December 2024
Intermediate to advanced
308 pages
6h 43m
English
Content preview from DuckDB: Up and Running
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Chapter 1. Getting Started with DuckDB
When it comes to data analytics, pandas is often the go-to library for many developers. Recently, Polars has emerged as a faster and more efficient alternative for handling DataFrames. However, despite the popularity of these libraries, SQL (Structured Query Language) remains the most widely recognized and used language among developers. If your data is stored in a database that supports SQL, using SQL to query and manipulate that data is often the most intuitive and effective approach.
While Python has become the dominant language in data science—particularly for working with data in tabular formats through DataFrame objects—SQL continues to be the universal language of data. Given that most developers are already comfortable with SQL, wouldn’t it be more efficient to use SQL directly for data manipulation?
This is where DuckDB shines. DuckDB was initially conceptualized in 2018 as an OLAP (online analytical processing) database optimized for fast analytical queries. Its aim was to bridge the gap between fully-fledged database systems and the simplicity of embedded DBs like SQLite, but with a focus on analytical rather than transactional workloads. The first stable release of DuckDB was in 2019, and its ease of integration with Python and R made it a very popular choice among the data science and analytics communities. While DuckDB is open source, DuckDB Labs was founded in 2021 to provide commercial support and further development. To bring ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.