Completed
Limited data types Excessive memory consumption Poor processing efficiency for non-numeric types Accommodate larger-than-memory datasets
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Apache Arrow and the Future of Data Frames with Wes McKinney
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- 1 Apache Arrow and the Future of Data Frames
- 2 Career Theme Programming interfaces for data preparation, analytics, and feature engineering
- 3 What exactly is a data frame?
- 4 A data frame is ... a programming interface ... for expressing data manipulations
- 5 Data frames address many analytical workloads that are either not possible or not well-served by traditional SQL-based systems
- 6 In R, the "data frame" data structure is part of the language Other projects implement their own (e.g. pandas) • Some projects may not use any data structures (e.g. compiling operations to SQL)
- 7 Most data frames are effectively "Islands" with a hard serialization barrier • Many non-reusable implementations of the same algorithms • Limited collaboration across projects and programming languag…
- 8 Apache Arrow Open source community project launched in 2016 • Intersection of database systems, big data, and data science tools • Purpose: Language independent open standards and libraries to accele…
- 9 Improve interoperability problems with other data processing systems . Standardize data structures used in data frame implementations • Promote collaboration and code reuse across libraries and progr…
- 10 Limited data types Excessive memory consumption Poor processing efficiency for non-numeric types Accommodate larger-than-memory datasets
- 11 Apache Arrow Project Overview Language-agnostic in-memory columnar format for analytical query engines, data frames • Binary protocol for IPC/RPC . "Batteries included" development platform for build…
- 12 Arrow and the Future of Data Frames . As more data sources offer Arrow-based data access, it will make sense to process Arrow in situ rather than converting to some other data structure • Analytical …
- 13 Runtime memory format for analytical query processing • Ideal companion to columnar storage like Apache Parquet Fully shredded columnar, supports flat and nested schemas Organized for cache-efficient…
