SQL Data Explorer: methods and data limits

Query local records with joins, CTEs and window functions.

Use the guide alongside the working tool.

Explicit working sample

customer,order,date,product,amount
A,100,2026-01-01,Notebook,30
A,100,2026-01-01,Pen,10
B,101,2026-01-03,Notebook,25
A,102,2026-02-01,Pen,20
C,103,2026-02-02,Notebook,50
C,103,2026-02-02,Pen,15
B,104,2026-03-01,Pen,35

Query local records with joins, CTEs and window functions. SQLite SQL dialect. Input tables are named data and reference; columns import as text to preserve identifiers. Cast numeric columns explicitly. One read-only SELECT/WITH query, 5 MB input and 10,000 output rows. No network access or extensions.

Prepare the input

Query local records with joins, CTEs and window functions.

CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.

Review the method

SQLite SQL dialect. Input tables are named data and reference; columns import as text to preserve identifiers. Cast numeric columns explicitly. One read-only SELECT/WITH query, 5 MB input and 10,000 output rows. No network access or extensions.

Keep the useful output

Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.

CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.

Ready to apply this workflow?

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Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.