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spaceflight catalog
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# 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/data/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/data/data_catalog.html | |
companies: | |
type: pandas.CSVDataSet | |
filepath: data/01_raw/companies.csv | |
# more about layers in the Data Engineering Convention: | |
# https://kedro.readthedocs.io/en/stable/tutorial/visualise_pipeline.html#interact-with-data-engineering-convention | |
layer: raw | |
reviews: | |
type: pandas.CSVDataSet | |
filepath: data/01_raw/reviews.csv | |
layer: raw | |
shuttles: | |
type: pandas.ExcelDataSet | |
filepath: data/01_raw/shuttles.xlsx | |
layer: raw | |
data_processing.preprocessed_companies: | |
type: pandas.ParquetDataSet | |
filepath: data/02_intermediate/preprocessed_companies.pq | |
layer: intermediate | |
data_processing.preprocessed_shuttles: | |
type: pandas.ParquetDataSet | |
filepath: data/02_intermediate/preprocessed_shuttles.pq | |
layer: intermediate | |
model_input_table: | |
type: pandas.ParquetDataSet | |
filepath: data/03_primary/model_input_table.pq | |
layer: primary | |
data_science.active_modelling_pipeline.regressor: | |
type: pickle.PickleDataSet | |
filepath: data/06_models/regressor_active.pickle | |
versioned: true | |
layer: models | |
data_science.candidate_modelling_pipeline.regressor: | |
type: pickle.PickleDataSet | |
filepath: data/06_models/regressor_candidate.pickle | |
versioned: true | |
layer: models | |
data_science.candidate_modelling_pipeline.metrics: | |
type: tracking.MetricsDataSet | |
filepath: data/09_tracking/metrics_active.json | |
data_science.active_modelling_pipeline.metrics: | |
type: tracking.MetricsDataSet | |
filepath: data/09_tracking/metrics_candidate.json | |
data_processing.companies_columns: | |
type: tracking.JSONDataSet | |
filepath: data/09_tracking/companies_columns.json |
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