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Distributed Django with Time Series

Deploy Django on Raspberry Pis and Collect Sensor Data

In this course, we show you how to run multiple iterations of Django with Celery to handle time series data. Time series data is simply any database entry that might change over time -- such as monitoring changes in temperature data that is collected by a sensor attached to a Raspberry Pi. This kind of data is rarely isolated to one sensor; there's usually a lot more.

The goal of this course is to teach you how to use Django as a orchestration tool for managing various sensor-like compute nodes. Another way to think of this is having Django integrated with Celery be the primary tool responsible for collecting the data, inserting it into a time-series optimized Postgres database, then visualizing the data.

Course topics

✅ Integrate Self-Hosted TimescaleDB with Django

✅ Integrate Timescale.com Cloud with Django

✅ Use TimescaleDB with django-timescaledb

✅ Integrate Django & Celery

✅ Django Celery Task to Generate Fake Data

✅ Using a Beat Server to run tasks on a schedule (e.g. every 5 seconds)

✅ Learn how to use Celery Task Queues for Individual Worker Nodes

✅ Run multiple Django instances through Docker Compose to emulate a multi-node production environment

✅ Docker Compose Watch to Auto Refresh Django Container

✅ Multi-Node Django+Celery Running on Docker Compose

✅ Configure Raspberry Pi OS for local network connection

✅ Use Ansible to Configure Pi Cluster for Django

✅ Integrate Production TimescaleDB across Docker Compose, Raspberry PIs, and a local Django project.

✅ TimescaleDB Queries and API Responses

✅ Visualizing Data with TimescaleDB and Chart.js

✅ Customize Python Decouple for multiple dotenv Environment Variable files

Lessons

Welcome