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.
DuckDB - The Ultimate Guide
5h 56m 13s
English
Paid
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!
About the Author: Udemy
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.
Watch Online 84 lessons
0:00
/ #1: Welcome!
All Course Lessons (84)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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.