Convert CSV and JSON to optimized analytics-ready Parquet.
Fast. Simple. Scalable.

Purpose-built and lightweight, that runs inside your cloud or data environment, without ETL pipelines.

We offer private AMI deals today via direct contract, with AWS Marketplace private offers available once our listing is approved.

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Are your datasets getting expensive to store and query?

Storage costs keep growing.

Query costs increase with scanned bytes.

Transfers and reads take longer.

Parqify writes compressed Parquet files, resulting in smaller datasets, faster reads, reduced storage usage, and lower query costs.

Are CSV and JSON slowing down analytics?

Queries take longer as data grows.

Text formats force engines to read and parse more data.

Costs rise with scanned bytes.

Parqify converts CSV and JSON data into columnar Parquet.

In query engines that support it, Parquet enables predicate pushdown and column pruning, which can significantly reduce scanned data and improve query performance.

Is Schema handling slowing you down?

CSV and JSON structures can drift over time.

Manual schema maintenance is brittle.

Mismatched fields can break downstream jobs.

Parqify automatically infers schemas from sampled input and can reuse cached schemas for matching file structures, produces consistent, analytics-ready Parquet output.

Are ETL tools overkill for simple format conversion?

Complex pipelines for a straightforward task.

Operational overhead you may not need.

Longer setup and maintenance cycles.

Parqify is a lightweight, purpose-built conversion tool that focuses on one thing only: converting CSV and JSON to Parquet — without building ETL infrastructure.

Why Parquet Format?

✔️ Smaller storage

✔️ Faster analytics and query engines (Athena / Redshift / Spark)

✔️ Columnar format

✔️ Language agnostic

✔️ Support for complex data types

✔️ Industry standard

Parquet is a columnar storage file format that offers several advantages over traditional row-based formats like CSV and JSON, especially for big data processing and analytics:

Efficient Data Compression
Faster Query Performance
Schema Evolution
Optimized for Big Data Frameworks
Reduced I/O Operations

These benefits make Parquet an ideal choice for data warehousing, analytics, and machine learning workloads, where performance and storage efficiency are critical.

How Parqify Works

Parqify schema conversion diagram showing CSV/JSON to Parquet transformation

Parqify provides a server application, packaged as an AMI, that customers can deploy within their cloud environment.

This server reads CSV and JSON files from a specified cloud object storage, converts them to the Parquet format, and then writes the converted files back to a destination storage location.

Parqify supports AWS S3.

🚀 Optimized for Conversion — Not General ETL

Parqify uses a lightweight streaming pipeline designed specifically for cloud object storage → Parquet conversion.

Unlike Spark-based tools, it avoids cluster startup, staging datasets, and JVM overhead. Files are streamed directly from cloud object storage into Parquet writers with column-aware buffering and parallel IO.

The result: