  Moving structured data between different big data frameworks and/or data
warehouses/storage systems often cause significant overhead. Most of the time
more than 80\% of the total time spent in accessing data is elapsed in
serialization/de-serialization step. Columnar data formats are gaining
popularity in both analytics and transactional databases. Apache Arrow, a
unified columnar in-memory data format promises to provide efficient data
storage, access, manipulation and transport. In addition, with the introduction
of the Arrow Flight communication capabilities, which is built on top of gRPC,
Arrow enables high performance data transfer over TCP networks. Arrow Flight
allows parallel Arrow RecordBatch transfer over networks in a platform and
language-independent way, and offers high performance, parallelism and security
based on open-source standards.
  In this paper, we bring together some recently implemented use cases of Arrow
Flight with their benchmarking results. These use cases include bulk Arrow data
transfer, querying subsystems and Flight as a microservice integration into
different frameworks to show the throughput and scalability results of this
protocol. We show that Flight is able to achieve up to 6000 MB/s and 4800 MB/s
throughput for DoGet() and DoPut() operations respectively. On Mellanox
ConnectX-3 or Connect-IB interconnect nodes Flight can utilize upto 95\% of the
total available bandwidth. Flight is scalable and can use upto half of the
available system cores efficiently for a bidirectional communication. For query
systems like Dremio, Flight is order of magnitude faster than ODBC and turbodbc
protocols. Arrow Flight based implementation on Dremio performs 20x and 30x
better as compared to turbodbc and ODBC connections respectively.
