# Here you can define all your data sets by using simple YAML syntax.
#
# Documentation for this file format can be found in "The Data Catalog"
# Link: https://kedro.readthedocs.io/en/stable/04_user_guide/04_data_catalog.html
#
# We support interacting with a variety of data stores including local file systems, cloud, network and HDFS
#
# An example data set definition can look as follows:
#
#bikes:
#  type: pandas.CSVDataSet
#  filepath: "data/01_raw/bikes.csv"
#
#weather:
#  type: spark.SparkDataSet
#  filepath: s3a://your_bucket/data/01_raw/weather*
#  file_format: csv
#  credentials: dev_s3
#  load_args:
#    header: True
#    inferSchema: True
#  save_args:
#    sep: '|'
#    header: True
#
#scooters:
#  type: pandas.SQLTableDataSet
#  credentials: scooters_credentials
#  table_name: scooters
#  load_args:
#    index_col: ['name']
#    columns: ['name', 'gear']
#  save_args:
#    if_exists: 'replace'
#    # if_exists: 'fail'
#    # if_exists: 'append'
#
# The Data Catalog supports being able to reference the same file using two different DataSet implementations
# (transcoding), templating and a way to reuse arguments that are frequently repeated. See more here:
# https://kedro.readthedocs.io/en/stable/04_user_guide/04_data_catalog.html

#
# This is a data set used by the "Hello World" example pipeline provided with the project
# template. Please feel free to remove it once you remove the example pipeline.

example_iris_data:
  type: CSVLocalDataSet
  filepath: data/01_raw/iris.csv