

You need your AWS Access Key ID, Secret Access Key, Bucket name, and Region to authenticate with the Amazon S3 API and establish the connection. Kondado uses these credentials to access CSV files stored in your specified S3 bucket through standard AWS API endpoints. Ensure the IAM user associated with these credentials has appropriate read permissions for the objects you want to replicate.

Do I need to whitelist Kondado IP addresses for Amazon S3 connections? ▼

IP whitelisting is not typically required for standard Amazon S3 connections since S3 is accessed via the public AWS API endpoints. However, if your organization has implemented strict bucket policies or VPC endpoint configurations that restrict access by IP, you may need to add Kondado's IP addresses to your allowlist. Check your current AWS security group and bucket policy settings to determine if this step is necessary for your environment.

How do I choose which files or datasets to replicate from my S3 bucket? ▼

Kondado uses dynamic mapping to let you browse and select specific CSV files, folders, or data schemas within your S3 bucket during setup. After entering your credentials, the platform displays available data structures for you to choose which pipelines to activate. You can select individual files or entire directory structures depending on your analysis requirements.

Does Kondado support incremental sync for Amazon S3 data? ▼

Yes, Kondado supports incremental replication for Amazon S3 pipelines, updating only new or modified records since your last sync rather than reloading entire datasets. This approach optimizes processing time and reduces costs associated with transferring large volumes of historical data repeatedly. Configure your sync schedule to run as frequently as every five minutes or as infrequently as daily based on your business needs.

Can I combine Amazon S3 data with other data sources in Kondado? ▼

Absolutely, Kondado enables you to merge Amazon S3 data with over 80 other sources including CRM platforms, advertising tools, and databases. Create cross-platform pipelines that join your S3 CSV files with Salesforce contacts, Google Ads campaigns, or PostgreSQL transaction records. This capability allows you to build comprehensive reports that correlate file-based storage data with live system information.

Which destinations can I send my Amazon S3 data to? ▼

You can replicate Amazon S3 data to business intelligence tools like Power BI and Looker Studio, spreadsheets including Google Sheets and Excel, or data warehouses such as BigQuery, PostgreSQL, MySQL, Redshift, and SQL Server. This flexibility allows you to choose the destination that best fits your existing analytics stack and technical requirements.

What file format does Kondado require for Amazon S3 connections? ▼

Kondado currently supports CSV files stored in Amazon S3 buckets for data replication. Your files should be properly formatted with consistent column headers and delimiters to ensure accurate schema detection during the dynamic mapping process. Ensure your data is structured in CSV format before configuring your pipeline to enable successful replication to your chosen destination.

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