Storing & Visualizing Time Series Data

2h 11m 34s
English
Paid

Course description

The processing, storage, and visualization of time series data are becoming increasingly important tasks. From IoT data and system logs to production process statistics, the volume of information requiring processing is constantly growing. Time series storage systems such as **InfluxDB** and visualization tools like **Grafana** allow data management and make it available for analysis. In this course, you will learn how to build a full pipeline for working with time series in practice.
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What you will learn in the course

  • How to process time series data from CSV files (e.g., air quality data)
  • How to connect external APIs to obtain real-time weather data
  • How to write data to InfluxDB 2.0 and query it using Python and the Flux language
  • How to create and configure dashboards in Grafana: server installation, panel configuration, and access management

Course structure

Introduction

You will get an overview of the project, familiarize yourself with the data set being used and the InfluxDB interface: graphs, queries, and data structure. We will also discuss the platform architecture to understand how all components interact.

Data Schema Design

You will become acquainted with the features of relational and time series databases and understand when to use each. You will learn to design a storage schema based on the characteristics of the data and their usage methods.

Environment Setup

You will install and launch InfluxDB and Grafana using Docker. You will configure the Python library for working with InfluxDB, create an access token, and set up the development environment in VS Code.

Working with Test Data

You will learn how to load test CSV files and weather data into InfluxDB using Python. You will explore potential issues during data loading and how to solve them.

Working with Air Quality Data

You will load air quality data into InfluxDB, write queries in Python, and connect Grafana to InfluxDB to create visualizations. You will set up a data source and create a dashboard for air quality analysis.

Working with External Weather API

You will get acquainted with the weather API, obtain an access key, and learn to manage time zones. You will connect an external API, load data into InfluxDB, and visualize it on a Grafana dashboard.

Working with Grafana Dashboards

You will explore the capabilities of Grafana: multi-user mode, access rights management, user and organization settings. In the final part of the project, you will create two organizations in Grafana, connect weather data and air quality data to them, applying the skills you have acquired in practice.

This course will provide you with practical skills in working with modern tools for storing and visualizing time series data, which are in demand in real-world analytics and monitoring projects.

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#1: Introduction

All Course Lessons (25)

#Lesson TitleDurationAccess
1
Introduction Demo
03:18
2
What we are going to build
02:24
3
The data set we use
02:28
4
Relational DBs vs Time Series DBs features
06:13
5
Access patterns
02:59
6
InfluxDB key concepts
05:20
7
Schema design relational database
05:16
8
Schema design InfluxDB
03:39
9
InfluxDB & Grafana Docker setup
05:54
10
Container startup & Python lib installation
03:15
11
InfluxDB Python token & VS Code setup
01:49
12
Writing test data to influxDB with Python
08:42
13
Exploring & soliving the data type problem
05:56
14
Writing in air quality data to InfluxDB
07:33
15
Query data with Python from InfluxDB
05:38
16
Grafana data source setup
05:07
17
Create Grafana dashboard for InfluxDB
05:23
18
Weather API introduction
03:30
19
Managing time zones
06:15
20
API ingestion script
12:25
21
Grafana user & rights management
04:51
22
Beijing organization setup with direct user permissions
10:01
23
Beijing organization with team permissions
03:35
24
Weather API organization setup
07:43
25
Summary
02:20

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