
Using the Excel Data Model for Larger Datasets in Practice
Load millions of rows into the Excel Data Model with Power Query, relate tables, build pivots on it, keep it small and know the limits of memory and bitness.
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Load millions of rows into the Excel Data Model with Power Query, relate tables, build pivots on it, keep it small and know the limits of memory and bitness.

Compare openpyxl, calamine, pandas engines and DuckDB for reading xlsx: build test files, time runs, record peak memory and avoid common benchmark mistakes.

Find why an xlsx is large by looking inside the zip, then remove unused styles, empty formatted rows, pivot caches, images and links to shrink the file.

Analyze data beyond Excel's 1,048,576 row limit with DuckDB, SQLite, pandas in chunks or the Data Model, and bring only the summary back to Excel.

Convert a very large Excel workbook to CSV or Parquet with a streaming script, DuckDB or LibreOffice, keeping types, dates, encoding and every sheet right.

Why Excel slows down with many formulas: the calculation chain, volatile functions, whole column references and scanning lookups, and how to find the cost.

Read a very large xlsx in Python without exhausting memory: why xlsx expands, openpyxl read only mode, calamine and DuckDB, and how to measure peak memory.

Diagnose an Excel workbook that is huge, slow to calculate or too large to read in code, then fix it by size, formulas, the Data Model or a different format.

Total amounts by currency and top SKUs over 20,000 invoices in one streaming pass with Decimal, 21 MB of memory, and the same USD total as a tree-based check.

Locate an Opening and ending tag mismatch in a 28.6 MB XML file four ways: the parser, per-tag counts, record-by-record parsing and bisection, with timings.