|
gptkbp:instance_of
|
gptkb:software_framework
|
|
gptkbp:bfsLayer
|
5
|
|
gptkbp:bfsParent
|
gptkb:Skikda_Airport
|
|
gptkbp:can_be_used_with
|
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch
XG Boost
|
|
gptkbp:developed_by
|
Coiled
|
|
gptkbp:first_released
|
gptkb:2016
|
|
gptkbp:has_community
|
active community
|
|
gptkbp:has_documentation
|
https://docs.dask.org
|
|
gptkbp:has_feature
|
gptkb:software_framework
arrays
fault tolerance
data locality
dataframes
task graphs
delayed execution
adaptive scaling
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
DASK
|
|
gptkbp:integrates_with
|
gptkb:park
Dask Distributed
|
|
gptkbp:is_a_hub_for
|
https://github.com/dask/dask
|
|
gptkbp:is_compatible_with
|
gptkb:computer
SQL databases
No SQL databases
|
|
gptkbp:is_designed_for
|
big data processing
real-time data processing
scalable analytics
|
|
gptkbp:is_open_source
|
gptkb:theorem
|
|
gptkbp:is_optimized_for
|
distributed systems
multi-core processors
|
|
gptkbp:is_part_of
|
Python ecosystem
|
|
gptkbp:is_popular_in
|
gptkb:academic_research
industry applications
data science community
|
|
gptkbp:is_scalable
|
thousands of cores
petabytes of data
|
|
gptkbp:is_supported_by
|
gptkb:Microsoft_Azure
gptkb:Google_Cloud
gptkb:AWS
gptkb:Anaconda
|
|
gptkbp:is_used_by
|
gptkb:physicist
data scientists
data engineers
|
|
gptkbp:is_used_for
|
ETL processes
parallel computing
stream processing
batch processing
data science workflows
data engineering tasks
|
|
gptkbp:language
|
gptkb:Library
|
|
gptkbp:latest_version
|
2023.10.0
|
|
gptkbp:license
|
gptkb:BSD_License
|
|
gptkbp:passes_through
|
cloud environments
clusters
local machines
|
|
gptkbp:performance
|
high performance
|
|
gptkbp:provides
|
dynamic task scheduling
|
|
gptkbp:scales
|
large datasets
|
|
gptkbp:supports
|
gptkb:scikit-learn
gptkb:numpy
pandas
|
|
gptkbp:tutorials
|
available online
|
|
gptkbp:use_case
|
data analysis
data preprocessing
data visualization
data cleaning
machine learning model training
|
|
gptkbp:written_in
|
gptkb:Library
|