Apache Parquet

@ApacheParquet

language agnostic, open source Columnar file format for analytics

Připojil se duben 2013

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  1. Retweetnuto uživatelem
    8. 5.
    Odpověď uživatelům

    Windows support for + will come in the upcoming parquet-cpp 1.1.0 release

  2. Retweetnuto uživatelem
    23. 5.

    Working on updating conda-forge and PyPI packages for pyarrow 0.4.0; will also include Windows support for . Stay tuned

  3. Retweetnuto uživatelem
    22. 5.

    We have just released C++ 1.1.0 including Windows support and expanded Arrow type support

  4. Retweetnuto uživatelem
    16. 5.
  5. Retweetnuto uživatelem
    15. 5.
    Odpověď uživatelům

    Plus I get to work on projects like , , , and (and contribute, too!)

  6. Retweetnuto uživatelem
    4. 5.
    Odpověď uživatelům a

    If you are at this weekend don't miss talking about and backing pandas

  7. Retweetnuto uživatelem
    4. 5.

    Sun&Sat, I'll be at ,convince you about , show how helped us bring it to Pandas and enjoy good talks

  8. Retweetnuto uživatelem
    26. 4.

    Video of my talk at "Efficient data formats for analytics with and "

  9. Retweetnuto uživatelem
    25. 4.

    At : Efficient Columnar Storage w/ by Ranganathan Balashanmugam Join us!

  10. Retweetnuto uživatelem
    25. 4.
    Odpověď uživatelům

    A snapshot of some community OSS projects (pandas, , , and others) that we've gotten involved with

  11. Retweetnuto uživatelem
    21. 4.
  12. Retweetnuto uživatelem
    21. 4.

    Great breakdown on pros/cons of coumnar format for data at meetup.

  13. Retweetnuto uživatelem
    19. 4.

    At : Even Faster: When Presto Meets @ Uber by Zhenxiao Luo Join us!

  14. Retweetnuto uživatelem
    7. 4.

    If you use Dask and , other perspectives on this issue would be useful:

  15. Retweetnuto uživatelem
    30. 3.
    Odpověď uživatelům a

    on and is mind blowing .. great analytical speeds . Great file compression

  16. Retweetnuto uživatelem
    6. 4.

    Check out the awesome schedule of I'll talk about DataFrame storage with

  17. Retweetnuto uživatelem
    4. 4.

    Bringing file support for with , and fast distributed data processing with

  18. Retweetnuto uživatelem
    30. 3.

    can be so frustrating. But when you finally write to a file, it's so satisfying.

  19. Retweetnuto uživatelem
    31. 3.

    and : Why We Needed Different Projects for Columnar Data, On Disk and In-Memory

  20. Retweetnuto uživatelem
    30. 3.

    Software I would write if I had the time: a foreign data wrapper for files.

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