SmartQueryTools

Arrow Tools

19 free Arrow tools. All run in your browser, with no uploads and no account.

Convert Arrow to another format

Convert other formats to Arrow

View, inspect and query Arrow files

Arrow Format Comparisons

About Arrow

Apache Arrow defines both an in-memory columnar format and an IPC (Inter-Process Communication) file format for persisting Arrow data to disk. The format is designed for zero-copy reads, SIMD-optimised operations, and high-speed data exchange between systems and programming languages — all without the serialisation overhead of formats like CSV or JSON. DuckDB, pandas, Polars, PySpark, and Ray all use Arrow as their internal memory representation.

Arrow IPC files appear in high-performance data engineering contexts: passing a large dataset from a Python process to a Rust worker without copying memory, checkpointing in-memory Arrow tables to disk for fast reload, sharing data between processes using Arrow Flight, and as an exchange format in distributed query systems. If you are building or debugging a pipeline that uses Arrow natively, you may occasionally need to inspect or convert an Arrow file.

Arrow is rarely the end destination for data — it is an intermediate format for performance-critical exchange. Our Arrow tools let you inspect the schema and contents of an Arrow IPC file, convert to CSV or Parquet for storage and sharing, or convert from other formats into Arrow. Everything runs in the browser with no installs.

Frequently Asked Questions

What is the difference between Apache Arrow and Parquet?

Arrow is an in-memory columnar format optimised for zero-copy access and processing speed. Parquet is a disk-based columnar format optimised for compression and efficient storage. Arrow IPC files preserve the in-memory layout (no decompression needed on read) but are typically larger than Parquet. Parquet is the right choice for long-term storage; Arrow is the right choice for fast inter-process data exchange.

What is an Arrow IPC file?

An Arrow IPC (Inter-Process Communication) file stores one or more Arrow record batches — chunks of columnar data — in a binary format that can be memory-mapped directly, enabling zero-copy reads. The file usually has a .arrow, .ipc or .feather extension. It is produced by any Arrow-compatible library when you write an Arrow table to disk.

Can I open an Arrow file without Python?

Yes. Use the Arrow Viewer or Arrow to CSV converter here to inspect or export the contents without a Python environment or data science toolchain. Arrow IPC files and streams are read directly in the browser. Files written with LZ4 or ZSTD compression, the pyarrow and pandas Feather default, open as they are.

More Arrow operations

Every CSV operation also works directly on Arrow files:

Remove duplicates · Filter · Sort · Split · Sample · Select columns · Rename · Merge · Trim · Fill empty values · Transpose · First N rows · Last N rows · Add row numbers · Validate structure · Count by value · Unique values · Compare · Format timestamps · Find & replace · Split column · Combine columns · Convert case · Aggregate · Round numbers · Conditional column · Regex extract · Parse dates · Compare schemas · Change column types · Unpivot · Shuffle · Bin column · Calculated column · Extract JSON column · Date difference · Normalize · Rank · Running total · Detect outliers · Validate emails · Moving average · Search text · Top N per group · Percent of total · Parse URLs · Lag / lead · Filter by date range · Correlation matrix · Fuzzy deduplicate · Hash columns · Group concatenate · Truncate dates · Coalesce columns · Add UUID column · Pad column · Percentile · Arg max / min