000			01799nam a2200193Ia 4500
008			240825s9999 xx 000 0 und d
020			_a9789355421753 _qpbk
041			_aeng
082			_a005.74 _bMOS
100			_aMoses, Barr
245		0	_aData quality fundamentals : a practitioner's guide to building trustworthy data pipelines _c/Barr Moses, Lior Gavish, Molly Vorwerck
250			_a1st edition
260			_c2022 _aSebastopol, CA, _bO'Reilly media,
300			_axvi, 288 pages _bil,; _c23 cm.
504			_aindex
520			_aDo your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets
650			_aComputer Science Data mining
942			_cENGLISH
999			_c541937 _d541937