Convert Excel to Parquet
Unlock 10-100x faster performance and 80% smaller files. Complete guide with benchmarks.
Why Convert Excel to Parquet?
10-100x Faster
Parquet's columnar storage makes filtering, searching, and sorting 10-100x faster than CSV exports. Especially powerful for column-specific operations.
Example (1M rows):
❌ Excel: Can't handle
CSV: 30 sec filter
Parquet: <1 sec filter ⚡
80% Smaller
Parquet's built-in compression reduces file sizes by 80-90% compared to CSV exports. Columnar compression works exceptionally well on similar values.
Example (500K rows):
Excel: 50 MB
CSV Export: 100 MB
Parquet: 15 MB (85% smaller) 💾
No Row Limits
Excel has a hard limit of 1,048,576 rows. Parquet handles billions of rows with no limits. Perfect for big data analysis.
Row Capacity:
Excel: 1M max (hard ceiling)
CSV: Unlimited (but slow)
Parquet: Billions (and fast) 🚀
Bottom Line
Convert Excel to Parquet when: File has >100K rows, Excel is slow, or you need repeated queries. Keep Excel for reports with formatting/formulas.
How to Convert Excel to Parquet (3 Methods)
Method 1: Using Diwadi (Recommended) 🏆
Free • No coding • Fastest method
Step-by-Step:
Export Excel to CSV
Open your Excel file → File → Save As → CSV UTF-8 (.csv)
Note: If your file has >1M rows, you can't open it in Excel. Export directly from your data source as CSV.
Open CSV in Diwadi
Drag and drop your CSV file into Diwadi (or File → Open)
Convert to Parquet
Click "Convert to Parquet" button
Compression options:
- • Snappy Snappy (recommended): Balanced speed/size, 70-80% smaller
- • Gzip: Gzip: Maximum compression, 80-90% smaller, slower
- • None: None: Fastest, but larger files (not recommended)
Done! Work with Parquet
Your Parquet file is ready. Filter, search, and analyze at lightning speed.
Time to Convert:
- • 100K rows: ~5 seconds
- • 1M rows: ~15 seconds
- • 10M rows: ~2 minutes
- • 100M rows: ~10 minutes
Method 2: Using Python pandas
Free • Requires coding • For automation
# Install required libraries
pip install pandas pyarrow openpyxl
# Python code
import pandas as pd
# Read Excel file
df = pd.read_excel('data.xlsx')
# Convert to Parquet
df.to_parquet('data.parquet', compression='snappy')
print("Converted", len(df), "rows to Parquet")
✅ Pros:
- • Automation-friendly (scripts)
- • Batch conversion (process folders)
- • Unlimited customization
❌ Cons:
- • Requires Python knowledge
- • Setup time (install libraries)
- • No GUI
Method 3: Online Conversion Tools (Not Recommended) ❌
Various pricing • Privacy concerns • Slow uploads
Why We Don't Recommend:
- ❌ Privacy risk: Upload sensitive business data to third-party servers
- ❌ Slow uploads: 1GB file takes 10-30 minutes to upload (plus conversion time)
- ❌ File size limits: Most tools limit to 100MB-1GB
- ❌ Costs money: Free tiers are very limited, paid plans $10-50/month
- ❌ Requires internet: Can't work offline
Better option: Use Diwadi (100% local, instant, free, no upload). Desktop tools are 25x faster than online tools.
Performance Comparison: Excel vs CSV vs Parquet
| Metric | Excel (.xlsx) | CSV Export | Parquet |
|---|---|---|---|
| File Size (500K rows) | 50 MB | 100 MB | 15 MB (85% smaller) |
| Max Rows | 1M (hard limit) | Unlimited | Billions |
| Open Time (500K rows) | 30 sec | 5 sec | 1 sec (5x faster) |
| Filter Operation | 15 sec | 10 sec | <1 sec (15x faster) ⚡ |
| Search/Find | 20 sec | 15 sec | <1 sec (20x faster) ⚡ |
| Sort Operation | 25 sec | 30 sec | 2 sec (12x faster) ⚡ |
Winner: Parquet
85% smaller files, 10-20x faster queries, no row limits. The one-time conversion cost (5-15 sec) pays off immediately.
When Should You Convert Excel to Parquet?
✅ Convert to Parquet When:
- • File has >100K rows (Excel performance degrades)
- • Excel is slow (crashes, freezes, takes minutes to open)
- • Need fast queries (filtering, searching, sorting)
- • Repeated analysis (load once, query many times)
- • File is >1M rows (Excel can't open it)
- • Working with big data (10M-1B+ rows)
- • Need to save disk space (80% smaller files)
- • Data analysis workflow (not editing formulas)
❌ Keep Using Excel When:
- • File has <10K rows (Excel works fine)
- • Need formulas (SUM, VLOOKUP, calculations)
- • Need formatting (colors, fonts, borders)
- • Need charts/graphs (visualizations)
- • Creating business reports (dashboards)
- • Sharing with non-technical users (everyone has Excel)
- • One-time analysis (not worth converting)
- • File is small and fast (no performance issues)
Best of Both Worlds: Hybrid Workflow
You don't have to choose between Excel and Parquet. Use both for their strengths:
Source Data → Parquet
Convert raw Excel/CSV data to Parquet for fast analysis (filtering, searching, aggregating)
Analysis in Parquet
Work with Parquet in Diwadi (lightning fast queries, no row limits, 100% private)
Results → Excel
Export filtered results or summaries back to Excel for reports/sharing (with formulas, formatting, charts)
Example Use Case:
Scenario: Analyze 5 million rows of sales data to find top customers
- Export sales data from database as CSV (Excel can't handle 5M rows)
- Convert CSV to Parquet in Diwadi (takes 30 seconds)
- Filter Parquet for sales > $10,000 (instant query, returns 50K rows)
- Export filtered 50K rows to Excel (now small enough for Excel)
- Create pivot tables, charts, formatted report in Excel
- Share Excel file with team
Result: Fast analysis on big data + Beautiful Excel reports for stakeholders
Frequently Asked Questions
Why convert Excel to Parquet? ▼
Will I lose data when converting Excel to Parquet? ▼
Can I convert Parquet back to Excel? ▼
How long does Excel to Parquet conversion take? ▼
What happens to Excel formulas in Parquet? ▼
Can Excel natively open Parquet files? ▼
Should I convert all my Excel files to Parquet? ▼
Is Parquet only for data scientists? ▼
How much disk space will I save? ▼
Can I still use Excel after converting to Parquet? ▼
Start Converting Excel to Parquet Today
Benefits:
- ✓ 10-100x faster queries (filter, search, sort)
- ✓ 80-90% smaller files (save disk space)
- ✓ No row limits (billions of rows)
- ✓ 100% private (no cloud upload)
- ✓ Free (no subscriptions)