Working with big data can be a challenge, thanks to the performance overhead associated with moving data between different tools and systems as part of the data processing pipeline. Indeed, because programming languages, file formats and network protocols have different ways of representing the same data in memory, the process of serializing and deserializing data into a different representation at potentially each step in a data pipeline makes working with large amounts of data slower and more costly in terms of hardware.
Apache Arrow solves this problem, making analytics workloads more efficient for modern CPU and GPU hardware. A framework that defines an in-memory columnar data format that every processing engine can use, Apache Arrow does for OLAP (online analytical processing) workloads what ODBC/JDBC did for OLTP (online transaction processing) workloads by creating a common interface for different systems working with analytics data.
