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Data Engineering with Azure Databricks: Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks
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Data Engineering with Azure Databricks: Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks
Data Engineering with Azure Databricks: Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks
物品 #: 273774596

Data Engineering with Azure Databricks: Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks

90% of respondents would recommend this to a friend

物品 #: 273774596

HKD 535

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What Stands Out

Scalable Solutions
Leverage Azure Databricks to effortlessly design and build scalable data pipelines, ensuring efficiency and reliability for large datasets in real-time analytics.
Optimized Performance
Utilize advanced analytics solutions that optimize performance, enabling organizations to generate insights quickly without the need for complex infrastructure management.
User-Friendly Interface
Experience an intuitive interface that simplifies data engineering processes, empowering both data engineers and analysts to collaborate effectively and enhance productivity.

產品詳情

Shop Data Engineering with Azure Databricks: Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks online at a best price in 香港. 180610637X
  • Master end-to-end data engineering on Azure Databricks. From data ingestion and Delta Lake to CI/CD and real-time streaming, build secure, scalable, and performant data solutions with Spark, Unity Catalog, and ML tools.Key FeaturesBuild scalable data pipelines using Apache Spark and Delta LakeAutomate workflows and manage data governance with Unity CatalogLearn real-time processing and structured streaming with practical use casesImplement CI/CD, DevOps, and security for production-ready data solutionsExplore Databricks-native ML, AutoML, and Generative AI integrationBook DescriptionData Engineering with Azure Databricks is your essential guide to building scalable, secure, and high-performing data pipelines using the powerful Databricks platform on Azure. Designed for data engineers, architects, and developers, this book demystifies the complexities of Spark-based workloads, Delta Lake, Unity Catalog, and real-time data processing.Beginning with the foundational role of Azure Databricks in modern data engineering, you’ll explore how to set up robust environments, manage data ingestion with Auto Loader, optimize Spark performance, and orchestrate complex workflows using tools like Azure Data Factory and Airflow.The book offers deep dives into structured streaming, Delta Live Tables, and Delta Lake’s ACID features for data reliability and schema evolution. You’ll also learn how to manage security, compliance, and access controls using Unity Catalog, and gain insights into managing CI/CD pipelines with Azure DevOps and Terraform.With a special focus on machine learning and generative AI, the final chapters guide you in automating model workflows, leveraging MLflow, and fine-tuning large language models on Databricks. Whether you're building a modern data lakehouse or operationalizing analytics at scale, this book provides the tools and insights you need.What you will learnSet up a full-featured Azure Databricks environmentImplement batch and streaming ingestion using Auto LoaderOptimize Spark jobs with partitioning and cachingBuild real-time pipelines with structured streaming and DLTManage data governance using Unity CatalogOrchestrate production workflows with jobs and ADFApply CI/CD best practices with Azure DevOps and GitSecure data with RBAC, encryption, and compliance standardsUse MLflow and Feature Store for ML pipelinesBuild generative AI applications in DatabricksWho this book is forThis book is for data engineers, solution architects, cloud professionals, and software engineers seeking to build robust and scalable data pipelines using Azure Databricks. Whether you're migrating legacy systems, implementing a modern lakehouse architecture, or optimizing data workflows for performance, this guide will help you leverage the full power of Databricks on Azure. A basic understanding of Python, Spark, and cloud infrastructure is recommended.Table of ContentsThe Role of Azure Databricks in Modern Data EngineeringSetting up an End-To-End Azure Databricks EnvironmentData Ingestion Strategies for Azure DatabricksData Engineering with Apache SparkBuilding Real-Time Data PipelinesWorking with Delta Lake: ACID Transactions and Schema EvolutionAutomating Data Systems with Lakeflow Spark Declarative PipelinesOrchestrating Data Workflows: From Notebooks to ProductionCI/CD and DevOps for Azure DatabricksOptimizing Query Performance and Cost ManagementSecurity, Compliance, and Data GovernanceMachine Learning and AI on Databricks
Publisher Packt Publishing
Publication date 30 April 2026
Language English
Print length 412 pages
ISBN-10 180610637X
ISBN-13 978-1806106370
Item weight 703 g
Dimensions 19.05 x 2.36 x 23.5 cm

Who Should Buy?

Suitable For
  • Data Engineers

    Professionals focusing on building, designing, and optimizing data pipelines using Azure Databricks for big data solutions.

  • Data Analysts

    Individuals looking to enhance their analytics capabilities with efficient data processing and visualization tools offered by Databricks.

  • Cloud Architects

    Technical experts tasked with designing cloud-based data solutions will find this product essential for creating scalable architectures.

Not Suitable For
  • Novice Users

    Beginners without prior experience in data engineering may struggle to understand and utilize the advanced features effectively.

產品敘述

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Data Warehousing Editorial Review

Data Engineering With Azure Databricks offers an in-depth exploration into designing, building, and optimizing scalable data pipelines and analytics solutions using Azure Databricks. This comprehensive guide spans 412 pages and is published by Packt Publishing, making it an essential resource for data professionals looking to leverage Azure's powerful capabilities for data engineering tasks. Readers will appreciate the modern approach to data architecture and the effective strategies presented to enhance performance in analytics projects. The clarity and organization of the content, along with hands-on examples, have received positive feedback from many users, making it a valuable addition to any data engineer's library.

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優點

  • Comprehensive guide for data engineering
  • Practical examples enhance learning
  • Clear and organized content structure
  • Written by industry experts
  • Suitable for both beginners and professionals

缺點

  • Some advanced topics may require prior knowledge

Platform Trust & Buyer Confidence

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HKD 535

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