Hands-on Cloud Analytics with Microsoft Azure Stack

"One of the best new Azure Machine Learning books" - BookAuthority Explore and work with various Microsoft Azure services for real-time Data Analytics Key Features Understanding what Azure can do with your dataUnderstanding the analytics services offered by AzureUnderstand how data can be transformed to generate more dataUnderstand what is done after a Machine Learning model is built Go through some Data Analytics real-world use cases Description Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services.The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads.You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. What will you learn Explore and work with various Azure servicesOrchestrate and ingest data using Azure Data FactoryLearn how to use Azure Stream AnalyticsGet to know more about Synapse Analytics and its featuresLearn how to use Azure Analysis Services and its functionalities Who this book is for This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Table of Contents 1. Data and its power2. Evolution of Analytics and its Types3. Internet of Things4. AI and ML5. Why cloud6. What are a data lake and a modern datamart7. Introduction to Azure services8. Types of data9. Azure Data Factory10. Stream Analytics11. Azure Data Lake Store and Azure Storage12. Cosmos DB13. Synapse Analytics14. Azure Databricks15. Azure Analysis Services16. Power BI17. Azure Machine Learning18. Sample Architectures and synergies - Real-Time and Batch19. Azure Data Catalog20. Azure Active Directory21. Azure Webapps22. Power apps23. Time Series Insights24. Azure Cognitive Services25. Azure Logicapps26. Azure VM27. Azure Functions28. Azure Containers29. Azure Kubernetes Service30. Use Case 131. Use Case 2 About the Author Prashila Naik has over 16 years of experience in the tech sector. She has worked for multiple global organizations, primarily in the data and analytics space. She has seen data and analytics grow from strength to strength and thinks it will always be one of the most interesting areas in technology ever.