CF

Dockerized ETL With AWS, TDengine & Grafana

29m 12s
English
Paid
Updated September 2026

Dockerized ETL With AWS, TDengine & Grafana is a 16-lesson 29 minutes self-paced course by Andreas Kretz. Sometimes a data engineering job just needs a simple ETL script that works.

Course facts

Lessons
16
Duration
29 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Andreas Kretz
Price
Premium

Sometimes a data engineering job just needs a simple ETL script that works. Andreas Kretz builds exactly that in this project: pulling live weather data and writing it into a time-series database, packaged to run serverless on AWS.

What You Build

  • A Python script that reads real-time data from a public weather API
  • A Docker container for that script, deployed as a serverless job using Amazon ECR, Lambda, and EventBridge
  • A TDengine instance, set up through TDengine Cloud, tuned to store the incoming time-series data
  • A Grafana dashboard connected to TDengine for visualizing the results

Skills Covered

Along the way, the course introduces the basics of temporal databases and their typical use cases, giving you hands-on experience with a small but complete Dockerized ETL pipeline you can adapt for your own real-time data sources.

Additional

https://github.com/team-data-science/dockerized-etl-aws-tdengine

https://github.com/team-data-science/dockerized-etl-aws-tdengine/blob/main/src/writer_json.py

Who teaches Dockerized ETL With AWS, TDengine & Grafana? Andreas Kretz

Andreas Kretz thumbnail

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

What lessons are included in Dockerized ETL With AWS, TDengine & Grafana?

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#1: Quick note from Andreas before you begin
All Course Lessons (16)
#Lesson TitleDurationAccess
1
Quick note from Andreas before you begin Demo
00:44
2
Introduction
01:27
3
Setup Of The Project
02:53
4
Time Series Data Basics
02:21
5
Big Pros Of Timeseries Databases
02:07
6
About TDengine
01:23
7
Setup Weather API
01:05
8
Code query API
02:42
9
TDengine Setup
03:05
10
Connect Python To TDengine
01:51
11
Lambda Docker Container & Push To ECR
01:56
12
AWS Setup
01:37
13
Create Lambda Function Using Docker image
01:05
14
Schedule Function With EventBridge
01:26
15
Cloud Watch Lambda Events
00:28
16
Grafana Setup
03:02
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Frequently asked questions

What prerequisites are needed before enrolling in this course?
Before enrolling, students should have a basic understanding of Python scripting and familiarity with cloud computing concepts. Some experience with Docker and AWS services would be beneficial but not mandatory, as the course guides through setting up Docker containers and deploying them on AWS.
What kind of project will I work on during the course?
The course focuses on building a Dockerized ETL project that involves connecting live weather data from a public API, storing it in a TDengine time-series database, and visualizing it using Grafana. You'll learn to automate these processes using AWS services like Lambda and EventBridge.
Who is the target audience for this course?
This course is designed for data engineers and developers who want to enhance their skills in data integration and visualization using modern tools and platforms. It is also suitable for those interested in learning about Dockerized ETL processes on AWS.
How does the depth of this course compare to similar courses?
The course offers a hands-on approach with 16 lessons, focusing on practical implementation of ETL processes using Docker, AWS, TDengine, and Grafana. Unlike theoretical courses, it emphasizes real-world application and integration of these technologies.
Which specific tools and platforms are covered in the course?
The course covers Docker for containerization, AWS for deploying serverless ETL processes (using Lambda, ECR, and EventBridge), TDengine for managing time-series data, and Grafana for data visualization. It also involves working with a public weather API and Python scripting.
What topics are not covered in this course?
The course does not cover advanced programming techniques, in-depth AWS architecture beyond the services used, or detailed Grafana dashboard customization. It remains focused on the integration and deployment of ETL processes using the specified tools.
What is the expected time commitment for completing the course?
The course comprises 16 lessons, each designed to provide hands-on experience. While the total runtime is not specified, students should allocate time for both learning the material and completing practical exercises, typically suggesting a commitment of several hours per week.