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This is a summary of a book chapter from DuckDB in Action, published by Manning. Download the complete book for free to read the complete chapter.

Chapter 5: Exploring Data Without Persistence

Why Use a Database Without Persisting Any Data?

Analyzing data without persisting it is useful when data is stored remotely or contains sensitive information. DuckDB allows querying such data without committing to a data model, leveraging its fast query engine and SQL support.

Inferring File Type and Schema

DuckDB simplifies file processing by auto-detecting file types (CSV, JSON, Parquet) and schemas, allowing for efficient and flexible data handling without predefined formats. This capability is especially useful for exploratory data analysis.

Shredding Nested JSON

DuckDB's JSON extension can parse, unnest, and convert JSON documents into queryable tabular data. This functionality is demonstrated using JSON files of Premier League football match shots, showcasing how to create views and handle schema inconsistencies.

Translating CSV to Parquet

DuckDB can convert CSV files to Parquet format efficiently, managing memory constraints and optimizing data types for storage. This process includes inspecting and transforming data types for better query performance on the resulting Parquet files.

Analyzing and Querying Parquet Files

Parquet files provide a schema and metadata that DuckDB can leverage for optimized queries. DuckDB can retrieve and analyze this metadata, demonstrating how to explore and query the internal structure of Parquet files.

Querying SQLite and Other Databases

DuckDB can attach to and query other databases like SQLite, offering enhanced analytical capabilities. This section covers integrating SQLite databases, handling type mismatches, and creating views for smoother querying.

Working with Excel Files

DuckDB supports reading and writing Excel files (.xlsx), albeit with some limitations. The spatial extension is required for this functionality, and while reading is straightforward, writing Excel files may necessitate type conversions and formatting adjustments.


DuckDB excels at querying and processing data from various sources without needing to persist it. Its robust query engine and flexible data handling capabilities make it suitable for transforming, normalizing, and analyzing external datasets efficiently.