How can you open a Parquet file without Python?
Use a tool with native Parquet support, such as an appropriate Power BI import workflow or Data Analysis App’s supported file upload. Inspect the schema, row coverage, and nested fields before analysis. Keep the original file: conversion to CSV can remove type information, flatten structures, and expose identifiers to spreadsheet conversion.
Key numbers
- Columnar
- how Parquet organizes data
- Typed
- schema carried with the file
- CSV
- a conversion with trade-offs
Key takeaways
- A small compressed file can require much more working memory.
- Confirm nested fields and logical types survive the chosen import.
- Do not assume every Excel Power Query host includes a Parquet connector.
A colleague sends a .parquet file instead of a workbook. Renaming it to .csv will not make it a spreadsheet: Parquet is a binary columnar format with a schema. Choose an importer that understands the format before trying to inspect its contents.
A no-code workflow can be practical for a one-off analysis. The important checks are the same as in a scripted workflow: preserve types, establish the row grain, confirm coverage, and make unsupported structures visible.

Section 01
Understand what the file contains
Parquet stores data by columns and supports compression and typed values. Its schema can preserve distinctions that a plain text export does not declare. That is useful for repeated analysis, but a readable table preview is still only one representation of the underlying data.
Ask the sender for a data dictionary, expected row count, reporting period, and control totals. A field called amount is incomplete without its unit and currency. A timestamp is incomplete without the intended time-zone interpretation. The format preserves structure; it does not supply every business definition.
Section 02
Choose a supported import route
Microsoft documents a Parquet connector for Power BI semantic models and Power Query Online, with capabilities depending on the host. Do not assume that an Excel installation includes every connector described under the broader Power Query name. Check the product's current supported connector list and the actual Get Data options available to you.
Data Analysis App also lists Parquet as a supported input. Use a file you are authorized to upload and review the current upload limits. A supported extension does not promise that every codec, nested structure, or unusually large file will work in every workflow.
Section 03
Inspect types before summarizing
Check whether IDs arrive as exact strings, numeric measures remain numeric, and dates and timestamps have the intended meaning. Logical decimal types need appropriate precision and scale; an identifier stored as an integer may still require text treatment for a later spreadsheet export.
Look for lists, maps, and nested records. Expanding a list can turn one source row into several output rows. That changes the grain and can duplicate a parent-level amount. Preserve an original row key and reconcile counts and totals after any flattening step.
Section 04
Plan for memory and file partitions
Compressed file size is not the same as the memory required to decode and analyze it. A compact file with many rows or long strings can exceed an interactive tool's limits. If the import fails, ask for a narrower period, a selected set of columns, or a source-generated aggregate that preserves the question you need to answer.
A directory of Parquet files can also represent one partitioned dataset. Opening one file does not establish that you have the complete table. Request a manifest or an authoritative row count, and determine whether partition information such as date or region is encoded in folder paths rather than inside each file.
Section 05
Validate a small sample and a full control
Inspect sample rows across the period and across important categories, not only the first screen. Check missing-value counts, duplicate business keys, minimum and maximum dates, and a known numeric total. Compare accepted rows with the sender's expected count.
- Preserve the original binary file unchanged.
- Record which partitions or files were included.
- List fields that could not be interpreted or flattened safely.
- Keep missing values distinct from numeric zeros.
If a control total fails, stop before presenting a polished chart. The mismatch may indicate a partial dataset, repeated partitions, an unexpected nested field, or a unit conversion. A plausible visual shape is not evidence that the import is complete.
Section 06
Convert to CSV only for a defined reason
CSV can be convenient for a recipient who needs a flat table, but it does not carry Parquet's type declarations. Nested structures need an explicit flattening policy, timestamps need a stated convention, and identifiers need safe import instructions. Use a new filename and retain the Parquet source.
The identifier-preservation guide covers spreadsheet conversion risks. After conversion, compare row counts, exact identifier strings, missingness, and numeric control totals. Successful export is only one step; a controlled round trip shows whether the recipient can recover the intended values.
Section 07
Analyze within the evidence available
Prepare a focused question and a short schema note, then start a project. Review the detected columns, coverage, and limitations before using the findings or purchasing downloadable outputs. If the dataset is incomplete or a structure is unsupported, resolve that gap with the source owner instead of treating an attractive report as proof that nothing was lost.
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Evidence
Sources
- Apache Parquet (2026). Apache Parquet: format overview.
- Apache Parquet (2026). Apache Parquet: data types.
- Microsoft (2026). Microsoft: Parquet connector and supported products.