دانلود کتاب Data Lakehouse in Action: Architecting a modern and scalable data analytics platform

فهرست مطالب :

Cover\nTitle Page\nCopyright\nDedication\nContributors\nTable of Contents\nPreface\nPART 1: Architectural Patterns for Analytics\nChapter 1: Introducing the Evolution of Data Analytics Patterns\n Discovering the enterprise data warehouse era\n Exploring the five factors of change\n The exponential growth of data\n The increase in compute\n The decrease in storage cost\n The rise of artificial intelligence\n The advancement of cloud computing\n Investigating the data lake era\n Introducing the data lakehouse paradigm\n Summary\n Further reading\nChapter 2: The Data Lakehouse Architecture Overview\n Developing a system context for a data lakehouse\n Data providers\n Data consumers\n Developing a logical data lakehouse architecture\n Data ingestion layer\n Data lake layer\n Data processing layer\n Data serving layer\n Data analytics layer\n Data governance layer\n Data security layer\n Developing architecture principles\n Disciplined at the core, flexible at the edges\n Decouple compute and storage\n Focus on functionality rather than technology\n Create a modular architecture\n Perform active cataloging\n Summary\n Further reading\nPART 2: Using NLP to Accelerate Business Outcomes\nChapter 3: Ingesting and Processing Data in a Data Lakehouse\n Ingesting and processing batch data\n Differences between the ETL and ELT patterns\n Batch data processing in a data lakehouse\n Ingesting and processing streaming data\n Streaming data sources\n Extraction-load\n Transform-load\n Bringing it all together\n The batch layer\n The speed layer\n The serving layer\n Summary\n Further reading\nChapter 4: Storing and Serving Data in a Data Lakehouse\n Storing data in the data lake layer\n Data lake layer\n Common data formats\n Storing data in the data serving layer\n SQL-based serving\n NoSQL-based serving\n Data-sharing technology\n Summary\n Further reading\nChapter 5: Deriving Insights from the Data Lakehouse\n Discussing the themes of analytics capabilities\n Descriptive analytics\n Advanced analytics\n Enabling analytics capabilities in a data lakehouse\n The analytics sandbox service\n The business intelligence service\n The AI service\n Summary\n Further reading\nChapter 6: Applying Data Governance in the Data Lakehouse\n The 3-3-3 framework for data governance\n The three objectives of data governance\n The three pillars of data governance\n The three components of the data governance layer\n Implementing data governance policy management\n Implementing the data catalog\n Implementing data quality\n Summary\n Further reading\nChapter 7: Applying Data Security in a Data Lakehouse\n Realizing the data security components in a data lakehouse\n Using IAM in a data lakehouse\n Methods of data encryption in a data lakehouse\n Methods of data masking in a data lakehouse\n Methods of implementing network security in a data lakehouse\n Summary\n Further reading\nPART 3: Implementing and Governing a \rData Lakehouse\nChapter 8: Implementing a Data Lakehouse on Microsoft Azure\n Why is cloud computing apt for implementing a data lakehouse?\n The rapid advancements in cloud computing facilitate data analytics\n Architectural flexibility is native to the cloud\n Cloud computing enables tailored cost control\n Implementing a data lakehouse on Microsoft Azure\n The data ingestion layer on Microsoft Azure\n The data processing layer on Microsoft Azure\n Summary\n Further reading\nChapter 9: Scaling the Data Lakehouse Architecture\n The need for a macro-architectural pattern for analytics\n Implementing a data lakehouse in a macro-architectural pattern\n The hub-spoke pattern\n The data mesh pattern\n Choosing between hub-spoke and data mesh\n Summary\n Further reading\nIndex\nAbout Packt\nOther Books You May Enjoy