Parquet vs CSV vs Excel
Complete format comparison for data professionals. Performance, file sizes, and when to use each format.
Quick Format Guide
| Format | Best For | File Size | Speed | Max Rows | Compatibility |
|---|---|---|---|---|---|
| Excel (.xlsx) | Formulas, formatting, reports | Medium | Slow | 1M max | ✅ Universal |
| CSV (.csv) | Simple data, compatibility | Large | Medium | Unlimited | ✅ Universal |
| Parquet (.parquet) | Big data, fast queries, analytics | Small | Very fast ⚡ | Unlimited | ⚠️ Data tools |
The Winner for Big Data: Parquet 🏆
When you have:
- ✅10M+ rows
- ✅Need fast filtering/searching
- ✅Repeated analysis on same data
- ✅Large files (multi-GB)
Parquet is 10-100x faster and 80-90% smaller ⚡
Detailed Format Breakdown
Parquet (.parquet) ⭐⭐⭐⭐⭐
For Big Data
Pros
- ✅Columnar storage = 10-100x faster queries (especially column operations)
- ✅80-90% smaller than CSV (built-in compression, typically 5-10x)
- ✅Preserves schema (data types, column names)
- ✅Industry standard (Apache open-source)
- ✅Billions of rows (no practical limit)
Cons
- ⚠️Not human-readable (binary format)
- ⚠️Requires tools (can't open in Notepad like CSV)
- ⚠️Excel can't open (need conversion tool like Diwadi)
Performance Example (100M rows)
CSV:
- • File size: 20 GB
- • Open time: 5 minutes
- • Filter time: 3 minutes
Parquet:
- • File size: 4 GB (80% smaller)
- • Open time: 10 seconds (30x faster)
- • Filter time: 2 seconds (90x faster)
When to Use:
- • 10M+ rows
- • Fast filtering/searching needed
- • Large file sizes (multi-GB CSVs)
- • Repeated analysis (load once, query many times)
- • Data engineering workflows
CSV (.csv) ⭐⭐⭐⭐
For Compatibility
Pros
- ✅Universal compatibility (Excel, Sheets, pandas, SQL, etc.)
- ✅Human-readable (open in text editor)
- ✅Simple format (just comma-separated values)
- ✅Unlimited rows (no hard limit)
Cons
- ❌Large files (no compression)
- ❌Slow queries (must scan entire file)
- ❌No schema (all values are text, must infer types)
- ❌Encoding issues (UTF-8, ASCII problems)
When to Use:
- • Need universal compatibility
- • Sharing with others (everyone can open CSV)
- • Simple data (no nested structures)
- • 1M-100M rows (if speed isn't critical)
Excel (.xlsx) ⭐⭐⭐
For Business
Pros
- ✅Formulas (calculations, VLOOKUP, pivot tables)
- ✅Formatting (colors, fonts, borders)
- ✅Charts/graphs (visualizations)
- ✅Universal in business (everyone has Excel)
Cons
- ❌1M row limit (hard ceiling, can't exceed)
- ❌Slow performance (>100K rows = freeze/crash)
- ❌Large file sizes (worse than CSV for big data)
When to Use:
- • <1M rows
- • Need formulas, formatting, charts
- • Standard business reports
- • Sharing with non-technical users
When to Convert:
- • File >1M rows → Convert to CSV or Parquet
- • Slow performance → Convert to Parquet for analysis
- • Need faster queries → Convert to Parquet
Performance Comparison (10M rows)
| Operation | Excel | CSV | Parquet |
|---|---|---|---|
| File Size | ❌ Can't create | 2.5 GB | 500 MB (80% smaller) |
| Open Time | ❌ Can't open | 10 sec | 2 sec (5x faster) |
| Filter Rows | ❌ N/A | 30 sec | <1 sec (30x faster) ⚡ |
| Search | ❌ N/A | 25 sec | <1 sec (25x faster) ⚡ |
| Sort | ❌ N/A | 60 sec | 2 sec (30x faster) ⚡ |
| Sum Column | ❌ N/A | 15 sec | <1 sec (15x faster) ⚡ |
Winner: Parquet (10-100x faster for queries, 80-90% smaller)
How Parquet Works (Columnar Storage)
Row-based formats (CSV, Excel)
Row 1: Name, Age, City, Salary
Row 2: Alice, 30, NYC, 75000
Row 3: Bob, 25, LA, 65000
Problem: To filter by Salary, must scan entire file (all columns)
Columnar format (Parquet)
Column Name: Alice, Bob, Charlie, ...
Column Age: 30, 25, 35, ...
Column Salary: 75000, 65000, 80000, ...
Advantage: To filter by Salary, only read Salary column (10-100x faster for column operations!)
Conversion Workflows
Excel → Parquet
Use case: Excel file is slow or hitting row limits
1.Export Excel to CSV
2.Open CSV in Diwadi
3.Convert to Parquet (one click)
4.Analyze in Parquet (100x faster)
5.Export results back to Excel
CSV → Parquet
Use case: Large CSV file (>1 GB), slow to query
1.Open CSV in Diwadi
2.Convert to Parquet (automatic compression)
3.Work with Parquet (fast queries)
4.Convert back to CSV/Excel when needed
Result: 80-90% smaller file, 10-100x faster queries
CSV to Parquet Converter →Parquet → Excel
Use case: Share Parquet analysis with business users
1.Open Parquet in Diwadi
2.Filter/analyze data
3.Export results to Excel (for sharing)
Note: Excel has 1M row limit. Use CSV for large exports.
Parquet to Excel Converter →Recommendations by Use Case
For Excel Users (Hitting Limits)
Migrate to Parquet workflow:
- Export Excel to CSV (or use CSV export from source)
- Convert CSV to Parquet
- Analyze in Parquet (10-100x faster for analytical queries)
- Export results to Excel (for business users)
Savings: 80-90% smaller files, 10-100x faster queries, no row limits
For Data Analysts (Working with Big Data)
Use Parquet as standard:
- • All datasets >10M rows → Parquet
- • Keep CSV for compatibility/sharing
- • Export to Excel only for final reports
Tools: Diwadi (GUI), pandas (Python), DuckDB (SQL)
For Business Users (Sharing Reports)
Use Excel for final output:
- • Work with Parquet behind the scenes (fast)
- • Export summaries/filtered results to Excel (for sharing)
- • Keep original data in Parquet (no row limits)
Frequently Asked Questions
What is Parquet format? ▼
Parquet is a columnar storage format developed by Apache. It's optimized for big data analytics - 10-100x faster queries (especially column operations) and 80-90% smaller files than CSV.
Why is Parquet so much faster? ▼
Columnar storage - Parquet stores data by column (not row). When filtering, it only reads relevant columns, not the entire file. This makes column-specific operations 10-100x faster than row-based formats like CSV or Excel.
Can Excel open Parquet files? ▼
No. Parquet is a specialized binary format that Excel cannot open. Use Diwadi to convert Parquet → Excel/CSV for sharing with Excel users.
Will converting to Parquet lose data? ▼
No! Parquet conversion is lossless - all data is preserved perfectly. However, Excel-specific features (formulas, formatting, charts) are not stored in Parquet since it's a pure data format.
Is Parquet only for data scientists? ▼
No! Anyone can use Parquet with GUI tools like Diwadi. No coding required. Parquet is becoming the standard format for anyone working with large datasets (10M+ rows).
When should I use CSV vs Parquet? ▼
Use CSV for: universal compatibility, <10M rows, sharing with others. Use Parquet for: big data (>10M rows), need speed (10-100x faster), repeated analysis, large file sizes (multi-GB CSVs).
How much smaller is Parquet? ▼
Typically 80-90% smaller than CSV (5-10x compression) due to columnar compression. A 10GB CSV might become a 2GB Parquet file. Some datasets compress even more.
Can I edit Parquet files? ▼
Yes! Tools like Diwadi let you filter, clean, and modify Parquet files with a GUI. You can also convert to CSV/Excel, edit, and convert back to Parquet.
Is Parquet an industry standard? ▼
Yes! Used by Google, Amazon, Microsoft, Netflix, Uber, and all major data companies. It's the Apache open-source standard for big data storage.
Should I convert all my Excel files to Parquet? ▼
Only if they're large (>1M rows) or performance matters. Keep using Excel for business reports, formatted dashboards, and files with formulas. Use Parquet for data analysis on large datasets.
The 2025 Data Format Strategy
- 1.Use Excel for business reports (<1M rows, need formatting/formulas)
- 2.Use CSV for compatibility (sharing, universal access)
- 3.Use Parquet for big data (>10M rows, need speed)
Best workflow: Source data → Parquet (fast analysis) → CSV/Excel (sharing)
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