CF

DuckDB - The Ultimate Guide

5h 56m 13s
English
Paid

DuckDB - The Ultimate Guide is a 84-lesson 5 hours 56 minutes self-paced course by Udemy. Why should you learn DuckDB?

Course facts

Lessons
84
Duration
5 hours 56 minutes
Level
All levels
Language
English
Updated
Instructor
Udemy
Price
Premium

Why should you learn DuckDB? DuckDB is one of the fastest-growing technologies, with search queries increasing by 1200% over the past two years! This is not by chance: DuckDB offers powerful analytical capabilities similar to PostgreSQL but allows you to run local databases without complex configurations and costs.

Why Choose DuckDB?

  • Ease of Integration and Cost-Free: DuckDB supports a variety of integrations, such as Python, dbt, Streamlit, s3, and even Docker. Data export is available in CSV, Parquet, and JSON formats, accelerating the exchange of analysis results. Integration with Python is simple - just the command pip install duckdb!
  • Local Big Data Analysis: DuckDB allows running columnar databases for local analysis of large data volumes, making it an indispensable tool for analysts.
  • Speed: DuckDB operates 3 times faster than Pandas, allowing work with large datasets and distributing the load across all CPU cores.

This course is not just about learning DuckDB. It's a solution for fully mastering this new and rapidly growing technology!

What You Will Get After the Course:

  • Master the architecture and principles of DuckDB and learn how to create analytical solutions based on it
  • Learn to use DuckDB from Python and the command line
  • Apply DuckDB as a database for analytical applications on Streamlit
  • Master working with MotherDuck - a cloud platform for working with DuckDB
  • Learn how to use DuckDB in Docker and integrate it into the microservice architecture of analytical services
  • Master Rill - a platform based on DuckDB for creating fast dashboards and BI solutions

Join the course and find out how DuckDB can help you implement powerful analytical solutions!

Who teaches DuckDB - The Ultimate Guide? Udemy

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Udemy is the largest open marketplace for online courses on the internet. Founded in 2010 by Eren Bali, Oktay Caglar, and Gagan Biyani and headquartered in San Francisco, the company went public on the Nasdaq in 2021 under the ticker UDMY. The platform hosts well over two hundred thousand courses across software development, IT and cloud, data science, design, business, marketing, and creative skills, taught by tens of thousands of independent instructors. Roughly seventy million learners use it worldwide, and the corporate arm — Udemy Business — supplies a curated subset of that catalog to enterprise customers.

Because Udemy is a marketplace rather than a single editorial publisher, the catalog is uneven by design. The strongest material lives in the long-form, project-based courses authored by working engineers — full-stack JavaScript, React, Node.js, Python data science, AWS, Docker and Kubernetes, mobile development with Flutter and React Native, and cloud certification preparation. The CourseFlix listing under this source is the slice of that catalog that has been mirrored here for offline-friendly viewing, organized by topic and updated as new releases land. Pricing on Udemy itself swings dramatically with the site's near-permanent sales, which is why the platform is best treated as a deep reference catalog: pick instructors with strong reviews and a track record of updating their material rather than buying on the headline price alone.

What lessons are included in DuckDB - The Ultimate Guide?

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#1: Welcome!
All Course Lessons (84)
#Lesson TitleDurationAccess
1
Welcome! Demo
02:19
2
What will You Learn in this Course?
02:40
3
What is DuckDB & Why is it SO COOL?
02:40
4
What is DuckDB? (detailed)
06:13
5
Why use DuckBD?
06:08
6
What role does DuckDB play in modern Analytics World?
04:16
7
DuckDB's competition & market niche
06:58
8
When should you use DuckDB? (typical use cases)
06:27
9
Who Should Use DuckDB?
02:42
10
DuckDB Installation
07:20
11
Environment configuration
07:57
12
Getting started with DuckDB's SQL
05:49
13
Outputting SQL's results into files
08:34
14
Practice Case Description
03:03
15
Importing Data
03:16
16
DuckDB SQL Innovations: SUMMARIZE & REPLACE
05:11
17
DuckDB SQL Innovations: EXCLUDE & COLUMNS & GROUP BY ALL
05:27
18
Window Functions: the DuckDB way
04:08
19
PIVOTing in DuckDB
03:14
20
TABLE Functions in DuckDB
01:55
21
Practice Case Description
01:10
22
Downloading Data
03:33
23
Duckdb and Python: Analytics workflow - part1
06:35
24
Duckdb and Python: Analytics workflow - part2
05:02
25
Duckdb and Python: Analytics workflow - part3
04:22
26
Streamlit Introduction
01:29
27
Practice Case Description
04:41
28
Fetching Data - part1
02:33
29
Fetching Data - part2
05:00
30
Launching the App
05:39
31
Data Build Tool (dbt) Introduction
01:45
32
Practice Case Description
02:47
33
Data Walkthrough
03:27
34
Fetching Data - part1
07:23
35
Fetching Data - part2
02:33
36
Running dbt Pipeline
07:18
37
DBeaver: Amazing Database Management Tool
02:47
38
DuckDB Backward Compatibility Issue: SOLVED
06:53
39
Exploring End Result: duckdb DataWarehouse
03:00
40
What is MotherDuck?
00:57
41
MotherDuck's Features
07:44
42
Attaching a Remote Database
05:55
43
Detaching a Remote Database
01:54
44
Automating Authentication to MotherDuck Platform
03:48
45
Mother Duck Updates: Summer 2024
01:06
46
Sharing Databases
06:41
47
AI in MotherDuck: Intro
04:40
48
Querying Data with Natural Language feature
04:09
49
A More Challenging Query for AI
03:38
50
Rill Intro
01:02
51
Case End Product DEMO
03:04
52
What is Rill?
03:55
53
Case Data
04:37
54
Data Sources
05:07
55
Data Models
03:44
56
Dashboard Outlining
02:42
57
UI: Part 1
05:02
58
UI: Part 2
04:34
59
Setting up a Github repo
05:06
60
Connecting Rill Cloud to Github
02:37
61
Sharing access to Dashboard
04:03
62
Scheduling Data Refresh
05:19
63
Deleting Rill Project
01:32
64
DuckDB in Data Pipelines
04:49
65
End Result: What We'll be Working Towards
02:48
66
Dagster: Intro
02:51
67
Setting up Environment
04:17
68
Data Pipeline Walkthrough: part 1
06:59
69
Data Pipeline Walkthrough: part 2
03:59
70
Launching Pipeline & Case wrapping up
05:49
71
Case Intro
01:29
72
Business Case Architecture
06:02
73
Disclaimer: Fast Forward if needed
01:10
74
Movies Data base API
04:25
75
Dockerfile: Packaging the Project
03:47
76
Managing Python Dependencies with Poetry
03:42
77
Fetching Data from API
05:55
78
Understanding Data
03:51
79
Recommender System Codebase
06:00
80
FastAPI Microservice
02:16
81
Building Docker Image
05:55
82
Exploring Data inside DuckDB
06:25
83
Getting Recommendations
05:22
84
Wrapping Up the Case
01:12
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Frequently asked questions

What are the prerequisites for this course?
The course does not explicitly list prerequisites, but familiarity with SQL and basic database concepts would be beneficial. Early lessons cover DuckDB installation and environment configuration, indicating that some general technical knowledge is expected. Understanding analytics workflows, particularly with Python, might also be helpful as these are covered in detail throughout the course.
What will I build or achieve by the end of this course?
By the end of the course, you will have completed several practice cases and projects, including launching an app with Streamlit, running a dbt pipeline, and creating a DuckDB DataWarehouse. Additionally, you'll explore MotherDuck's features and engage in querying data using AI with DuckDB, enhancing your practical skills with real-world applications.
Who is the ideal audience for this course?
This course is ideal for data analysts, data scientists, and database administrators interested in enhancing their analytical capabilities with DuckDB. It is also suitable for developers working with Python and those interested in integrating DuckDB with other tools like Streamlit, dbt, and Rill.
How does this course compare in depth and scope to other database courses?
This course focuses specifically on DuckDB and its integration with analytical workflows. Unlike broader database courses, it delves into DuckDB's specific SQL innovations, its use in modern analytics, and its interaction with platforms like Python, dbt, and MotherDuck. While comprehensive, it targets those looking to specialize in DuckDB rather than providing a general overview of databases.
What specific tools or platforms does this course teach?
The course covers a range of tools and platforms including DuckDB, Streamlit, Data Build Tool (dbt), and Rill. It also explores MotherDuck and its features, providing insights into attaching and detaching remote databases and using AI for querying data. These tools are integral to modern data analytics workflows, as demonstrated in various practice cases.
What topics are not covered in this course?
The course does not cover advanced topics outside the scope of DuckDB, such as in-depth discussions on other databases like PostgreSQL or MySQL. It focuses on DuckDB's unique capabilities and its role in analytics, without delving into broader database administration or tuning practices for other systems.
How much time should I expect to commit to this course?
Though the total runtime of the course is listed as 00:00:00, which seems to be a placeholder, with 84 lessons covering various complex topics, a significant time commitment is likely required. Students should allocate time for both watching lessons and engaging in hands-on practice with DuckDB, as well as integrating it with tools like Streamlit and dbt.