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[ARCHIVED] This starter is no longer maintained.

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You can still find the starter code in older release tags: https://github.com/kedro-org/kedro-starters/tree/0.18.14/standalone-datacatalog

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The standalone-datacatalog Kedro starter

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This starter is unavailable in Kedro version 0.19.0 and beyond and the latest version of Kedro that supports it is Kedro 0.18.14. To check the version of Kedro you have installed, type kedro -V in your terminal window. To install a specific version of Kedro, e.g. 0.18.14, type pip install kedro==0.18.14.

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To create a project with this starter using kedro new, type the following (assuming Kedro version 0.18.14):

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kedro new --starter=standalone-datacatalog --checkout=0.18.14\n
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Introduction

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This starter, formerly known as mini-kedro, sets up a lightweight Kedro project that uses the Kedro Data Catalog as a registry for data without using any of the other features of Kedro.

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The starter comprises a minimal setup to use the traditional Iris dataset.

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Usage

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To create a new project based on this starter:

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kedro new --starter=standalone-datacatalog
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You can call the project any name you choose. When created, the project contains the following:

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# conf/base/catalog.yml\nexample_dataset_1:\n type: pandas.CSVDataset\n filepath: folder/filepath.csv\n\nexample_dataset_2:\n type: spark.SparkDataset\n filepath: s3a://your_bucket/data/01_raw/example_dataset_2*\n credentials: dev_s3\n file_format: csv\n save_args:\n   if_exists: replace
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df = catalog.load(\"example_dataset_1\")\ndf_2 = catalog.save(\"example_dataset_2\")
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