This book is recommended for students and professionals seeking practical mastery in Data Mesh, distributed data architecture, multi-cloud integration, governance, and data pipeline automation in modern corporate environments. The content covers everything from the fundamentals of Data Mesh to its implementation with leading frameworks such as AWS Lake Formation, Azure Synapse, Databricks, Google BigQuery, Kafka, Apache Spark, Kubernetes, Airflow, dbt, Snowflake, Terraform, Prometheus, Grafana, and ELK Stack.

Explore strategies for data decentralization, domain-oriented architecture, data product management, CI/CD pipeline automation, multidisciplinary team integration, monitoring, security, and scalability for high-performance data environments. Learn to build robust solutions, integrate heterogeneous sources, implement data governance practices, S3, IAM, deployment automation, observability, and compliance with LGPD/GDPR.

You will learn:

Essential concepts and pillars of Data Mesh: domains, data products, interoperability, and self-service platforms
Reference architecture with AWS, Azure, Google Cloud, Databricks, Snowflake, and Kubernetes
Orchestration of data pipelines with Apache Airflow, dbt, Spark, Kafka, and Terraform
Implementation of monitoring, observability, and automation with Prometheus, Grafana, and ELK Stack
Data integration in multi-cloud, container, and edge computing environments
Best practices in security, authentication, IAM, governance, and compliance
Deployment automation, backup, versioning, and troubleshooting in corporate environments

By the end, you will be able to apply Data Mesh to create scalable, secure, auditable, and business-oriented data platforms, optimizing analytical and operational workflows in data-driven companies of any size.

"About this title" may belong to another edition of this title.

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