ParquetReader Now Supports Vortex Files
Vortex file support is now available
ParquetReader now supports Vortex, a new columnar data format designed for high-performance analytical workloads.
You can upload a Vortex file to inspect its schema and data, then export the contents to CSV, JSON, or Parquet. This makes it easier to explore Vortex files without first writing code or setting up a dedicated local data environment.
The addition expands ParquetReader's support for modern analytical formats alongside Parquet, CSV, JSON, Excel, Avro, ORC, Feather, and other structured data files.
What is Vortex?
Vortex is an open-source, extensible format for columnar data. Like Apache Parquet, it is intended for storing and processing structured data efficiently, but it approaches compression, physical layout, and computation differently.
The format separates the logical data type from the physical representation of the data. This allows Vortex to use different encodings and layouts for different kinds of values while presenting a consistent logical schema to applications.
Vortex is hosted as an incubation-stage project by LF AI & Data, part of the Linux Foundation. The project is Apache 2.0 licensed and integrates with technologies including Apache Arrow, Apache DataFusion, DuckDB, Spark, Pandas, and Polars.
You can learn more about the project, its design, documentation, and benchmarks at vortex.dev.
Why create another columnar format?
Apache Parquet has become one of the most important storage formats in data engineering. It offers strong compression, broad support, and interoperability across data warehouses, query engines, analytics platforms, and programming languages.
However, Parquet was designed more than a decade ago. Modern workloads increasingly involve cloud object storage, wide schemas, selective access, machine-learning pipelines, vectors, and heterogeneous CPU and GPU processing.
Vortex is designed to explore what a columnar format can look like when compression and computation are considered together. Instead of always fully decoding values before working with them, Vortex can support operations directly on encoded data and delay materialization until it is required.
This does not make Parquet obsolete. It makes Vortex an interesting alternative for systems where read performance, extensibility, selective access, or newer compute patterns are especially important.
How Vortex describes its performance
The Vortex project reports substantially faster random access, scans, and writes than Apache Parquet in its published benchmarks, while targeting a similar compression ratio.
According to the project's website, its benchmark results include up to 100 times faster random access, 10 to 20 times faster scans, and 5 times faster writes compared with Parquet.
These figures are claims from the Vortex project and should be understood in the context of its benchmark datasets, engines, configurations, hardware, and access patterns. Performance results for any storage format can vary significantly depending on the data and workload.
For the current figures and benchmark methodology, visit the official Vortex website and Vortex benchmark results.
Compression is only one part of the design
A smaller file is useful because it reduces storage and network transfer, but analytical performance depends on more than compression ratio alone.
A highly compressed format can still be slow if an engine must read and decode large sections of a file before applying a filter. Vortex is designed around lightweight, composable encodings and compute operations that can work with encoded data.
Its extensible architecture allows new encodings, data types, compression strategies, and physical layouts to be added. This gives implementers more flexibility to optimize storage for specific data and access patterns.
The practical result depends on the reader, writer, query engine, dataset, and workload. The interesting part of Vortex is therefore not one isolated performance number, but the combination of compression, selective access, computation, and extensibility.
A format designed around Apache Arrow
Vortex is closely connected to the Apache Arrow ecosystem. Arrow defines a language-independent columnar representation for working with data efficiently in memory, while Vortex provides representations for compressed arrays in memory, on disk, and over the wire.
This relationship makes Vortex relevant to modern analytical systems that already use Arrow as a common interface between query engines, data-frame libraries, and storage layers.
Vortex also separates logical types from their physical encodings. Multiple physical representations can therefore describe the same logical data type, allowing storage decisions to evolve without forcing applications to adopt a different logical schema.
Is the Vortex file format stable?
Vortex is still a young project, but its file-format documentation states that the format has been considered backward-compatible since version 0.36.0. Newer Vortex releases are expected to continue reading files written by version 0.36.0 and later.
The surrounding library APIs may still change between releases, and forward compatibility is still planned for a future release. Teams adopting Vortex should therefore distinguish between stability of existing files and stability of the programming interfaces used to create and process them.
The current compatibility guarantees are documented in the official Vortex file-format specification.
Vortex and Parquet serve different adoption stages
Parquet has more than a decade of adoption and remains one of the safest choices when data must move between many different tools, cloud services, data warehouses, and BI platforms.
Vortex is much newer. Its integrations are growing quickly, but support is not yet as universal as Parquet support. That distinction matters when choosing a long-term storage or interchange format.
For experimentation, specialized analytical systems, and workloads that can benefit from its design, Vortex is already worth exploring. For maximum portability across existing data platforms, Parquet remains the more established option.
The two formats do not have to be mutually exclusive. A team can evaluate Vortex for specific storage and processing workloads while continuing to use Parquet when broad compatibility is the primary requirement.
Open and inspect a Vortex file
A new file format is difficult to evaluate when opening it requires custom code, command-line tools, or a complete analytics environment.
With ParquetReader, you can upload a Vortex file and inspect its structure and contents through a visual interface. This is useful when you receive an unfamiliar file, want to confirm its schema, or need to understand the data before choosing another tool or format.
You can review column names, data types, values, and the overall shape of the dataset without building a separate application simply to look inside the file.
Convert Vortex to CSV, JSON, or Parquet
ParquetReader can also export Vortex data to CSV, JSON, or Parquet.
CSV is useful for spreadsheets, simple integrations, and tools that do not support columnar formats. JSON is useful for APIs, development workflows, and document-oriented processing. Parquet is useful when you want to preserve a compact columnar representation while maximizing compatibility with established analytics platforms.
Conversion gives you a practical bridge between an emerging format and the tools you already use. You can explore Vortex without requiring every downstream application to support it immediately.
When should you consider Vortex?
Vortex may be worth evaluating when your workload depends on fast selective reads, cloud object storage, modern analytical engines, machine-learning pipelines, wide tables, or extensible physical encodings.
It may also be interesting for engineers researching alternatives to Parquet or designing new storage layers where the reader and writer can both be controlled.
Parquet is still likely to be the better default when interoperability is the main concern, especially when files must be consumed by a wide and unpredictable set of third-party tools.
As always, use representative data and queries when comparing formats. Marketing benchmarks can identify promising technologies, but your own workload should determine which format is the best fit.
Try Vortex support in ParquetReader
Vortex support is available now in ParquetReader. Upload a Vortex file to inspect its schema and data or convert it to CSV, JSON, or Parquet.
Open a Vortex file with ParquetReader
To learn more about the format itself, visit the official Vortex project website.