No-code pipeline · SQLServer → Amazon S3

Send data from SQLServer to Amazon S3

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

Kondado automatically replicates data from SQLServer to Amazon S3 on a configurable schedule, supporting intervals from every 5 minutes to daily updates, enabling seamless data lake architecture for analytics and business intelligence.

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Replicate SQLServer Data to Amazon S3 Automatically

How to send SQLServer data to Amazon S3? Kondado provides a direct pipeline that connects your SQLServer database to Amazon S3 without requiring complex coding or manual CSV exports. You configure the connection once, select which database objects to replicate, and define your preferred update schedule ranging from every 5 minutes to daily intervals. The platform handles data extraction and loading automatically, ensuring your S3 bucket stays synchronized with the latest information from your SQLServer instance while maintaining your existing workflow efficiency.

Once your SQLServer data lands in Amazon S3, you can immediately leverage AWS analytics services like Athena, Presto, or Dremio to query information directly without impacting your production database performance. This separation of storage and compute resources allows your analytics team to build custom reports, perform complex data transformations, and combine SQLServer information with other sources for comprehensive business insights.

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Kondado’s Tables and Views pipeline automatically discovers and replicates all your SQLServer database objects to Amazon S3, ensuring comprehensive coverage of your operational data. Whether you need transaction records, customer databases, or analytical views, each dataset arrives in your S3 bucket ready for immediate consumption by analytics engines.

With your SQLServer tables and views stored in Amazon S3, you can create unified data lakes that combine relational data with information from other platforms. Load this data into BigQuery for enterprise warehousing, connect to Power BI for executive dashboards, or use Looker Studio for marketing analysis alongside data from Google Analytics and other sources.

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Replicated to Amazon S3

Dynamic data

Kondado automatically reads the schema of your SQLServer. 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 SQLServer to Amazon S3 →

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How to send SQLServer data to Amazon S3

Sync data automatically — no code, no manual exports.

Connect Your SQLServer Database

Enter your SQLServer connection details including server address, database name, and credentials in the Kondado platform. The system validates your SQLServer connection and establishes a direct pipeline ready for data extraction.

Configure Amazon S3 Destination

Specify your target S3 bucket name and AWS region where you want SQLServer data stored, along with the appropriate access credentials. Kondado tests the connection to ensure your database information will land in the correct Amazon S3 location.

Select Data and Set Schedule

Choose which tables and views to replicate from your SQLServer instance and define your update frequency, from every 5 minutes to daily intervals. Once configured, Kondado automatically maintains your Amazon S3 data lake with fresh information on your specified schedule.

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Send data from SQLServer to other destinations

SQLServer > Looker Studio

SQLServer > Power BI

Other visualizations

SQLServer > Amazon S3

SQLServer > BigQuery

SQLServer > Excel

SQLServer > Google Sheets

SQLServer > MySQL

SQLServer > PostgreSQL

SQLServer > Redshift

SQLServer > SQL Server

Choose a tool to visualize your SQLServer data

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

SQLServer > Airtable

SQLServer > Alteryx

SQLServer > Amazon Quicksight

SQLServer > BIMachine

SQLServer > Cortex

SQLServer > Data Studio

SQLServer > Geckoboard

SQLServer > Grafana

SQLServer > IBM Cognos Analytics

SQLServer > Klipfolio

SQLServer > Looker Studio

SQLServer > Looker

SQLServer > Looqbox

SQLServer > Metabase

SQLServer > Microstrategy

SQLServer > Pentaho

SQLServer > Power BI

SQLServer > Qlik Cloud Analytics

SQLServer > Qlik Sense

SQLServer > Qlik

SQLServer > QlikView

SQLServer > Redash

SQLServer > Superset

SQLServer > Tableau

SQLServer > BI TOTVS

SQLServer > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending SQLServer data to Amazon S3 automatically

How does Kondado replicate SQLServer data to Amazon S3? ▼

Kondado establishes a direct connection to your SQLServer instance and extracts data from your selected tables and views. The platform then loads this information into your specified Amazon S3 bucket in a structured format, running on the schedule you configure from every 5 minutes to daily updates.

What SQLServer objects can I replicate to Amazon S3? ▼

You can replicate both tables and views from your SQLServer database through Kondado's automated pipeline. The platform maps all available database objects automatically, allowing you to select specific datasets or replicate your entire relational schema to S3 for comprehensive analytics.

How often does SQLServer data update in my Amazon S3 bucket? ▼

Kondado updates your Amazon S3 data on a configurable schedule that you control, with options ranging from every 5 minutes to hourly or daily intervals. This automated approach ensures your data lake reflects recent database changes without requiring manual intervention or scripting.

What file format does SQLServer data use when stored in Amazon S3? ▼

Data from SQLServer arrives in Amazon S3 in a structured format optimized for analytics workloads, ready for immediate querying through AWS Athena, Presto, or Dremio. This format supports efficient data virtualization and integration with business intelligence tools without additional transformation steps.

Can I combine SQLServer data in S3 with other data sources? ▼

Yes, Amazon S3 serves as a central data lake where SQLServer information can coexist with data from PostgreSQL, MySQL, Excel, and other platforms. You can then query this combined dataset using AWS analytics services or load it into BigQuery, Power BI, or Looker Studio for unified reporting.

Do I need coding skills to send SQLServer data to Amazon S3? ▼

No coding is required to configure the SQLServer to Amazon S3 pipeline, as Kondado provides a no-code interface for selecting data sources and destinations. You simply authenticate your database connection, choose your S3 bucket, and set your replication schedule through the visual platform.

How do I query SQLServer data once it is in Amazon S3? ▼

Once stored in Amazon S3, your SQLServer data can be queried directly using AWS Athena, Presto, or Dremio without moving it to another database. You can also load this information into BigQuery or connect business intelligence tools like Power BI and Looker Studio for comprehensive reporting and visualization.

What happens when my SQLServer schema changes? ▼

Kondado automatically detects new columns and schema modifications in your SQLServer tables and views during the replication process. These changes propagate to your Amazon S3 data on the next scheduled update, ensuring your analytics workflows continue functioning without manual pipeline adjustments.

Try out all the features for free for 14 days

Related on Kondado

Try out all the features for free for 14 days