{"id":3146,"date":"2026-08-10T09:02:21","date_gmt":"2026-08-10T09:02:21","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-duckdb-the-ultimate-guide-2024-10\/"},"modified":"2026-08-10T09:02:21","modified_gmt":"2026-08-10T09:02:21","slug":"udemy-duckdb-the-ultimate-guide-2024-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-duckdb-the-ultimate-guide-2024-10\/","title":{"rendered":"Udemy \u2013 DuckDB \u2013 The Ultimate Guide 2024-10"},"content":{"rendered":"
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Descriptions<\/h2>\n

DuckDB \u2013 The Ultimate Guide, 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. 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 \u201cpip install duckdb\u201d & you\u2019re ready to go! We will dive deep into duckdb Python integration in one of the cases. 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\u2019s no problem either: Duckdb Httpfs is a package that we\u2019ll also study.<\/p>\n

If you want to run a columnar database locally on pretty big data, there isn\u2019t 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. It\u2019s 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. Pandas loads all data into memory and runs on a single thread. Hence it can\u2019t operate on larger than memory datasets and also doesn\u2019t 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!<\/p>\n

What you\u2019ll learn<\/h3>\n