Apache Arrow Definition
Apache Arrow is a platform that analyzes the memory in a server’s random access memory (RAM). It works in any computer language and defines a columnar memory format standard. The columnar layout allows for faster processing of data than rows. Apache Arrow performance also provides computational libraries and saves the central processing unit (CPU) from having to copy data from one memory area to another.
What Is Apache Arrow?
Apache Arrow improves the speed of data analytics by creating a standard columnar memory format that any computer language can understand. In addition to being a development platform, it also provides libraries for software.
Apache Arrow performance allows for the transfer of data without the cost of serialization (the process of translating data into a format that can be stored). Apache Arrow is a standard that can be implemented by any computer program that processes memory data.
How Does Apache Arrow Work?
Apache Arrow acts as an interface between different computer programming languages and systems. By creating a standard for columnar data layout (versus rows) for memory processing, it speeds up the transfer of data by eliminating unnecessary input/output communication. It also provides caching benefits for data structures. This optimizes the performance of modern central processing units (CPUs) and graphics processing units (GPUs).
Apache Arrow processes large amounts of data quickly by using Single Instruction Multiple Data (SIMD). Sets of data are broken into batches that fit the cache layers of a CPU. The Apache Arrow project has a standard format allowing for seamless sharing of data between systems instead of using CPU cycles to convert data between formats.
Apache Arrow Benefits
- A columnar memory-layout in which the memory analytics required by the algorithm are constant and do not depend on the size of the input.
- The layout permits Single Instruction Multiple Data (SIMD) optimizations. Software engineers can create very fast algorithms by performing the same analytic workloads on multiple data points simultaneously.
- Cache-efficient and fast data interchange between systems without the serialization costs of other systems.
When To Use Apache Arrow
Apache Arrow is used to accelerate analytic workloads within a particular system when data needs to be exchanged with low overhead. It is flexible enough to support most complex data science models.
How Is Apache Arrow Used In Big Data Analytics?
Apache Arrow is used for handling big data generated by the Internet of Things and large scale applications. Its flexibility, columnar memory format and standard data interchange offers the most effective way to represent dynamic datasets.
Apache Arrow performance does more than just speed up a big data project — it can handle multiple projects by acting as a common data interchange mechanism. Instead of moving datasets between projects, applications using Apache Arrow can trade data directly and speed up access. Learn more about big data analytics tools here.
Does HEAVY.AI Offer Apache Arrow?
HEAVY.AI realizes the value of Apache Arrow and we are working to integrate it deeply within our open source SQL engine. Apache Arrow performance solves precisely the problems we expect to encounter related to data interchange. And a natural outcome of being a GPU-native engine means that there is great interest in integrating HEAVY.AI into machine learning where Apache Arrow forms the foundation of the GPU dataframe, which provides a highly performant and low-overhead data interchange mechanism.