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Full Description
Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will
This book includes comprehensive coverage of how:
To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure
The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem
These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions
Data platform professionals who have been working on relational data stores, non-relational data stores, and big data technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure.
What Will You Learn
You will understand the:
Concepts of data lake analytics, the modern data warehouse, and advanced data analytics
Architecture patterns of the modern data warehouse and advanced data analytics solutions
Phases—such as Data Ingestion, Store, Prep and Train, and Model and Serve—of data analytics solutions and technology choices available on Azure under each phase
In-depth coverage of real-time and batch mode data analytics solutions architecture
Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight
Who This Book Is For
Data platform professionals, database architects, engineers, and solution architects
Contents
Chapter 1: Data Lake Analytics Concepts.- Chapter 2: Building Blocks of Data Analytics.- Chapter 3: Data Analytics on Public Cloud.- Chapter 4: Data Ingestion.- Chapter 5: Data Storage.- Chapter 6: Data Preparation and Training Part I.- Chapter 7: Data Preparation and Training Part II.- Chapter 8: Model and Serve.- Chapter 9: Summary.
