No-code pipeline · Amazon S3 → BigQuery

Send data from Amazon S3 to BigQuery

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

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

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Send Amazon S3 Data to BigQuery Automatically

To send Amazon S3 data to BigQuery, connect your AWS storage as a data source in Kondado, configure your Google Cloud destination warehouse, and select the CSV Files pipeline from the available options. Kondado handles the entire extraction and loading process on a configurable schedule that you control, eliminating manual file transfers, complex ETL scripts, and API maintenance. Your data arrives in BigQuery ready for immediate SQL analysis and visualization, allowing you to focus on generating actionable business insights rather than managing infrastructure or writing custom Python scripts to parse CSV files from S3 buckets.

Kondado provides a direct integration between Amazon S3 and BigQuery, replicating CSV files from your AWS storage to your Google Cloud warehouse on a configurable schedule ranging from every 5 minutes to daily. The platform manages file detection, format parsing, and schema mapping automatically, delivering structured data to BigQuery without requiring coding or manual intervention.

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Available Pipelines

With the CSV Files pipeline, you can replicate structured data exports from your S3 buckets directly into BigQuery for comprehensive analysis and transformation. This enables you to combine historical transaction logs, marketing campaign exports, or application-generated CSV dumps with other business data in your warehouse, creating a centralized analytics environment. Once in BigQuery, you can join these datasets with information from additional sources to create unified reports in Looker Studio, Power BI, or Google Sheets, building custom dashboards that track performance metrics across your entire data ecosystem without manual data preparation.

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Replicated to BigQuery

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

BigQuery

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 BigQuery →

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How to send Amazon S3 data to BigQuery

Sync data automatically — no code, no manual exports.

Connect Amazon S3 Data Source

Set up your Amazon S3 connection by providing your AWS credentials and bucket details in Kondado's data source configuration panel.

Configure BigQuery Destination

Select BigQuery as your destination and enter your Google Cloud project and dataset information to establish the target warehouse for your replicated files.

Select CSV Pipeline and Schedule

Choose the CSV Files pipeline, set your column delimiter and file prefix preferences, then configure your update schedule from 5 minutes to daily for automated replication.

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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

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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 BigQuery automatically

How does Kondado replicate data from Amazon S3 to BigQuery? ▼

Kondado connects to your Amazon S3 bucket and monitors for CSV files based on your configured prefix and date settings. When new files appear, the platform automatically extracts the data, parses the column structure using your specified delimiter, and loads it into your BigQuery dataset. The process runs on your chosen schedule, ensuring your warehouse stays current without manual file downloads or uploads.

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

Currently, Kondado supports CSV file replication from Amazon S3 to BigQuery, with configurable delimiters to handle various formatting standards. The pipeline reads files sequentially based on your start date parameter and file prefix filters, ensuring only relevant data transfers occur. This focused approach delivers clean, structured data ready for SQL analysis in your Google Cloud environment.

How often does the data update from S3 to BigQuery? ▼

You control the replication frequency through Kondado's configurable scheduling options, which range from every 5 minutes to daily updates. This flexibility allows you to balance data freshness with processing costs, updating near-real-time operational data more frequently while scheduling historical exports for less frequent transfers. The automated schedule eliminates the need for manual trigger-based synchronization.

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

Yes, once your S3 data resides in BigQuery, you can join it with datasets from over 80 additional data sources available in Kondado. This capability allows you to enrich CSV exports from S3 with CRM data, advertising metrics, or database records, creating comprehensive views that span multiple platforms. The unified warehouse environment supports complex SQL joins and transformations across all your replicated data.

What happens to my CSV structure when it arrives in BigQuery? ▼

Kondado preserves your CSV column structure as BigQuery schemas, converting text-based data types to appropriate BigQuery formats during the load process. The pipeline respects your specified column delimiter settings, ensuring accurate field separation that maintains data integrity. Once loaded, you can query the data using standard SQL or connect it to visualization tools like Looker Studio for immediate analysis.

Do I need to manually map CSV columns to BigQuery schema? ▼

The CSV Files pipeline includes automated schema detection that maps your CSV columns to BigQuery fields based on the file header row and delimiter configuration. You define the column delimiter and file prefix during setup, which guides how Kondado interprets and structures the incoming data. This automated mapping reduces setup time while ensuring consistent data organization in your warehouse.

Can I filter which CSV files get replicated from my S3 bucket? ▼

Yes, you can use the File prefix setting to specify which CSV files Kondado should replicate from your S3 bucket, allowing you to target specific directories or naming patterns. Combined with the Start reading date parameter, this filtering ensures only relevant, recent files enter your BigQuery warehouse. This selective replication prevents unnecessary data transfer and storage costs while keeping your analytics focused on current information.

Try out all the features for free for 14 days

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Try out all the features for free for 14 days