A01=Hubert Dulay
A01=Stephen Mooney
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Author_Hubert Dulay
Author_Stephen Mooney
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Category1=Non-Fiction
Category=UTR
COP=United States
data mesh Kafka real-time streaming data engineering stream processing data services
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Language_English
PA=Available
Price_€50 to €100
PS=Active
softlaunch
Product details
- ISBN 9781098130725
- Dimensions: 178 x 232mm
- Publication Date: 25 May 2023
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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Data lakes and warehouses have become increasingly fragile, costly, and difficult to maintain as data gets bigger and moves faster. Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services.
Authors Hubert Dulay and Stephen Mooney examine the vast differences between streaming and batch data meshes. Data engineers, architects, data product owners, and those in DevOps and MLOps roles will learn steps for implementing a streaming data mesh, from defining a data domain to building a good data product. Through the course of the book, you'll create a complete self-service data platform and devise a data governance system that enables your mesh to work seamlessly.
With this book, you will:
Design a streaming data mesh using Kafka
Learn how to identify a domain
Build your first data product using self-service tools
Apply data governance to the data products you create
Learn the differences between synchronous and asynchronous data services
Implement self-services that support decentralized data
Hubert Dulay is a systems & data engineer at Confluent. A veteran engineer with over 20 years of experience in big & fast data and MLOps, Hubert has consulted for many financial institutions, healthcare organizations, and telecommunications companies, providing simple solutions that solved many data problems. Stephen Mooney is an independent data scientist and data engineer serving multiple clients. With over 20 years of experience in big data, MLOps and data science, he has worked in many major companies across healthcare, retail, and the public sector. Through this experience Stephen has delivered many technical and functional projects throughout the entire product lifecycle.
