O 39;reilly Data Mesh Pdf Download !!HOT!!
Kam Bergmann
We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale.
Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance.
@media screen and (max-width:414px) .data-mesh-bio display: none;Zhamak Dehghani works with ThoughtWorks as the director of emerging technologies in North America, with a focus on distributed systems and data architecture. She has a deep passion for decentralized technology solutions and has founded the concept of Data Mesh. She is a member of ThoughtWorks Technology Advisory Board and contributes to the creation of ThoughtWorks Technology Radar. Zhamak has worked as a software engineer and architect for over 20 years and has contributed to multiple patents in distributed computing communications, as well as embedded device technologies.
It speaks to the very core of our professional identity. Can it be trusted? Is it good enough to make an impact? Ostensibly we are referring to the data, but at times we may as well be speaking into a mirror.
We knew the best practices and methodologies we had collected from our personal experience, as well as that of our colleagues and customers, could help data teams start to change that equation. And we knew with that change would come a data renaissance.
For decades, teams have struggled to measure, maintain, improve, and predict data quality, and over the past few years, the speed and scale at which we ingest, process, transform, and analyze data have made these challenges even harder. This lack of visibility into the health of our data leads to data downtime, periods of time when data is missing, inaccurate, or otherwise erroneous, and a leading reason why data quality initiatives fail.
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