CF

Contact Tracing with Elasticsearch

1h 37m 3s
English
Paid
Updated September 2026

Contact Tracing with Elasticsearch is a 19-lesson 1 hour 37 minutes self-paced course by Andreas Kretz. This engineering project puts Elasticsearch to work on a genuinely large dataset: simulating 100,000 users making a million store check-ins, then tracing their movements through search.

Course facts

Lessons
19
Duration
1 hour 37 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Andreas Kretz
Price
Premium

This engineering project puts Elasticsearch to work on a genuinely large dataset: simulating 100,000 users making a million store check-ins, then tracing their movements through search.

Building the Dataset

Starting from an open San Francisco stores dataset of over 140,000 locations, you use Python and Pandas to narrow it to 10,000 stores, generate 100,000 fictional user profiles, and produce a million check-in events, each user averaging ten visits.

Search and Visualization

  • Uploading the prepared data to Elasticsearch and managing indexes with Kibana
  • Building a Streamlit interface with maps, filters, and tables
  • Searching by store name, ZIP code, business ID, or device ID to trace a specific user's path

The course, taught by Andreas Kretz, runs 19 lessons and recommends prior Elasticsearch and Pandas experience, plus a machine with at least 8 GB of RAM.

Additional

https://github.com/team-data-science/ElasticSearch-contact-tracing

Who teaches Contact Tracing 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 Contact Tracing with Elasticsearch?

This is a demo lesson (10:00 remaining)

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#1: Introduction
All Course Lessons (19)
#Lesson TitleDurationAccess
1
Introduction Demo
03:01
2
Setup & Goals
03:28
3
San Francisco dataset
03:49
4
Relational database vs elasticsearch
06:49
5
Preparing the dev environment
02:05
6
Prepare the SF dataset 1
09:48
7
Preparing the SF dataset 2
08:47
8
Creating 100k fake users
08:59
9
Merging 100k users with SF dataset
06:02
10
Creating app scans for users
08:22
11
Preparing Elasticsearch and loading the data
04:41
12
Creating the Streamlit app basics and folium maps
02:27
13
Page setup and querying from Elasticsearch
05:28
14
Creating free text search
04:58
15
Zip code search
02:24
16
Business_id search
04:03
17
Search by device ID & tracking people
03:38
18
Summary
03:53
19
Outlook
04:21
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Frequently asked questions

What are the prerequisites for this course?
The course does not explicitly list prerequisites, but familiarity with Python and Pandas is beneficial as they are used for creating and refining the dataset. Understanding basic concepts of Elasticsearch and data visualization tools like Streamlit will also help, although the course covers these topics in detail.
What will I build by the end of this course?
By the end of the course, you will have developed a fully functional application capable of conducting analyses on user movement data. This includes creating a dataset from a San Francisco store dataset, uploading it to Elasticsearch, and building an interactive user interface with Streamlit that allows for searching and tracking user activity by store, ZIP code, business ID, and device ID.
Who is the target audience for this course?
This course is aimed at individuals interested in data engineering and analysis, particularly those who want to learn about integrating Elasticsearch into data-driven applications. It is suitable for those who wish to explore building user interfaces for data visualization using Streamlit.
How does this course compare in depth and scope to other Elasticsearch courses?
While many Elasticsearch courses focus on search and data indexing, this course distinguishes itself by providing a hands-on project that integrates data preparation, uploading, and visualization using Streamlit. It combines practical application development with data analysis, making it more comprehensive for those interested in end-to-end solutions.
What specific tools and platforms are used in this course?
The course utilizes several specific tools and platforms including Python, Pandas, Elasticsearch, Kibana, and Streamlit. Python and Pandas are used for data manipulation, Elasticsearch for data storage and search, Kibana for index management, and Streamlit for building the user interface.
What topics are not covered in this course?
The course does not cover advanced Elasticsearch features such as machine learning or security configurations. It focuses primarily on setting up basic search and data visualization functionalities. Topics like relational database management or detailed Python programming are also not covered in depth.
What is the expected time commitment for completing this course?
Although the total runtime of lessons is not specified, the course consists of 19 lessons. Considering the hands-on nature of the project, students should expect to spend additional time outside of the video lessons working on data preparation, application development, and understanding the functionalities of the tools used.