DuckDB - The Ultimate Guide

Description

Why should I learn DuckDB?

1. + 1200% of searches in the last 2 years
   Its popularity is growing RAPIDLY!

   
   

2. Data lakes and bulky Big Data Infrastructure (like Apache Hadoop & Spark) are not optimal solution to every Data problem

   DuckDB is an awesome solution for running a database very similar to PostgreSQL, but with HUGE Analytical Capabilities, locally without any fuss

   
   

3. 100% free & supports dozens of various integrations

   duckdb Python, duckdb dbt, duckdb Streamlit, duckdb s3 & wasm & Docker + many more: you can almost anything with it. Additionally, you can easily do data exports: duckdb csv, duckdb parquet, duckdb json are all ways to share your analysis results in no time! Python integration is as easy as doing "pip install duckdb" & you're ready to go! We will dive deep into duckdb Python integration in one of the cases.

   
   

4. Ease of use
   Rather than having a PostgreSQL/Mariadb for each developer on the team, you can setup configuration to spawn an in memory instance of DuckDB. If you need to fetch data from the Internet, it's no problem either: Duckdb Httpfs is a package that we'll also study.
   

5. Local Analysis of BigData
   If you want to run a columnar database locally on pretty big data, there isn't really anything else like it. You could instead run PySpark locally but that would be much more of a headache. Duckdb Pivot can even help you create Spreadsheet-like tables.
   

6. Easy to learn after SQLite
   It's a step forward to Analytics field from SQLite. DuckDB performs great when running aggregate queries on limited columns whereas SQLite works great when fetching one or more rows using filters. In the Course we will compare and contrast duckdb vs Sqlite and duckdb vs Clickhouse.
   

7. 300%+ faster than Pandas
   Pandas loads all data into memory and runs on a single thread. Hence it can't operate on larger than memory datasets and also doesn't use all of your CPU  cores. Whereas DuckDB can operate on datasets larger than memory. Moreover, it can distribute load across all the CPU cores. All that using SQL language by default!

This Course is not just a duckdb tutorial: it's a packaged solution to master this new & rapidly growing technology.

Expected Outcomes

After this Course:

• You will learn how to Architect & Implement Analytics Solutions that use duck db as the database

• You will learn the underlying principles that make DuckDB so fast on any machine (Theory)

• You will understand how DuckDB is different from other data bases: both Analytical (Clickhouse, Redshift, Cassandra) and OLTP (PostgreSQL, SQLite)

• You will learn to work with DuckDB from Python environment (Practice)

• You will learn to work with DuckDB from CLI (command line) environment (Practice)

• Use DuckDB as a backend database for your Streamlit Python Analytics Apps (Practice)

• Use a DuckDB dbt (Data Build Tool) combo to streamline Analytics Data Warehouse development (Practice)

• You will learn to work in MotherDuck: a Cloud-native environment (SaaS) for duck db (Practice). You can think of it as DuckDB GUI that you might miss in CLI

  
  

What's inside

• Video lectures (with interactive annotations)

• PDFs with Practice Cases Outlines

• Demo Resources

• Fully packaged code base for Practice Projects

• Full lifetime access with all future updates

• Certificate of course completion

• 30-Day Money-Back Guarantee

The course isn't static! I collect students' feedback and work on improving it

Digital assets used:

-Image from freepik with free licence from freepik dot com "Free vector gradient dynamic blue lines background"

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