DuckDB - The Ultimate Guide

5h 56m 13s
English
Paid

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!

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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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