"The BlueBorne attack vector requires no user interaction, is compatible to all software versions, and does not require any preconditions or configurations aside of the Bluetooth being active. Unlike the common misconception, Bluetooth enabled devices are constantly searching for incoming connections from any devices, and not only those they have been paired with. This means a Bluetooth connection can be established without pairing the devices at all. This makes BlueBorne one of the most broad potential attacks found in recent years, and allows an attacker to strike completely undetected."
In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of columnar storage , columnar compression and data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with Parquet datasets across the six major tools used to read and write from Parquet in the Python ecosystem: Pandas , PyArrow , fastparquet , AWS Data Wrangler , PySpark and Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
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