Apache Arrow
Apache Arrow is an open source project that defines a standard in-memory columnar data format. It lets different databases, programming languages, and tools share data without costly conversion or serialization, so analytical work moves between systems with near-zero overhead.
The universal columnar memory format
Before Arrow, every database and analytics library had its own in-memory layout. Moving data from a database into Python, or from a query engine into a visualization tool, meant copying and reformatting. Arrow provides a single columnar memory specification that all of those systems can read and write directly.
The result is zero-copy reads. A Pandas DataFrame backed by Arrow does not need to deserialize anything. A query engine can hand results directly to a visualization layer. The columnar layout in memory matches the columnar layout on disk in formats like Parquet, so the fit is natural end to end.
Arrow has become the connective tissue of the modern data stack. DuckDB, Spark, BigQuery, Snowflake, InfluxDB IOx, DataFusion, and most analytical tools built in the last five years use it.
How Arc handles Apache Arrow
Arc uses Apache Arrow as its in-memory data interchange layer. Data flows from ingestion through the query engine in Arrow columnar format, then lands on disk as Parquet. The same columnar structure from memory to storage is a core reason Arc achieves high ingestion throughput and fast query response.
Related terms
Arc is a SQL-native time-series database. Open Parquet on storage you own, single Go binary, production-ready in 30 seconds.