No-code pipeline · InfluxDB v1 → PostgreSQL

Send data from InfluxDB v1 to PostgreSQL

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From InfluxDB v1 to PostgreSQL: managed, scheduled, no code.

Kondado connects InfluxDB v1 directly to PostgreSQL, automatically replicating measurement data on a configurable schedule with no coding required.

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Send InfluxDB v1 Data to PostgreSQL Automatically

How do you send InfluxDB v1 data to PostgreSQL without complex engineering work? With Kondado, you simply connect your InfluxDB v1 data source and select PostgreSQL as your destination. Choose which measurement pipelines you need, configure your update frequency from every 5 minutes to daily, and watch your time-series data flow automatically into your PostgreSQL database. This no-code approach eliminates manual exports and keeps your analytics infrastructure current with minimal effort.

Once your IoT metrics, application performance data, or infrastructure monitoring statistics land in PostgreSQL, you unlock powerful analytical possibilities. Combine your time-series data with transactional records from MySQL or CRM data from other sources to create unified datasets. Build custom reports in Power BI, Looker Studio, or Google Sheets to visualize trends alongside your business KPIs, enabling comprehensive analysis that drives operational decisions.

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Available Pipelines

Kondado offers specialized pipelines designed for time-series replication from InfluxDB v1 to PostgreSQL. The Measurement Data (Incremental) pipeline captures new metrics since your last update, perfect for tracking growing IoT sensor readings or application logs without duplication. Alternatively, the Measurement Data (Moving Window) pipeline lets you maintain a rolling dataset of recent observations, ideal for operational dashboards that only need the last 24 hours of infrastructure monitoring data. In PostgreSQL, this data becomes queryable alongside your relational business data, enabling you to correlate system performance with customer behavior or financial metrics using standard SQL.

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Replicated to PostgreSQL

Dynamic data

Kondado automatically reads the schema of your InfluxDB v1. All tables, views, and fields available in your account are extracted without manual configuration.

available pipelines

PostgreSQL

Destination

What Kondado extracts

Dados de Medição (Incremental)

Measurement data collected incrementally from the last execution

Dados de Medição (Janela Móvel)

Measurement data with a configurable moving time window

See all available data → Send InfluxDB v1 to PostgreSQL →

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How to send InfluxDB v1 data to PostgreSQL

Sync data automatically — no code, no manual exports.

Connect Your InfluxDB v1 Source

Enter your InfluxDB v1 connection details in Kondado to establish the data source. Authorize access to your time-series databases so the platform can read your measurement data and metadata.

Set Up PostgreSQL Destination

Provide your PostgreSQL credentials and specify the target database where your time-series data will land. Kondado automatically creates the necessary schema to store your InfluxDB v1 measurements in relational format.

Choose Pipelines and Schedule

Select from the Measurement Data (Incremental) or Measurement Data (Moving Window) pipelines to define which metrics to replicate. Set your update frequency, from every 5 minutes to daily, to ensure your PostgreSQL database stays current with your operational data.

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Send data from InfluxDB v1 to other destinations

InfluxDB v1 > Looker Studio

InfluxDB v1 > Power BI

Other visualizations

InfluxDB v1 > Amazon S3

InfluxDB v1 > BigQuery

InfluxDB v1 > Excel

InfluxDB v1 > Google Sheets

InfluxDB v1 > MySQL

InfluxDB v1 > PostgreSQL

InfluxDB v1 > Redshift

InfluxDB v1 > SQL Server

Choose a tool to visualize your InfluxDB v1 data

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

InfluxDB v1 > Airtable

InfluxDB v1 > Alteryx

InfluxDB v1 > Amazon Quicksight

InfluxDB v1 > BIMachine

InfluxDB v1 > Cortex

InfluxDB v1 > Data Studio

InfluxDB v1 > Geckoboard

InfluxDB v1 > Grafana

InfluxDB v1 > IBM Cognos Analytics

InfluxDB v1 > Klipfolio

InfluxDB v1 > Looker Studio

InfluxDB v1 > Looker

InfluxDB v1 > Looqbox

InfluxDB v1 > Metabase

InfluxDB v1 > Microstrategy

InfluxDB v1 > Pentaho

InfluxDB v1 > Power BI

InfluxDB v1 > Qlik Cloud Analytics

InfluxDB v1 > Qlik Sense

InfluxDB v1 > Qlik

InfluxDB v1 > QlikView

InfluxDB v1 > Redash

InfluxDB v1 > Superset

InfluxDB v1 > Tableau

InfluxDB v1 > BI TOTVS

InfluxDB v1 > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending InfluxDB v1 data to PostgreSQL automatically

How does Kondado replicate data from InfluxDB v1 to PostgreSQL? ▼

Kondado connects directly to your InfluxDB v1 instance and pulls measurement data through specialized pipelines. The platform writes this time-series data into your PostgreSQL database using automated processes that run on your chosen schedule, eliminating the need for manual CSV exports or custom scripts.

What specific measurement data can I replicate from InfluxDB v1? ▼

You can replicate any measurement data stored in your InfluxDB v1 databases, including IoT sensor readings, application performance metrics, and infrastructure monitoring statistics. The available pipelines handle both incremental updates of new data points and moving window extractions for specific time ranges.

How often can I update my InfluxDB v1 data in PostgreSQL? ▼

Kondado supports configurable update frequencies ranging from every 5 minutes to daily execution. This flexibility lets you choose near-real-time updates for critical monitoring or hourly syncs for analytical workloads, ensuring your PostgreSQL database reflects your operational state without overwhelming your systems.

What format does InfluxDB v1 data take when stored in PostgreSQL? ▼

Your time-series data arrives in PostgreSQL as structured relational tables with timestamped rows, making it immediately queryable with standard SQL. This transformation allows you to use familiar PostgreSQL tools and functions to analyze metrics that were previously locked in a specialized time-series format.

Can I combine InfluxDB v1 time-series data with other sources in PostgreSQL? ▼

Yes, once your metrics land in PostgreSQL, you can join them with data from MySQL, SQL Server, Amazon S3, or other business systems. This unified approach enables comprehensive analysis, such as correlating server performance metrics with sales data or customer activity tracked in separate databases.

Do I need coding skills to set up the InfluxDB v1 to PostgreSQL connection? ▼

No coding is required to configure this data source. The visual interface guides you through connecting your InfluxDB v1 instance, selecting pipelines, and scheduling updates, making it accessible to analysts and business users without engineering resources.

What is the difference between the Incremental and Moving Window pipelines? ▼

The Incremental pipeline captures only new measurement data since the last execution, building a growing historical dataset in PostgreSQL. The Moving Window pipeline extracts data from a configurable recent time period, such as the last 24 hours, which is ideal for maintaining current operational views while managing storage size.

Try out all the features for free for 14 days

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