Data Storage Optimization
Compress Archived Data and Reduce Storage Costs
Organizations accumulate enormous amounts of historical data over time. Application logs, exported reports, IoT records, financial transactions, and backups often remain in Amazon S3 for years.
While this archived data is accessed infrequently, it still generates storage costs every month. Many organizations continue storing CSV or JSON files because that's how the data originally arrived, even though these formats are highly inefficient for long-term storage.
Compressing archived data into Apache Parquet is one of the simplest ways to reduce storage costs while keeping data available for future analytics.
Why Archived Data Is Expensive
Text-based formats such as CSV and JSON contain significant redundancy:
- Repeated column names
- Repeated text values
- Large amounts of whitespace
- No efficient binary encoding
- Poor compression characteristics
As archived datasets grow into hundreds of gigabytes or terabytes, the unnecessary storage overhead becomes expensive.
Why Apache Parquet Is Better
Apache Parquet was designed for analytical workloads and efficient storage.
Instead of storing data row by row like CSV, Parquet stores values column by column. This allows similar values to compress extremely well.
Parquet also supports compression codecs such as:
- Snappy
- Zstandard (ZSTD)
- Gzip
- Brotli
- LZ4
Depending on the data, Parquet files are often 3–10× smaller than the original CSV or JSON files.
Storage Savings Example
Suppose you archive application logs every month.
| Format | Storage |
|---|---|
| CSV | 10 TB |
| Parquet (Snappy) | ~3 TB |
| Parquet (ZSTD) | ~2 TB |
Even moderate compression ratios can save thousands of dollars over the lifetime of large datasets.
Additional Benefits
Converting archived data to Parquet provides benefits beyond storage reduction.
Faster Future Analytics
If archived data ever needs to be queried using Amazon Athena, Apache Spark, Trino, or DuckDB, Parquet dramatically reduces the amount of data scanned.
Smaller files mean:
- faster queries
- lower query costs
- improved performance
Better Organization
Many organizations archive data by year or month.
For example:
archive/
2023/
2024/
2025/Keeping archived data in optimized Parquet files makes future retrieval much easier.
Choosing the Right Compression
Different compression codecs provide different trade-offs.
| Codec | Compression | Speed | Best For |
|---|---|---|---|
| Snappy | Good | Very Fast | General-purpose analytics |
| ZSTD | Very High | Fast | Long-term archives |
| Gzip | Very High | Slow | Maximum size reduction |
| Brotli | Excellent | Slow | Highly compressible datasets |
| LZ4 | Good | Very Fast | Fast read/write workloads |
For archived data that is rarely modified, higher compression is often worth the additional conversion time.
Compress Archived Data with Parqify
Parqify converts CSV and JSON files stored in Amazon S3 into optimized Apache Parquet.
Built-in optimization profiles automatically configure Parquet settings for common analytics engines. For advanced use cases, the Custom Optimization profile lets you choose the compression codec that best matches your storage and performance requirements. You can also configure these settings manually to fine-tune the output for your workload.
Because Parqify runs entirely inside your AWS account, your data never leaves your own infrastructure during the conversion process.
Final Thoughts
Archived datasets often remain untouched for years, yet they continue generating storage costs every month.
Converting historical CSV and JSON files into compressed Apache Parquet is a straightforward way to reduce storage costs, improve future query performance, and keep archived data ready for analytics whenever it is needed.