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¿µ¹®Ç¥Áظí DataOps - Part 1: Overview, Functionalities and Use Cases
Çѱ۳»¿ë¿ä¾à µ¥ÀÌÅͿɽº´Â ÀÌ¿ëÀÚ¿¡°Ô ÃÖÀûÀÇ µ¥ÀÌÅ͸¦ È¿°úÀûÀ¸·Î Á¦°øÇϱâ À§ÇØ, µ¥ÀÌÅÍ »ý¾Ö Áֱ⠸¦ ÀÚµ¿È­ÇÏ¿© µ¥ÀÌÅÍÀÇ È帧À» ¿øÈ°ÇÏ°Ô ÇÏ´Â µ¥ÀÌÅÍ °ü¸® ¹æ½Ä ¶Ç´Â °ü¸® ¹æ½ÄÀ» ±¸Çö ÇÑ Çù¾÷ȯ°æÀ» ¸»ÇÑ´Ù. µ¥ÀÌÅͿɽº´Â ¼Óµµ, Ç°Áú ¹× ¾ÈÁ¤¼ºÀÌ ÇÙ½ÉÀÎ ºòµ¥ÀÌÅÍ È¯°æ¿¡¼­ ¹ß»ýÇÏ´Â µ¥ÀÌÅÍ ¹®Á¦¸¦ ÇØ°áÇÏ´Â µ¥ ¸ñÀûÀÌ ÀÖ´Ù. µ¥ÀÌÅͿɽº´Â µ¥ÀÌÅÍ Á¢±Ù¼º, Á¤È®¼º, º¸¾È¼ºÀ» º¸ÀåÇϸç, µ¥ÀÌÅÍ ºÐ¼® ½Ã°£À» ÁÙÀÌ°í, Çù¾÷À» °³¼±ÇÏ°í µ¥ÀÌÅÍ °ü¸® Åõ¸í¼ºÀ» È®º¸ÇÒ ¼ö ÀÖ´Ù.
µ¥ÀÌÅͿɽºÀÇ µ¥ÀÌÅÍ Ã³¸® ¹× °ü¸® ¹æ½ÄÀ» ±¸ÇöÇϱâ À§ÇØ ¿©·¯ ÀÌ¿ëÀÚ°¡ Çù¾÷ÇÒ ¼ö Àִ ȯ°æÀÌ Á¦°øµÈ´Ù. ºÐ»ê µ¥ÀÌÅÍ ÆÄÀÌÇÁ¶óÀÎ µµ±¸´Â ÀÌ¿ëÀÚµéÀÌ Çù¾÷À» ÅëÇØ, µ¥ÀÌÅÍ Ã³¸® ¸ñÀû¿¡ ¸Â´Â ÆÄÀÌÇÁ¶óÀÎÀ» »ý¼º, °ËÁõÇÏ°í ¿©·¯ »çÀÌÆ®¿¡ ÆÄÀÌÇÁ¶óÀÎÀÇ ÄÄÆ÷³ÍÆ®¸¦ ¹èÄ¡ ÇÏ¿© ½ÇÇàÇÒ ¼ö ÀÖµµ·Ï Áö¿øÇÑ´Ù.
¿µ¹®³»¿ë¿ä¾à DataOps refers to a collaborative environment or approach that implements data management practices, automating the data lifecycle to facilitate the smooth flow of data to effectively provide users with optimal data. DataOps aims to address data issues in the context of big data environments where speed, quality, and stability are crucial. It ensures data accessibility, accuracy, and security while reducing data analysis time, improving collaboration, and ensuring transparency in data management.
To implement DataOps data processing and management practices, an environment that allows multiple users to collaborate is provided. Distributed data pipeline tools support users in creating, validating, and deploying pipelines' components to various sites, enabling them to execute pipelines according to their data processing needs through collaboration.
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