CF

Choosing Data Stores

1h 25m 31s
English
Paid
Updated September 2026

Choosing Data Stores is a 16-lesson 1 hour 25 minutes self-paced course by Andreas Kretz. Before you can design a data platform, you need to pick the right storage for the job — and Andreas Kretz built this course entirely around that one decision.

Course facts

Lessons
16
Duration
1 hour 25 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Andreas Kretz
Price
Premium

Before you can design a data platform, you need to pick the right storage for the job — and Andreas Kretz built this course entirely around that one decision.

Warehousing fundamentals

You'll start with OLTP vs. OLAP systems and their different use cases, then cover ETL and ELT and how each shapes which data warehouse makes sense.

Relational databases

A step-by-step look at choosing a relational data store, including CRUD operations, ACID principles, and how specific database management systems stack up.

NoSQL databases

Document, columnar, temporal, and search databases, along with the read/write speed trade-offs that should actually drive your choice.

Warehouses vs. lakes

Across 16 lessons, the course closes with a direct comparison of data warehouses and data lakes and where each one wins — groundwork that feeds directly into deeper, technology-specific courses later on.

Who teaches Choosing Data Stores? 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 Choosing Data Stores?

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#1: Introduction
All Course Lessons (16)
#Lesson TitleDurationAccess
1
Introduction Demo
02:10
2
OLTP vs OLAP
07:35
3
ETL vs ELT
05:46
4
Data Stores Ranking
04:06
5
How to Choose Data Stores
08:12
6
Relational Databases
06:35
7
NoSQL Basics
10:40
8
Document Stores
05:57
9
Time Series Databases
05:01
10
Search Engines
04:19
11
Wide Column Stores
04:23
12
Key Value Stores
05:00
13
Graph Databases
01:06
14
Data Warehouses
05:33
15
Data Lakes
07:11
16
Conclusion
01:57
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Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling, it's beneficial to have a foundational understanding of data engineering concepts. Familiarity with databases and data processing techniques will help you grasp the topics, such as OLTP and OLAP systems, ETL and ELT processes, and various data storage solutions including relational databases and NoSQL systems.
What types of data storage solutions are covered in the course?
The course covers a range of data storage solutions, including relational databases, NoSQL databases, data warehouses, and data lakes. Specific topics include document stores, time series databases, search engines, wide column stores, key-value stores, and graph databases. These lessons help you understand the appropriate scenarios for each type and how to integrate them into data architectures.
Who is the target audience for this course?
This course is designed for aspiring and current data engineers who need to make informed decisions about data storage solutions. It is also suitable for software engineers and IT professionals interested in understanding different data storage architectures and enhancing their capabilities in data platform creation and pipeline building.
How does the depth of this course compare to other courses on data storage?
This course provides a broad overview of various data storage solutions, focusing on when and how to use them effectively. It covers foundational concepts such as OLTP vs OLAP systems and ETL vs ELT processes. Future courses will delve deeper into specific technologies within each storage category, building on the foundational knowledge gained here.
What specific tools or platforms are discussed in the course?
While the course provides an overview of the types of data storage solutions, such as relational and NoSQL databases, it does not focus on specific tools or platforms. The emphasis is on understanding the characteristics and use cases of each type of data store, rather than the specifics of tools like PostgreSQL or MongoDB.
What topics are not covered in this course?
The course does not cover specific database management systems or advanced configuration and optimization of data stores. It also does not delve into the implementation details of specific technologies within each storage type, which will be addressed in future courses that focus on individual technologies.
How can the knowledge gained from this course benefit my career in data engineering?
By completing this course, you will gain a solid understanding of different data storage solutions and their appropriate use cases, enhancing your ability to design effective data architectures. This knowledge is crucial for data engineers tasked with building and optimizing data platforms and pipelines, and it lays the groundwork for further specialization in data storage technologies.