From Data to Dashboards: Building Streamlit Applications with InfluxDB 3

By Suyash Joshi / Developer
Oct 10, 2025

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Python developers often reach for Streamlit when they need to construct compelling web applications quickly. It provides a fast way to transform Python scripts into interactive applications without complex web frameworks. When paired with InfluxDB 3 Core, the leading time series database, engineers can build powerful real-time analytics dashboards entirely in Python.

This combination provides a Python-friendly alternative to traditional visualization tools like Grafana or Power BI, which can be somewhat limiting when advanced customization or domain-specific functionality is required.

Why Streamlit & InfluxDB 3 Core?

  • Time-Series Focus: InfluxDB 3 Core is optimized for handling high-ingest, time-stamped data.
  • Python Integration: The InfluxDB 3 Python client makes querying and analyzing data straightforward.
  • Rapid Prototyping: Streamlit allows you to create bespoke, interactive Python applications in minutes.
  • Flexibility: Developers can add custom logic, machine learning models, or specialized visualizations easily.

Architecture

Streamlit architecture

Example: financial data dashboard

To demonstrate this integration, I’ve created a starter sample project that stores real and dummy stock and cryptocurrency prices in InfluxDB 3 Core and visualizes them in Streamlit. You are encouraged to download/fork and build on top of it for your use case or use it to learn how easy it is to integrate InfluxDB 3 with the Streamlit framework.

Financial use cases:

This app can easily extend to cover the following financial use cases:

  • Real-time stock and crypto tracking
  • Portfolio comparison across multiple assets
  • Technical analysis indicators, such as moving averages and RSI, using InfluxDB 3’s Processing Engine
  • Clean, responsive interface with Plotly-powered charts for mobile and web

__Download/clone sample app: __ git clone https://github.com/InfluxCommunity/financials-dashboard-streamlit.git

__Install dependencies: __ pip install influxdb3-python streamlit plotly

Query data using InfluxDB 3’s Python client library and render in a Streamlit web app:

import streamlit as st
from influxdb_client_3 import InfluxDBClient3

client = InfluxDBClient3(host="http://localhost:8181", token="your_token", database="stock_data")
result = client.query("SELECT time, price FROM prices WHERE symbol='AAPL'")
df = result.to_pandas()

st.line_chart(df.set_index("time")["price"])

Open the dashboard app to interact to view a real-time dashboard of trading data.

streamlit run streamlit_app.py

Why this matters

By combining Streamlit and InfluxDB 3 Core, developers can move beyond prebuilt dashboards and create custom applications tailored to their data and use cases. Whether for IoT monitoring, financial analytics, or devops observability, this approach gives Python developers complete control over both the data pipeline and the user interface. We hope this was helpful and welcome any comments or questions in our developer forum, Slack, Reddit, and Discord community.