shine-data-query

Run SQL queries on CSV files locally without cloud dependencies by using DuckDB as the primary analytical engine to profile schemas and execute aggregations. This approach ensures data privacy and yields quick insights directly from your local environment.

Analyzing Parquet and Excel files using local SQL analytics is fully supported. The engine detects multiple data sources, including CSV, Parquet, JSON, and Excel, profiling their schemas and data types before executing your analytical queries.

Data profiling for local datasets is the process of detecting row counts, data types, nulls, and outliers. Schema detection reads your local files to identify structural metadata before executing analytical SQL queries.

DuckDB works for data analysis with SQLite as a fallback or Python pandas via Bash execution. This configuration allows you to execute complex analytical queries like joins and window functions on local data files.

The best way to visualize SQL query results from local datasets is by executing analytical queries with optional visualization outputs. This generates charts and schema results directly when connected to the local processing engine.

Limitations of local SQL analytics on large datasets include potential processing constraints without cloud resources. It relies entirely on local compute power for executing window functions and generating visualizations from large files.