DuckDB - The Ultimate Guide
5h 56m 13s
English
Paid
Course description
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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/ #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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