Drag & drop your files here
Max 50 files • Max file size: 500MB
Accepted formats: .csv, .tsv, .txt
Lossless data compression using DEFLATE algorithm
CSV file compression reduces the size of comma-separated value (CSV) files using lossless compression algorithms like DEFLATE, implemented in GZIP and ZIP formats. Unlike image or video compression that optimizes media content, CSV compression is pure data compression—compressing plain text by finding and eliminating repetitive patterns without altering a single character. The DEFLATE algorithm (used by GZIP, ZIP, PNG) combines LZ77 (pattern-matching) and Huffman coding (frequency-based encoding) to reduce file size 50-90% while preserving 100% of data.
CSV files contain highly repetitive data: same country names repeated thousands of times, status codes like "Active" or "Complete", identical timestamps, product categories, categorical variables. Example: A sales CSV with "United States" in 10,000 rows stores that string 10,000 times (140KB). DEFLATE stores it once plus references (2-5KB), achieving 95%+ reduction for that column alone. Typical compression: 70-80% for standard CSV files, 85-95% for highly repetitive data (logs, status codes), 50-60% for numeric-heavy data (floats, timestamps).
DEFLATE compression (GZIP/ZIP) is 100% lossless—every row, column, value, and character preserved exactly. Fundamentally different from lossy compression (JPEG, MP3) which discards data. Decompress the file and you get byte-for-byte identical CSV (verifiable with MD5 or SHA-256 file hashes). Safe for financial data, scientific datasets, database exports, analytics reports, legal records.
Browser-based convenience vs command-line tools
Technical users often compress CSV files with command-line tools (e.g., gzip filename.csv in Linux/Mac, or 7-Zip on Windows), which require terminal access and manual file handling.
This tool provides one-click compression in your browser with automatic settings and format selection—no terminal commands, no software installation, ideal for non-technical users or quick tasks.
Drag and drop CSV files onto the page, or click "Choose CSV Files" to browse. Up to 20 files at once (500MB maximum per file). Accepts CSV, TSV, and delimited text files. Sequential batch processing. Files processed locally using fflate v0.8.2 (modern high-performance compression library, 4-6x faster than older libraries)—files never leave your browser.
Three presets: Fast (Level 1, ~180 MB/s, 60-65% reduction—for large files >100MB or quick tasks), Balanced (Level 6, ~50 MB/s, 70-75% reduction—recommended default, best trade-off), Maximum (Level 9, ~15 MB/s, 75-80% reduction—highest compression, slower processing, for archival). Format options: GZIP (.csv.gz) for data pipelines (pandas can read directly: pd.read_csv('file.csv.gz')), PostgreSQL/MySQL native support, Linux/Mac environments. ZIP (.zip) for Windows users (built-in extraction), email to non-technical users. GZIP slightly better compression (1-3% smaller).
Processing time: 1-5 seconds for 10MB file, 5-15 seconds for 100MB file, 30-90 seconds for 500MB file at Balanced preset. Files get .csv.gz (GZIP) or .zip (ZIP) extension. Original CSV preserved inside compressed file (lossless). Download individually or as ZIP for batches. DEFLATE algorithm operates on file as continuous byte stream, finding and replacing repetitive patterns with shorter references—completely lossless. "Never return larger" guarantee.
Format comparison for technical vs general users
Both GZIP and ZIP use the same DEFLATE compression algorithm, but differ in file structure: GZIP compresses a single file (.csv.gz), while ZIP is an archive that can contain multiple files (.zip). For CSV compression, GZIP is the industry standard in data engineering (Python, R, SQL databases), while ZIP is more familiar to general users on Windows.
Repetitive data compresses best, random data compresses less
CSV files typically compress 50-90% depending on data characteristics—text-heavy files with repetitive patterns compress best, while numeric-heavy files with random values compress less. The DEFLATE algorithm works by finding repeated patterns: the more repetition in your data, the higher the compression ratio.
50MB CSV with 200,000 orders, repeated country names ("United States" 50K times), payment status ("Completed", "Pending"), product categories (20 unique categories) → 8MB after GZIP compression (84% reduction).
100MB CSV with 1M rows of timestamps, stock prices (floats), volumes (integers) → 45MB after GZIP compression (55% reduction). Numbers have less repetition than text.
500MB server log with 5M entries, 10 unique IP addresses, 50 unique URL paths, 5 HTTP status codes → 35MB after GZIP compression (93% reduction).
Compression level affects output size minimally (Level 1 vs 9 = 5-10% difference) but dramatically affects speed (Level 9 is 10-12x slower than Level 1). Balanced preset (Level 6) hits the sweet spot—80-90% of maximum compression at 3-4x faster speed than Maximum (Level 9).
Storage costs, data transfer, email limits, database backups
AWS S3, Google Cloud Storage, Azure Blob charge by GB stored. Compressing 1TB of CSV data to 200GB saves $16/month on S3 (standard storage at $0.023/GB). Multiply by thousands of files = significant savings.
Transferring 100MB CSV → 20MB compressed (GZIP) = 5x faster download/upload. Critical for remote teams pulling large exports from databases or analytics platforms.
Most email providers limit attachments to 25MB. A 40MB CSV compressed to 8MB fits within limits. GZIP .csv.gz files remain readable by pandas/Excel after recipient extracts.
PostgreSQL CSV exports can be 10-100GB for large tables. Compressing to 10-20GB reduces backup storage costs and speeds up disaster recovery transfers.
Serving compressed CSV files via API reduces bandwidth costs (CDN/egress charges) and improves client download speeds. Standard practice in data engineering.
Reddit discussions mention 10GB and 250MB CSV files. Compressing a 10GB CSV to 2GB (80% reduction) makes it manageable for local storage, Dropbox, or Google Drive (which have file size limits).
gzip command or pandas to_csv(compression='gzip')), but web tools offer convenience for ad-hoc exports and non-technical users.Everything about compressing CSV files
Yes, CSV files compress extremely well—typically 50-90% reduction. CSV files are plain text with repetitive data (same country names, status codes, product IDs repeated thousands of times). DEFLATE compression (used by GZIP and ZIP) identifies these patterns and replaces them with shorter references, achieving high compression ratios. Compression is lossless—100% of data is preserved. Example: A 100MB CSV with repeated country names → 15MB (85% reduction).
Two methods: (1) Compress the file using ZIP, GZIP, or online tools (50-90% reduction, lossless, recommended). (2) Reduce data inside the CSV: remove unnecessary columns, eliminate duplicate rows, round decimal precision (e.g., 10 decimals → 2), delete blank rows. Compression is easiest and preserves all data. For large files, use GZIP format which achieves better compression ratios than ZIP (1-3% smaller due to less archive overhead).
Yes, CSV files can be zipped using ZIP or GZIP compression. Both use the DEFLATE algorithm and achieve 50-90% reduction. ZIP creates an archive (.zip) that's familiar to Windows users (built-in extraction). GZIP creates a single compressed file (.csv.gz) that's standard in data engineering—Python pandas, R, PostgreSQL, and MySQL can read .csv.gz files directly without manual extraction.
No, compression with GZIP or ZIP is 100% lossless. Every character, row, column, and value is preserved exactly. Decompress the file and you get byte-for-byte identical CSV (verifiable with MD5 or SHA-256 file hashes). Safe for financial data, scientific datasets, database exports, analytics reports, and any scenario requiring data accuracy. This is fundamentally different from lossy compression (JPEG, MP3) which discards data. Text compression cannot alter a single character.
GZIP (DEFLATE algorithm) is the industry standard for CSV compression: 60-90% reduction, lossless, widely supported by data tools (pandas, R, PostgreSQL, MySQL). ZIP also uses DEFLATE and achieves identical compression. For extreme compression, LZMA (7-Zip) achieves 5-10% better ratios but is much slower and less compatible. For data engineering at scale, consider Parquet (columnar format) instead of compressed CSV—better compression + query performance.
CSV files typically compress 50-90% depending on data characteristics. Best case: server logs, categorical data, repeated strings (80-95% reduction). Average case: sales data, mixed text/numbers (70-80% reduction). Worst case: random data, UUIDs, hashes, unique values (30-50% reduction). Example: 100MB CSV with repeated country names → 15MB (85% reduction). Numeric-heavy data (floats, timestamps) compresses less due to less repetition.
Both use DEFLATE and achieve identical compression ratios (1-3% difference). GZIP (.csv.gz): better for data pipelines (pandas can read directly: pd.read_csv('file.csv.gz')), databases (PostgreSQL/MySQL native support), Linux/Mac environments, API transfers. ZIP (.zip): better for Windows users (built-in extraction), email to non-technical users, bundling multiple CSV files. Choose GZIP for technical work, ZIP for general sharing.
Use GZIP for maximum compression (Level 9 = 75-90% reduction). For very large files (>10GB): consider splitting into smaller files (easier to process), or convert to Parquet format (columnar compression, 10x better for analytics queries). This tool supports up to 500MB per file. For 10GB files, use command-line gzip or pandas to_csv(compression='gzip') for automation. Reddit discussions mention 10GB and 250MB CSV files as common pain points.
Depends on the tool. GZIP files (.csv.gz): Python pandas (pd.read_csv('file.csv.gz')), R, Excel 2019+, PostgreSQL, and MySQL can read directly without manual extraction. ZIP files (.zip): Most tools require extraction to .csv first, though some (pandas with zipfile module) can read directly. For general use, GZIP offers better direct-read compatibility for data analysis tools.
CSV compression (GZIP) is simpler, more universal, human-readable after extraction. Parquet is better for data engineering at scale: columnar format, better compression ratios (5-10x smaller), faster query performance for analytics. Use compressed CSV for: email sharing, general data transfer, maintaining compatibility with Excel/spreadsheets, human readability. Use Parquet for: large datasets (>1GB), data warehouses (BigQuery, Redshift), analytics workloads, data science pipelines. Reddit discussions suggest Parquet as alternative for very large CSV files (10GB+).
Reduce size 50-90% with lossless DEFLATE compression. Choose Fast (Level 1, 60-65%), Balanced (Level 6, 70-75%), or Maximum (Level 9, 75-80%). GZIP format for data pipelines (pandas, PostgreSQL, MySQL), ZIP for Windows users. 100% data integrity guaranteed—safe for financial, scientific, and business data. Files stay private in your browser, never uploaded.