CSV too large to share? Compress it 80–90% by converting to Parquet.
Reduce CSV File Size to Fit Any Platform Limit
Email rejects files over 25MB. GitHub hard-limits at 100MB. Jira and Confluence cap uploads at 10MB. If your CSV is bouncing back or failing to upload, Diwadi can convert it to Parquet, filter rows, remove duplicates, and drop unnecessary columns — all locally on your computer, with no uploads required.
Platform File Size Limits for Data Files
| Platform | File Size Limit | Notes |
|---|---|---|
| Email / Gmail | 25 MB | Total per email. Even a modest dataset with many columns can easily exceed this. |
| Slack (Free) | 1 GB | Generous limit, but large CSVs slow Slack and are hard for recipients to open inline. |
| GitHub | 100 MB hard limit, 50 MB warning | Files over 50 MB trigger a warning; over 100 MB are rejected. Git LFS required for large datasets. |
| Google Drive | 5 TB per file | No practical per-file limit, but large CSV uploads are slow and previews fail above a few MB. |
| Jira / Confluence | 10 MB | Attachment limit per file. CSV datasets rarely fit once they grow past a few thousand rows. |
| AWS S3 | 5 GB single upload | Single PUT request limit. Multipart upload required for files above 5 GB. |
| Kaggle Datasets | 100 GB | Large allowance, but Parquet datasets load 5–10x faster in notebooks and are preferred. |
Strategy 1: Convert CSV to Parquet (80–90% Smaller)
Parquet is a columnar binary format designed for exactly this problem. A 500 MB CSV often becomes a 50–100 MB Parquet file with zero data loss. The recipient can convert it back to CSV in seconds if needed.
80–90% Smaller File Size
Parquet uses columnar storage and built-in compression (Snappy or GZIP). Repeated values, long strings, and numeric columns compress dramatically compared to CSV's row-based text format.
No Data Loss
Every row, every column, every value is preserved exactly. Parquet also stores column types (integer, float, date) so the recipient gets correct data types automatically, not everything as strings.
Recipient Can Convert Back
Any Python/pandas user, DuckDB, Spark, or Diwadi can convert Parquet back to CSV in seconds. Parquet is the standard format for modern data teams.
Faster to Query
Parquet's columnar layout means tools can read only the columns they need, making queries on large datasets 10–100x faster than scanning a full CSV.
Strategy 2: Filter to Only Relevant Rows
Don't send 10 million rows when the recipient needs 50,000. Filtering before sharing is often the fastest way to shrink a CSV — and it makes the file more useful for the recipient.
- Filter by date range: last 30 days, last quarter, or the specific period being analyzed
- Filter by region, team, product line, or customer segment relevant to the recipient
- Filter by status: completed orders only, active accounts only, flagged records only
- Filter out test records, internal accounts, or staging data before sharing externally
- A 10M row CSV filtered to 50K rows is not just smaller — it's the data the recipient actually needs
Strategy 3: Remove Duplicate Rows
Large CSVs often contain 10–30% duplicate rows from data pipeline issues, multiple exports, or merge artifacts. Removing duplicates reduces file size and improves data quality.
- ETL pipelines often insert the same record multiple times during reruns or backfills
- Exports from CRMs and analytics tools frequently include rows duplicated across date ranges
- Join operations on large tables can multiply rows unexpectedly
- Deduplication on key columns (order ID, user ID, event ID) removes obvious duplicates
- A 1 GB CSV with 25% duplicates becomes 750 MB after deduplication — before any other compression
Strategy 4: Remove Unnecessary Columns
Most CSVs exported from databases or BI tools include columns the recipient doesn't need: internal IDs, debug timestamps, system flags, audit columns. Dropping them reduces both file size and noise.
- Drop internal primary keys, foreign keys, and surrogate IDs not needed by the recipient
- Remove created_at, updated_at, deleted_at timestamps unless they're the point of the analysis
- Strip debug columns, feature flags, system metadata, and pipeline tracking fields
- Remove columns with 95%+ null values — they add file size with no analytical value
- A CSV with 80 columns often has 20–30 columns the recipient actually uses
Which Strategy to Use — Decision Guide
The right approach depends on why your CSV is large. Most of the time, converting to Parquet plus filtering rows gives you the smallest result with the least effort.
CSV is large because it has millions of rows
Recommendation Filter rows first, then convert to Parquet. Filtering first reduces the Parquet conversion time too.
CSV is large because it has many columns (wide table)
Recommendation Drop unnecessary columns first. Parquet compresses wide tables well, but fewer columns means faster reads for the recipient.
CSV has lots of repeated text values (category columns)
Recommendation Convert to Parquet — columnar compression handles repeated categorical values (like country, status, product_type) extremely well.
Recipient needs CSV specifically (older tools, Excel)
Recommendation Filter rows + remove columns + remove duplicates, then export as CSV. Parquet gives the best compression, but CSV is fine for smaller results.
CSV needs to fit GitHub under 100 MB
Recommendation Convert to Parquet — most CSVs that are 200–800 MB compress to well under 100 MB in Parquet format.
Why Business Data Shouldn't Go Through Online Tools
CSV files often contain exactly the kind of data that should never be uploaded to a third-party server: client lists, financial records, transaction data, employee information, sales pipelines.
Client Lists and Contact Data
Customer names, emails, phone numbers, and account data are often subject to GDPR, CCPA, and contractual confidentiality obligations. Uploading them to an online converter may constitute a data breach.
Financial Records and Revenue Data
Transaction CSVs, revenue reports, and financial datasets are commercially sensitive. Competitors, auditors, and investors would value this data. Online tools have no obligation to protect it.
Employee and HR Data
Payroll exports, performance data, and HR records are legally protected in most jurisdictions. Uploading employee data to a third party creates compliance exposure.
Proprietary Business Metrics
Sales pipeline, churn rate, conversion data, and operational metrics are trade secrets. You have no visibility into who has access to files you upload to online tools or how long they retain them.
How to Compress a CSV for Sharing with Diwadi
Download and Open Diwadi
Install Diwadi on your Mac or Windows computer. Open it — no account needed, no internet required. All data operations run locally on your machine.
Load Your CSV
Open the CSV to Parquet tool and load your file. Diwadi shows you the file size, row count, and column list so you can see what you're working with.
Filter, Clean, and Reduce
Use Diwadi's filter tool to select the rows you need, the remove-duplicates tool to clean up repeated records, and the column selector to drop unnecessary fields. Each step reduces your file size before conversion.
Convert to Parquet and Share
Convert the cleaned CSV to Parquet with one click. Diwadi shows you the compressed file size. Share the Parquet file — or if the recipient needs CSV, export back to CSV from the filtered, cleaned result.
Data Tools for Sharing Large CSVs
CSV to Parquet
Convert CSV to Parquet for 80–90% compression
Filter CSV
Filter rows by column values, date ranges, or conditions
Remove Duplicates
Find and remove duplicate rows from CSV or Excel files
Clean Data
Remove empty rows, fix formatting, and standardize values
Excel to CSV
Convert Excel files to CSV for easier sharing and processing
Frequently Asked Questions
How much smaller does a CSV get when converted to Parquet?
Typically 80–90% smaller. A 500 MB CSV commonly becomes 50–100 MB in Parquet. The compression ratio depends on your data: CSVs with many repeated string values (categorical columns like country, status, product type) compress most aggressively. CSVs that are already mostly unique numeric values compress less, but still usually achieve 60–80% reduction. Parquet combines columnar storage with built-in compression (Snappy by default, GZIP optionally) to achieve compression ratios that ZIP cannot match on CSV.
Can the recipient open a Parquet file if they don't have Diwadi?
Yes. Parquet is supported by Python/pandas (pd.read_parquet), DuckDB, Apache Spark, R (arrow package), Julia, Tableau, Power BI, AWS Athena, Google BigQuery, Snowflake, Databricks, and most modern data tools. For recipients using only Excel, you can either send them the filtered CSV instead, or convert the Parquet back to CSV using Diwadi before sharing. Parquet is the standard format for data teams, so most analysts have tools that can read it.
Why is my CSV so large in the first place?
Several factors create large CSVs: (1) Row count — 10 million rows at 100 bytes each is already 1 GB. (2) Wide schemas — databases often export 80+ columns when the analysis needs 10–15. (3) Duplicates — ETL pipelines and repeated exports often insert duplicate rows, adding 10–30% bloat. (4) Long string values — free-text columns like comments, descriptions, or JSON-encoded fields can dwarf numeric columns. (5) No compression — CSV is plain text with zero compression, while formats like Parquet and feather use built-in compression.
Is it safe to convert CSVs that contain personal data?
Yes, as long as you use a tool that processes data locally. Diwadi converts and filters CSVs entirely on your computer — the data never leaves your machine. Online CSV converters require uploading your file to their servers, which creates GDPR, CCPA, and contractual compliance risks if the CSV contains customer data, employee records, or other personal information. Local processing eliminates this risk entirely.
What's the difference between compressing a CSV with ZIP vs converting to Parquet?
ZIP compresses the raw CSV text, which helps but has limits. A 500 MB CSV might ZIP to 150–200 MB. Converting to Parquet first, then ZIPping, often gets you to 30–50 MB because Parquet's columnar storage reorganizes data so that compression algorithms can find more patterns to exploit. For most use cases, converting to Parquet alone (without ZIP) is sufficient and produces a file that data tools can read directly without decompression.
How do I share a CSV that's too large for GitHub's 100 MB limit?
Converting to Parquet is the most common solution. Most CSVs that push GitHub's 100 MB limit compress to well under that in Parquet format. If the repo needs the data in CSV format specifically, consider: (1) Using Git LFS (Large File Storage) for files under GitHub's LFS limits. (2) Storing only a sample CSV in the repo with a script to download the full dataset. (3) Hosting the full CSV/Parquet on S3, GCS, or Hugging Face Datasets and referencing it from the repo. (4) Filtering the dataset to only the rows needed for the repo's use case.
Can I convert part of a CSV to Parquet — only certain columns?
Yes. Diwadi lets you select which columns to include before converting. This is often the best approach for wide tables: select the 15–20 columns the recipient needs, drop the rest, and convert. The result is both smaller in size and easier for the recipient to work with. You can also filter rows and remove duplicates in the same workflow before converting.
What if the recipient needs CSV and Parquet is not an option?
Focus on the other three strategies: filter rows to only what's needed, remove duplicate rows, and drop unnecessary columns. A 1 GB CSV with 10M rows and 80 columns might filter down to 50K relevant rows and 15 needed columns — resulting in a 50–100 MB CSV that fits most limits. If even that's too large, splitting the CSV into multiple files by date range or region is another option.
Does converting to Parquet lose any data?
No data is lost. Parquet is a lossless format — every row, column, and value is preserved exactly. Parquet also preserves column data types (integer, float, boolean, date, string) more accurately than CSV, which stores everything as text. The only way to lose data in this workflow is if you intentionally filter rows or drop columns, which are explicit choices you control.
What's the fastest way to reduce a CSV file size for email?
For email (25 MB limit), the fastest path is usually: (1) Filter rows to only what the recipient needs — if you can get from 500K rows to 5K rows, you're done. (2) Drop columns the recipient doesn't need. (3) Convert to Parquet if the recipient can open it. If the filtered CSV is still above 25 MB, convert to Parquet — most recipients with Python or modern BI tools can open it. If they need CSV specifically, consider using a file sharing link (Google Drive, Dropbox) instead of attaching.
Compress Your CSV for Sharing — Privately
Diwadi converts CSV to Parquet, filters rows, removes duplicates, and drops unnecessary columns — entirely on your computer. No uploads. No servers. Your business data stays on your machine.