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.

Read more about the course

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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# Title Duration
1 Welcome! 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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