Turn raw CSV and JSON files on S3 into analytics-ready Parquet files datasets in minutes. Fast.

Reduce storage costs, accelerate analytics, and prepare data for AWS services without building ETL pipelines.

Launch on AWS Marketplace
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    Delivery methods:

  • Amazon Machine Image (AMI).
  • CloudFormation Template (CFT).
Use Case

Small and Medium Businesses

Many growing companies store business data in CSV exports, JSON logs, and Amazon S3 buckets, but they do not always have an experienced data engineer to build and maintain data pipelines.

Challenge #1: High AWS S3 Storage Costs

As data volumes grow, storing CSV and JSON files becomes increasingly expensive. These formats are not optimized for analytics and usually consume more storage than necessary.

Solution

Parqify converts CSV and JSON files into compressed, optimized Parquet datasets through a friendly web UI. No data engineering expertise is required.

Result

  • Reduce S3 storage consumption
  • Compress datasets by 3–10x compared with CSV
  • Lower infrastructure costs
  • Store more data without expanding storage budgets

Challenge #2: Expensive and Slow Analytics Queries

Analytics platforms such as AWS Athena, Apache Spark, Trino, and Presto can become expensive and slow when querying raw CSV or JSON files.

Solution

Parqify converts raw files into analytics-ready Parquet datasets optimized for modern query engines. Convert and optimize datasets without writing code.

Result

  • Faster query performance
  • Reduced data scanned per query
  • Lower analytics platform costs
  • Reduce Athena query costs by up to 90%
Use Case

Small, Medium Businesses & Enterprise Data Teams

Challenge #1: Archive Data

Companies often keep old CSV or JSON files in S3 for years: logs, reports, exports, transactions, or customer activity data. These files take more storage space, are slow to search, and are expensive to query with tools like Amazon Athena.

Solution

Parqify converts archived CSV and JSON files into Parquet — a compressed, column-based format designed for analytics. The data stays in S3, but becomes smaller, cleaner, and easier to query later.

Result

The company reduces storage costs, keeps historical data available, and can still analyze old records when needed — without keeping heavy CSV/JSON archives forever.

Challenge #2: Partitioning by date when the source file stores date as text

An engineer wants to partition data by date, for example:

s3://bucket/events/date=2026-06-14/

But in the source CSV or JSON file, the date column is just a string:

"2026-06-14"

Many analytics tools treat it as plain text, not as a real date. This can make partitioning, filtering, and querying less reliable.

Solution

With Parqify schema customization, the engineer can define this column as a proper date or timestamp during conversion. Example:

event_date: string → date

Parqify converts the source file to Parquet with the correct type and can use that column for partitioning.

Result

The data lands in S3 already organized and optimized:

s3://bucket/events/event_date=2026-06-14/

Athena, Trino, Spark, or other analytics tools can filter by date more efficiently, scan less data, and avoid manual cleanup pipelines.
Features

Built for AWS Data Workflows

Parqify helps teams convert and optimize CSV and JSON datasets in Amazon S3 without building custom ETL pipelines or writing code.

Fast Conversion

Process large Amazon S3 datasets with parallel conversion designed for better performance across multiple files.

Runs in Your AWS Account

All processing happens inside your AWS environment, so your data stays within your own infrastructure.

Friendly Web UI

Configure S3 buckets, file prefixes, conversion settings, and import or export configs through a simple web interface.

Scalable Processing

Choose the EC2 instance size that matches your dataset volume, performance needs, and AWS budget.

Easy Deployment

Deploy Parqify from AWS Marketplace and start converting S3 files into optimized Parquet datasets.

Analytics-Ready Parquet

Use built-in optimization profiles for AWS Athena, Apache Spark, Trino, Presto, and AWS Glue.

Optimization Profiles

Built-in Parquet Optimization Profiles

Select your analytics engine and Parqify automatically applies optimized Parquet settings. No data engineering expertise required.

You Get

📉

Lower storage costs

⚡

Faster AWS Athena queries

🚀

Faster Apache Spark jobs

⏱️

Faster Trino and Presto performance

💰

Lower AWS analytics costs

Parqify Optimization Profiles

With Zero Effort

Convert and optimize datasets through a friendly web UI. No coding. No data engineering expertise required.

Just Select & Go

Choose Default, AWS Athena, Apache Spark, AWS Glue, Trino, Presto, or Custom. Parqify automatically applies recommended Parquet settings.

Format Size Athena Query Time
CSV 10 GB 45 sec
Default Parquet 2 GB 7 sec
Parqify Optimized 1.6 GB 4 sec
Purpose-Built Conversion

Optimized for Conversion — Not General ETL

Parqify uses a lightweight streaming pipeline designed specifically for S3 to Parquet conversion.

Why It’s Faster

Unlike Spark-based tools, Parqify avoids cluster startup, staging datasets, and JVM overhead.

Files are streamed directly from Amazon S3 into Parquet writers with column-aware buffering and parallel IO.

The Result

  • 🚀 Faster startup
  • 🚀 Lower memory usage
  • 🚀 Fewer S3 operations
  • 🚀 Smaller Parquet output
  • 🚀 Better performance for Amazon Athena and Amazon Redshift

Perfect for Teams That Just Need Parquet

Parqify is built for companies that want fast CSV and JSON to Parquet conversion without building ETL infrastructure.

Choose the Right Tool

Quick comparison by purpose, startup time, and best use.

Tool Designed for Startup cost Best use
Parqify Format conversion Fast CSV/JSON → Parquet
Glue / EMR Full ETL Medium Complex pipelines
Athena CTAS SQL transforms Medium Query-driven workflows
Use Cases

Common AWS Workloads for Parqify

Parqify helps teams prepare raw S3 data for faster, cheaper analytics.

Data Lake Optimization

Convert raw CSV and JSON files in Amazon S3 into compressed Parquet datasets for lower storage costs and faster querying with AWS Athena, Redshift Spectrum, Trino, and Presto.

Log Analytics

Transform application, service, and API logs from JSON into analytics-ready Parquet files for faster troubleshooting, reporting, and historical analysis.

Data Sharing

Share Parquet files instead of large CSV or JSON exports to reduce file size, improve downstream performance, and make data easier to consume.

Quick Start

Get started in minutes:

  • ✔️ Launch Parqify from AWS Marketplace
  • ✔️ Open browser to instance IP
  • ✔️ Create your first conversion

Ready to get started?

Launch on AWS Marketplace
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