Dockerized ETL With AWS, TDengine & Grafana

29m 12s
English
Paid
Data engineers often need to quickly set up a simple ETL script that just gets the job done. In this project, you will learn how to easily implement such an ETL on AWS: connect live data from a weather API and write it to a TDengine time-series database.
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What you will learn

The Basics of Temporal Databases

You will get acquainted with the basics of working with temporal databases, their architecture, and use cases.

Working with a Public Weather API

You will set up and explore an external weather API, and write a Python script to read data from the API.

Docker ETL on AWS

You will package the script into a Docker container and deploy it as a serverless ETL using Amazon Elastic Container Registry (ECR), Lambda, and EventBridge.

TDengine Setup

You will get familiar with TDengine, set up an instance via the TDengine cloud and configure the database.

Data Visualization in Grafana

You will visualize data from the API stored in TDengine using Grafana. You’ll learn how to connect TDengine to Grafana and create a dashboard for data analysis.

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# Title Duration
1 Quick note from Andreas before you begin 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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