No-code pipeline · PostgreSQL → Amazon S3

Send data from PostgreSQL to Amazon S3

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

Creating a pipeline that sends data from PostgreSQL to Amazon S3 data lakes takes only a few minutes with Kondado. And the whole integration from PostgreSQL to Amazon S3 is managed and executed by our platform. With Kondado, you can focus on extracting value from PostgreSQL data and combining it with other data in your Amazon S3 data lake

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Send PostgreSQL Data to Amazon S3 Automatically

Kondado automates the process of moving data from your PostgreSQL database to Amazon S3 without requiring engineering resources. Simply authenticate your database, configure your S3 bucket as the destination, and define your replication schedule. The platform handles the extraction and loading processes, ensuring your relational data arrives in your data lake ready for analysis.

Once your PostgreSQL data lands in Amazon S3, you unlock powerful analytics capabilities by connecting storage with compute services. Use the data with Athena for SQL queries, Presto for interactive analysis, or Dremio for data virtualization. This architecture enables you to build custom reports and combine your PostgreSQL datasets with information from other sources for comprehensive business intelligence.

Kondado provides automated replication of PostgreSQL data to Amazon S3 on a configurable schedule, supporting intervals from five minutes to daily updates. The service automatically maps all available database tables and views through its Tables and Views pipeline, delivering data in formats optimized for AWS analytics services including Athena, Presto, and Dremio.

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The Tables and Views pipeline automatically discovers and replicates all your PostgreSQL database objects to Amazon S3, maintaining the exact structure you need for comprehensive analytics. This pipeline maps every table and view from your relational database, delivering complete datasets in formats optimized for immediate querying with Athena, Presto, or Dremio.

With your historical and current data stored in S3, you can create persistent data lakes that feed into BigQuery, Power BI, or Looker Studio through direct integration. This setup empowers analysts to build custom reports combining PostgreSQL transactional data with marketing platforms, CRM systems, and other business applications stored in the same S3 environment, creating unified dashboards that drive strategic decisions.

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Replicated to Amazon S3

Dynamic data

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

available pipeline

Amazon S3

Destination

What Kondado extracts

Tabelas e Views

Kondado automatically maps all tables and views available in your database

See all available data → Send PostgreSQL to Amazon S3 →

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How to send PostgreSQL data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Your PostgreSQL Database

Add your PostgreSQL database credentials to Kondado, including host, port, username, and password, to establish the initial data source connection.

Configure Amazon S3 Destination

Provide your AWS bucket details and authentication keys to set up Amazon S3 as the target location for your replicated PostgreSQL data.

Select Pipelines and Schedule

Choose the Tables and Views pipeline to automatically map your database objects, then set your preferred update frequency from five minutes to daily intervals.

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Send data from PostgreSQL to other destinations

PostgreSQL > Looker Studio

PostgreSQL > Power BI

Other visualizations

PostgreSQL > Amazon S3

PostgreSQL > BigQuery

PostgreSQL > Excel

PostgreSQL > Google Sheets

PostgreSQL > MySQL

PostgreSQL > PostgreSQL

PostgreSQL > Redshift

PostgreSQL > SQL Server

Choose a tool to visualize your PostgreSQL data

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

PostgreSQL > Airtable

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PostgreSQL > Amazon Quicksight

PostgreSQL > BIMachine

PostgreSQL > Cortex

PostgreSQL > Data Studio

PostgreSQL > Geckoboard

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PostgreSQL > Klipfolio

PostgreSQL > Looker Studio

PostgreSQL > Looker

PostgreSQL > Looqbox

PostgreSQL > Metabase

PostgreSQL > Microstrategy

PostgreSQL > Pentaho

PostgreSQL > Power BI

PostgreSQL > Qlik Cloud Analytics

PostgreSQL > Qlik Sense

PostgreSQL > Qlik

PostgreSQL > QlikView

PostgreSQL > Redash

PostgreSQL > Superset

PostgreSQL > Tableau

PostgreSQL > BI TOTVS

PostgreSQL > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending PostgreSQL data to Amazon S3 automatically

How does Kondado handle schema changes when replicating PostgreSQL to Amazon S3? ▼

Kondado automatically detects new columns and schema modifications in your PostgreSQL database during each replication cycle. When you add tables or alter existing structures, the Tables and Views pipeline updates the corresponding files in your S3 bucket to reflect these changes. This ensures your data lake remains current with your source database structure without manual intervention.

Can I choose specific PostgreSQL tables to replicate to Amazon S3? ▼

Yes, the Tables and Views pipeline allows you to select individual tables and views from your PostgreSQL database for replication. You can exclude sensitive or temporary tables while focusing on the datasets needed for your analytics workflows. This selective approach optimizes storage costs in Amazon S3 and reduces processing time for your specific use cases.

What file formats does Kondado use when storing PostgreSQL data in Amazon S3? ▼

Kondado delivers your PostgreSQL data in formats optimized for AWS analytics services, typically structured formats that work efficiently with Athena, Presto, and Dremio. The platform handles the conversion from PostgreSQL's relational structure to the appropriate file types for your S3 data lake. This eliminates the need for manual data transformation before you can query your information.

How frequently can I update my PostgreSQL data in Amazon S3? ▼

You can configure replication schedules ranging from every five minutes to daily updates, depending on your business requirements and data freshness needs. Near-real-time updates every five minutes support operational dashboards, while hourly or daily schedules suit analytical workloads and historical reporting. You can adjust these frequencies anytime to match changing business priorities.

Can I combine PostgreSQL data with other sources in the same Amazon S3 bucket? ▼

Absolutely, Kondado enables you to replicate data from multiple sources including PostgreSQL, MySQL, and various SaaS applications into a unified Amazon S3 destination. This consolidated approach creates a single source of truth for your organization, allowing you to join datasets from different systems using Athena or Presto. Analysts can then build comprehensive reports that correlate transactional data with marketing, sales, and operational metrics.

How do I query my PostgreSQL data once it is stored in Amazon S3? ▼

Once replicated to S3, query your PostgreSQL data using Amazon Athena for serverless SQL analysis, or connect business intelligence tools like Power BI and Looker Studio directly to your data lake. The file structure created by Kondado supports standard SQL queries without requiring additional data modeling. This immediate accessibility accelerates your time-to-insight for custom dashboard creation.

Can I send PostgreSQL data from Amazon S3 to other destinations? ▼

While Amazon S3 serves as a powerful data lake, you can also route your PostgreSQL data to additional destinations such as BigQuery, Google Sheets, or Redshift for specialized analysis. Kondado supports multiple destination routing, allowing you to maintain S3 as your primary archive while pushing subsets to Power BI for immediate visualization. This flexibility ensures your data reaches every tool in your analytics stack.

Does Kondado maintain relationships between PostgreSQL tables when replicating to S3? ▼

Kondado replicates each PostgreSQL table and view as individual files in Amazon S3, preserving the original data structure and relationships through consistent naming conventions. When querying with Athena or Presto, you can reconstruct joins and relationships using standard SQL syntax across these files. This approach maintains data integrity while leveraging S3's scalability for your entire relational dataset.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days