Hands-on Cloud Analytics with Microsoft Azure Stack

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.

Tagline

Explore and work with various Microsoft Azure services for real-time Data Analytics

Key Features

• Understanding what Azure can do with your data

• Understanding the analytics services offered by Azure

• Understand how data can be transformed to generate more data

• Understand what is done after a Machine Learning model is built 

• Go through some Data Analytics real-world use cases

What Will You Learn 

• Explore and work with various Azure services

• Orchestrate and ingest data using Azure Data Factory

• Learn how to use Azure Stream Analytics

• Get to know more about Synapse Analytics and its features

• Learn 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.

1. Data and its power
2. Evolution of Analytics and its Types
3. Internet of Things
4. AI and ML
5. Why cloud
6. What are a data lake and a modern datamart
7. Introduction to Azure services
8. Types of data
9. Azure Data Factory
10. Stream Analytics
11. Azure Data Lake Store and Azure Storage
12. Cosmos DB
13. Synapse Analytics
14. Azure Databricks
15. Azure Analysis Services
16. Power BI
17. Azure Machine Learning
18. Sample Architectures and synergies - Real-Time and Batch
19. Azure Data Catalog
20. Azure Active Directory
21. Azure Webapps
22. Power apps
23. Time Series Insights
24. Azure Cognitive Services
25. Azure Logicapps
26. Azure VM
27. Azure Functions
28. Azure Containers
29. Azure Kubernetes  Service
30. Use Case 1
31. Use Case 2

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. She 

She is also a writer who primarily writes creative fiction and non-fiction, as well as an occasional translator. Her short stories have been published in various leading literary journals in India and elsewhere.

Your LinkedIn Profile: https://www.linkedin.com/in/prashila-naik-7645604