Parquet S3 & Athena Cloud Savings Calculator
Calculate exact cloud bill savings by converting CSV, JSON, or text logs to Apache Parquet. Estimate AWS S3 storage reduction and Athena/BigQuery scan savings.
Interactive WebAssembly Calculation Engine
This tool runs 100% in your local web browser tab. All amortization schedules, debt yield calculations, and tax deferral projections are computed client-side in WebAssembly. Zero bytes are uploaded to remote servers.
Use the interactive form above to adjust parameters, toggle extra principal schedules, stress-test interest rates, and export full reports to Excel (.xlsx) or CSV.
Underwriting Methodology & Mathematical Formulation
Our calculation algorithms strictly adhere to institutional lending conventions, CFPB disclosure rules, and statutory IRS definitions:
- Standard Annuity Formula: Monthly payment M = P * [r(1+r)^n] / [(1+r)^n - 1], calculating exact interest and principal allocations down to the penny.
- Zero-Roundoff Double Precision: Computations execute using standard IEEE 754 floating-point and integer math to match commercial bank loan documents.
- Sensitivity Analysis: Real-time stress testing against interest rate fluctuations, vacancy rate increases, and balloon maturity horizons.
Frequently Asked Questions
Why does Apache Parquet reduce AWS S3 storage bills by 80% to 90%?
Unlike row-based text files (CSV or JSON) where repetitive text strings are duplicated row by row, Apache Parquet organizes data in columns. Similar data types are grouped together, enabling ultra-efficient dictionary encoding, run-length encoding (RLE), bit-packing, and high-ratio compression codecs like ZSTD or Snappy.
How does Parquet cut Amazon Athena and Google BigQuery scanning costs?
Serverless query engines like AWS Athena bill $5.00 per TB of data scanned from S3. Because Parquet is columnar, a query selecting only 3 columns from a 50-column dataset reads ONLY those 3 columns from disk (column projection), skipping 90%+ of the file bytes. Combined with min/max predicate pushdown, Athena scan bills routinely fall by 90% to 99%.
Which Parquet compression codec is best: Snappy, ZSTD, or GZIP?
Snappy is the cloud default: it offers blazing fast decompression speeds with ~75% size reduction, ideal for real-time streaming queries. ZSTD (level 3) is the modern gold standard: it achieves 85% to 90% compression ratios while maintaining decomp speed close to Snappy. GZIP provides maximum compression but suffers from significantly slower decompression CPU overhead.
What is Predicate Pushdown and Row Group Pruning?
Parquet files divide tables into Row Groups (typically 128 MB to 512 MB) and store min/max statistics for every column in the file footer metadata. When you run a query like "WHERE event_date >= '2025-01-01'", the query engine reads the footer and skips reading entire row groups that don't match the criteria, avoiding millions of bytes of I/O.
Can I convert large CSV or JSON files to Parquet directly in the browser?
Yes! Using TableView's DuckDB-Wasm in-browser converter, you can convert gigabyte-sized CSV, JSON, and NDJSON files into Snappy or ZSTD Parquet files directly inside your browser without uploading any confidential data to third-party servers.
How does Parquet compare to Apache ORC or Avro?
Avro is a row-oriented format optimized for write-heavy streaming message queues (Kafka). Parquet and ORC are both columnar formats optimized for analytical read queries (OLAP). Parquet has achieved universal cross-platform dominance across Spark, DuckDB, Trino, Snowflake, Databricks, ClickHouse, and AWS Athena.