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Engineering Data Mesh in Azure Cloud

You're reading from  Engineering Data Mesh in Azure Cloud

Product type Book
Published in Mar 2024
Publisher Packt
ISBN-13 9781805120780
Pages 314 pages
Edition 1st Edition
Languages
Author (1):
Aniruddha Deswandikar Aniruddha Deswandikar
Profile icon Aniruddha Deswandikar

Table of Contents (23) Chapters

Preface Part 1: Rolling Out the Data Mesh in the Azure Cloud
Chapter 1: Introducing Data Meshes Chapter 2: Building a Data Mesh Strategy Chapter 3: Deploying a Data Mesh Using the Azure Cloud-Scale Analytics Framework Chapter 4: Building a Data Mesh Governance Framework Using Microsoft Azure Services Chapter 5: Security Architecture for Data Meshes Chapter 6: Automating Deployment through Azure Resource Manager and Azure DevOps Chapter 7: Building a Self-Service Portal for Common Data Mesh Operations Part 2: Practical Challenges of Implementing a Data Mesh
Chapter 8: How to Design, Build, and Manage Data Contracts Chapter 9: Data Quality Management Chapter 10: Master Data Management Chapter 11: Monitoring and Data Observability Chapter 12: Monitoring Data Mesh Costs and Building a Cross-Charging Model Chapter 13: Understanding Data-Sharing Topologies in a Data Mesh Part 3: Popular Data Product Architectures
Chapter 14: Advanced Analytics Using Azure Machine Learning, Databricks, and the Lakehouse Architecture Chapter 15: Big Data Analytics Using Azure Synapse Analytics Chapter 16: Event-Driven Analytics Using Azure Event Hubs, Azure Stream Analytics, and Azure Machine Learning Chapter 17: AI Using Azure Cognitive Services and Azure OpenAI Index Other Books You May Enjoy

The data mesh solution

As we learned in the Discovering the challenges of modern-day enterprises section, having a central data lake or data warehouse has several disadvantages, especially for large organizations. In the previous section, we learned about changes in data processing requirements driven by ML and advanced analytics and how data now needs to be treated like a product with its own complete life cycle.

To explain a data mesh in one sentence, a data mesh is a centrally managed network of decentralized data products. The data mesh breaks the central data lake into decentralized islands of data that are owned by the teams that generate the data. The data mesh architecture proposes that data be treated like a product, with each team producing its own data/output using its own choice of tools arranged in an architecture that works for them. This team completely owns the data/output they produce and exposes it for others to consume in a way they deem fit for their data. Here...

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