Databricks Platform Essentials (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"Databricks Platform Essentials" Unlock the full potential of cloud-native analytics and intelligent data engineering with "Databricks Platform Essentials." This comprehensive guide traces the evolution of Databricks from its roots in Apache Spark to its present-day role as an industry-leading unified analytics platform. Through clear explanations of Databricks' multi-layered architecture, lakehouse paradigm, and broad multi-cloud integrations, readers gain a foundational understanding of how the platform bridges data lakes and warehouses, delivers robust security and governance, and integrate...
"Databricks Platform Essentials"
Unlock the full potential of cloud-native analytics and intelligent data engineering with "Databricks Platform Essentials." This comprehensive guide traces the evolution of Databricks from its roots in Apache Spark to its present-day role as an industry-leading unified analytics platform. Through clear explanations of Databricks' multi-layered architecture, lakehouse paradigm, and broad multi-cloud integrations, readers gain a foundational understanding of how the platform bridges data lakes and warehouses, delivers robust security and governance, and integrates seamlessly with major cloud ecosystems.
The book delves into the mechanics of the Databricks environment, covering workspace organization, collaborative development with notebooks, and sophisticated version control strategies. By detailing cluster management, autoscaling, and high-availability patterns, it equips practitioners to design resilient and cost-efficient compute infrastructures. Chapters on data engineering illustrate best practices in ingestion, ETL pipeline design, Delta Lake optimization, and operationalizing robust workflows, while advanced sections explore distributed machine learning workflows, MLOps with MLflow, responsible AI, and governance in large-scale data projects.
Purpose-built for data engineers, analysts, architects, and platform administrators, "Databricks Platform Essentials" provides actionable guidance for real-time streaming, deep security and compliance controls, and the extensibility needed for complex modern data ecosystems. With practical solutions for integration, performance tuning, disaster recovery, and cost optimization, this book empowers teams to confidently deliver high-value analytics and machine learning on Databricks-at scale and with enterprise-grade reliability.
Unlock the full potential of cloud-native analytics and intelligent data engineering with "Databricks Platform Essentials." This comprehensive guide traces the evolution of Databricks from its roots in Apache Spark to its present-day role as an industry-leading unified analytics platform. Through clear explanations of Databricks' multi-layered architecture, lakehouse paradigm, and broad multi-cloud integrations, readers gain a foundational understanding of how the platform bridges data lakes and warehouses, delivers robust security and governance, and integrates seamlessly with major cloud ecosystems.
The book delves into the mechanics of the Databricks environment, covering workspace organization, collaborative development with notebooks, and sophisticated version control strategies. By detailing cluster management, autoscaling, and high-availability patterns, it equips practitioners to design resilient and cost-efficient compute infrastructures. Chapters on data engineering illustrate best practices in ingestion, ETL pipeline design, Delta Lake optimization, and operationalizing robust workflows, while advanced sections explore distributed machine learning workflows, MLOps with MLflow, responsible AI, and governance in large-scale data projects.
Purpose-built for data engineers, analysts, architects, and platform administrators, "Databricks Platform Essentials" provides actionable guidance for real-time streaming, deep security and compliance controls, and the extensibility needed for complex modern data ecosystems. With practical solutions for integration, performance tuning, disaster recovery, and cost optimization, this book empowers teams to confidently deliver high-value analytics and machine learning on Databricks-at scale and with enterprise-grade reliability.
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