Apache Arrow

Powering Columnar In-Memory Analytics

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Latest News: Apache Arrow 0.6.0 release


Apache Arrow™ enables execution engines to take advantage of the latest SIM D (Single input multiple data) operations included in modern processors, for native vectorized optimization of analytical data processing. Columnar layout of data also allows for a better use of CPU caches by placing all data relevant to a column operation in as compact of a format as possible.

The Arrow memory format supports zero-copy reads for lightning-fast data access without serialization overhead.


Arrow acts as a new high-performance interface between various systems. It is also focused on supporting a wide variety of industry-standard programming languages. Java, C, C++, Python are underway and more languages are expected soon.


Apache Arrow is backed by key developers of 13 major open source projects, including Calcite, Cassandra, Drill, Hadoop, HBase, Ibis, Impala, Kudu, Pandas, Parquet, Phoenix, Spark, and Storm making it the de-facto standard for columnar in-memory analytics.

Performance Advantage of Columnar In-Memory


Advantages of a Common Data Layer

common data layer
  • Each system has its own internal memory format
  • 70-80% CPU wasted on serialization and deserialization
  • Similar functionality implemented in multiple projects
common data layer
  • All systems utilize the same memory format
  • No overhead for cross-system communication
  • Projects can share functionality (eg, Parquet-to-Arrow reader)