CF

Log Analysis with Elasticsearch

59m 42s
English
Paid
Updated September 2026

Log Analysis with Elasticsearch is a 13-lesson 59 minutes self-paced course by Andreas Kretz. Manually scrolling through logs to find one error is a bad use of a data engineer's time, and this Andreas Kretz course teaches Elasticsearch specifically as a faster way to search and monitor pipeline logs.

Course facts

Lessons
13
Duration
59 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Andreas Kretz
Price
Premium

Manually scrolling through logs to find one error is a bad use of a data engineer's time, and this Andreas Kretz course teaches Elasticsearch specifically as a faster way to search and monitor pipeline logs.

Why Elasticsearch

It's built to make searching large volumes of log data feel as easy as a web search, and the course opens by comparing its architecture to a relational database so the differences actually click.

Setting things up

You'll deploy Elasticsearch and Kibana locally with Docker, pulling images from Docker Hub and writing a Docker Compose file, then get familiar with Kibana's interface.

Getting logs in and searching them

You'll create an index, write a Python script to generate and send log events, then use Kibana to search, build visualizations, and assemble dashboards for monitoring pipeline activity.

Catching problems faster

Across 13 lessons and a companion GitHub repo, the final stretch focuses on spotting and resolving log errors quickly, aimed at cutting the downtime that comes from slow manual log review.

Additional

https://github.com/team-data-science/Elasticsearch-Log-Analysis

Who teaches Log Analysis with Elasticsearch? 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 Log Analysis with Elasticsearch?

This is a demo lesson (10:00 remaining)

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#1: Course introduction
All Course Lessons (13)
#Lesson TitleDurationAccess
1
Course introduction Demo
02:08
2
Elasticsearch fundamentals vs relational databases
05:44
3
ETL log analysis & debugging problems
03:55
4
Streaming log analysis & debugging problems
02:49
5
How to solve these problems with Elasticsearch
04:38
6
ELK stack overview
02:04
7
Elasticsearch setup limiting RAM & environment setup
04:27
8
Running Elasticsearch
04:08
9
ElasticsearchAPIs & creating an index with Python
07:32
10
Write logs (JSON) to Elasticsearch
04:47
11
Create Kibana visualizations & dashboards
09:28
12
Analyse logs by searching Elasticsearch index
04:58
13
Summary
03:04
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Frequently asked questions

What prerequisites are required for this course?
Participants should have a basic understanding of data engineering concepts and familiarity with Docker, as it is used to set up Elasticsearch and Kibana. No prior knowledge of Elasticsearch is necessary, but general experience with log analysis tools would be beneficial.
What projects will I build during this course?
The course includes practical projects such as setting up Elasticsearch and Kibana using Docker, creating and managing Elasticsearch indices with Python, and developing visual dashboards in Kibana. These projects help students apply their knowledge in real-world scenarios.
Who is the target audience for this course?
This course is designed for data engineers looking to enhance their log monitoring capabilities. It is also suitable for IT professionals who want to automate log analysis and improve error identification processes using Elasticsearch.
How does this course compare in scope to other log analysis courses?
This course specifically focuses on using Elasticsearch for log analysis, contrasting it with relational databases, and includes setting up a practical environment using Docker. It offers a concentrated approach on the ELK stack, unlike broader log analysis courses which may cover a wider range of tools without depth in Elasticsearch.
What specific tools and platforms are covered in this course?
The course covers Elasticsearch and Kibana extensively, including deploying these tools in a Docker environment. It also involves using Python to interact with Elasticsearch APIs for creating indices and writing logs.
What topics are not covered in this course?
The course does not delve into advanced Elasticsearch administration topics or cover other log analysis tools outside the ELK stack. It also does not explore in detail the data security aspects of Elasticsearch deployment.
How can the skills learned in this course benefit my career?
Mastering Elasticsearch for log analysis can improve your efficiency in monitoring and troubleshooting data pipelines. These skills are valuable for data engineering roles and can be applied to various industries that rely on log data for operational insights and error resolution.