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Pandas"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1872701959":{"id":"text-b5c0dfdc0d","text":"\u003Cp\u003EPandas 라이브러리는 Python에서 데이터 엔지니어링에 가장 자주 사용되는 라이브러리 중 하나입니다. 다양한 기능을 갖춘 이 라이브러리는 데이터 엔지니어에게 강력한 데이터 조작 및 분석 기능을 제공합니다. Pandas는 가공 전 데이터를 전처리, 정제 및 변환하여 다운스트림 분석 또는 저장에 활용하는 데 사용됩니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05 snowflake-responsive-component-top-padding-none"},"title_v2_copy_copy":{"id":"title-v2-e10d6c2e31","additionalClasses":"headline-decoration","type":"heading4","lines":["2. Apache Airflow"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1568900679":{"id":"text-5d3e4b5712","text":"\u003Cp\u003EApache Airflow는 데이터 엔지니어가 워크플로우를 작성, 예약 및 모니터링할 수 있는 플랫폼 역할을 수행합니다. 이뿐만 아니라 쉽게 접근할 수 있는 직관적인 인터페이스를 제공하여, 데이터 엔지니어가 여러 작업을 생성, 예약 및 실행하고 복잡한 데이터 처리 파이프라인을 관리할 수 있도록 합니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-05b78c861f","additionalClasses":"headline-decoration","type":"heading4","lines":["3. Pyparsing"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_1632259302":{"id":"text-9046215dca","text":"\u003Cp\u003EPython 클래스 라이브러리인 Pyparsing을 사용하면 파싱(parsing) 상태 기계를 수동으로 구축하지 않아도 됩니다. 데이터 엔지니어는 Pyparsing으로 재귀 하향 파서(parser)를 빠르게 만들 수 있습니다.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_481585156":{"id":"title-v2-2f044ba611","additionalClasses":"headline-decoration","type":"heading4","lines":["4. TensorFlow"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613_725647343":{"id":"text-ce57aefb06","text":"\u003Cp\u003ETensorFlow는 머신러닝 및 딥러닝 응용 분야에서 널리 사용되는 Python 라이브러리로, 모델 학습과 모델 제공을 위한 다목적 플랫폼을 제공합니다. 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