Hands-on Cloud Analytics with Microsoft Azure Stack: Transform Your Data to Derive Powerful Insights Using Microsoft Azure

by Prashila Naik

Length: 306 pages
Edition: 1
Language: English
Publisher: BPB Publications
Publication Date: 2020-11-11
ISBN-10: 9389898145
ISBN-13: 9789389898149
Sales Rank: #4616267 (See Top 100 Books)

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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

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.

About the Authors

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

Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Reviewer
Acknowledgement
Preface
Errata
Table of Contents
1. Data and its Power
    Structure
    Objective
    1.1 Introduction to data
    1.2 Types of data
    1.3 Characteristics of data
    1.4 Where does data gets generated
    1.5 How different datasets link to each other to produce more data
    Conclusion
    Multiple choice questions
        Answers
2. Evolution of Analytics and its Types
    Structure
    Objective
    2.1 Reporting and visualization
    2.2 Descriptive analytics
    2.2 Diagnostic analytics
    2.3 Predictive analytics
    2.4 Prescriptive analytics
    2.5 Other types of machine learning
    Conclusion
    Multiple choice questions
        Answers
3. Internet of Things
    Structure
    Objective
    3.1 IoT
    3.2 Sensors
    3.3 Gateway
    3.4 Edge
    3.5 Automation
    3.6 Where IoT can be used?
    Conclusion
    Multiple choice questions
        Answers
4. AI and ML
    Structure
    Objective
    4.1 Introduction to AI and ML
    4.2 Supervised learning
    4.3 Unsupervised learning
    4.4 Deep learning
    4.5 Models
    4.6 Cognitive
    Conclusion
    Multiple choice questions
        Answers 
5. Why Cloud?
    Structure
    Objective
    5.1 Introduction to cloud
    5.2 Types of cloud
        5.2.1 IaaS
        5.2.2 PaaS
        5.2.3 SaaS
    5.3 Compute
    5.4 Storage
    5.5 Pay-as-you-go
    Conclusion
    Multiple choice questions
        Answers
6. What is a Data Lake and a Modern Datawarehouse/Mart
    Structure
    Objective
    6.1 Advent of data lakes
    6.2 Data lake high-level architecture
    6.3 Data ingestion
    6.4 Data storage
    6.5 Data transformation/processing
    6.6 Data quality
    6.7 Data lineage
    6.8 Data cataloging
    6.9 Auditing
    6.10 Logging
    6.11 Monitoring
    6.12 Orchestration
    6.13 Reporting/data visualization
    6.14 Virtualization
    6.15 Modern data warehouse/datamart
    Conclusion
    Multiple choice questions
        Answers
7. Introduction to Azure Services
    Structure
    Objective
    7.1 Azure Data Factory
    7.2 Azure Virtual Machine
    7.3 Azure Synapse Analytics
    7.4 Azure BOT Service
    7.5 Azure Databricks
    7.6 Azure Data Explorer
    7.7 Azure Blockchain Service (preview)
    7.8 App Service
    7.9 Azure Web App
    7.10 Azure Data Catalog
    7.11 Azure Data Share
    7.12 Azure Functions
    7.13 Azure DevOps
    7.14 Azure DevTest Labs
    7.15 Azure SQL Database
    7.16 Azure ExpressRoute
    7.17 Azure Sentinel
    7.18 Azure database for PostgreSQL
    7.19 Azure IoT Hub
    7.20 Azure IoT Edge
    7.21 Azure Backup
    7.22 Azure Maps
    7.23 Azure Content Delivery Network (CDN)
    7.24 Azure Active Directory
    7.25 Azure Machine Learning
    7.26 Azure Stream Analytics
    7.27 Azure Time Series Insights
    7.28 Azure Cosmos DB
    7.29 Azure Advisor
    7.30 Azure Automation
    7.31 Azure Cognitive Search
    7.32 Computer Vision
    7.33 Face
    7.34 Content moderator
    7.35 Azure Data Lake Storage
    7.36 Azure Analysis Service
    7.37 Logic apps
    7.38 Azure API for FHIR
    7.39 Azure Database Migration Service
    7.40 Azure Cache for Redis
    7.41 Event Grid
    7.42 Azure SQL Database Edge (Preview)
    Conclusion
    Multiple choice questions
        Answers
8. Types of Data
    Structure
    Objective
    8.1 Traditional operational systems like Enterprise Resource Planning (ERP)
    8.2 Sensor data
    8.3 Historical data and current data
    8.4 Real-time and batch usage of data
    Conclusion
    Multiple choice questions
        Answers
9. Azure Data Factory
    Structure
    Objective
    9.1 Runtime
    9.2 Pipelines
    9.3 Linked service
    9.4 Data flows
    9.5 SSIS
    9.6 Custom and Web ADF activities
    9.7 Pricing
    Conclusion
    Multiple choice questions
        Answers
10. Stream Analytics
    Structure
    Objective
    10.1 Pipeline architecture
    10.2 Input
    10.3 Output
    10.4 Query and partition (transform)
    10.5 Streaming units
    10.6 Geospatial data
    10.7 Integration with Azure Machine Learning
    10.8 Recommended usage scenarios
    10.9 Pricing
    Conclusion
    Multiple choice questions
        Answers
11. Azure Data Lake Store and Azure Storage
    Structure
    Objective
    11.1 Azure Data Lake Storage Gen 2
        11.1.1 Query acceleration (currently preview)
        11.1.2 Creating an ADLS Gen 2 account
    11.2 Folders
    11.3 Hierarchy namespace
    11.4 APIs
        11.4.1 File operations (for the file system)
        11.4.2 Path operations (for directories)
    11.5 Access from Power BI
    11.6 Connectivity to other Azure components
        11.6.1 Data ingestion
        11.6.2 Events ingestion
        11.6.3 Streaming data
        11.6.4 Processing data
        11.6.5 Extracting data out of ADLS Gen 2
        11.6.6 Other services
    11.7 Azure Storage
    Conclusion
    Multiple choice questions
        Answers
12. Cosmos DB
    Structure
    Objective
    12.1 Fitment and use cases
    12.2 Graph database
    12.3 Multi-model database
    12.4 SQL query
    12.5 Consistency choices
    12.6 Partitioning
    12.7 Analytics
    12.8 Pricing
    Conclusion
    Multiple choice questions
        Answers
13. Synapse Analytics
    Structure
    Objective
    13.1 Features other than storage and why this is the most powerful storage choice
    13.2 Security options
    13.3 Data querying
    13.4 Compute and storage
    13.5 Apache Spark and SQL engines
    13.6 External tables
    13.7 Real-time analytics
    13.8 Azure Synapse Analytics workspace (in preview as this being written)
    Conclusion
    Multiple choice questions
        Answers
14. Azure Databricks
    Structure
    Objective
    14.1 Azure Databricks
        14.1.1 Analytical workloads
    14.2 Apache Spark environment
    14.3 Mount storage
    14.4 Workspace
    14.5 Clusters and auto-scale
    14.6 Notebooks
    14.7 Jobs
    14.8 Machine Learning
        14.8.1 Apache Spark MLlib
    14.9 MLFlow
    14.10 Deep learning
        14.10.1 TensorFlow
        14.10.2 Keras
        14.10.3 PyTorch
        14.10.4 Model inference
        14.10.5 Reference solutions
    14.11 Graph analysis
    14.12 Genomics
    14.13 Structured streaming
    14.14 Spark SQL
    14.15 Spark streaming
    Conclusion
    Multiple choice questions
        Answers
15. Azure Analysis Services
    Structure
    Objective
    15.1 In memory versus direct query
    15.2 Multidimensional versus tabular
    15.3 Models in AAS
    15.4 Size tiers and how to pick
    15.5 Pause, resume, and other concepts
    15.6 DAX
    15.7 Compression
    15.8 Scale up and down
    15.9 Firewall
    15.10 RLS (Row Level Security)
    15.11 Automation
    15.12 AAS in Power BI
    Conclusion
    Multiple choice questions
        Answers
16. Power BI
    Structure
    Objective
    16.1 Introduction to Reports
    16.2 Power BI desktop
    16.3 Self-service data preparation with dataflows
    16.4 PBI Premium
    16.5 Power BI Q&A
        16.5.1 From dashboard in Power BI service
        16.5.2 Featured questions
        16.5.3 Q&A Visuals
        16.5.4 Linguistic schema
        16.5.5 Phrasing
        16.5.6 Attributes phrasing
        16.5.7 Name phrasing
        16.5.8 Multiple phrasings
        16.5.9 Types of visuals in Power BI
        16.5.10 R visual
        16.5.11 Power Apps visual
    16.6 Data sources
    Conclusion
    Multiple choice questions
        Answers
17. Azure Machine Learning
    Structure
    Objective
    17.1 ML Studio (Designer)
        17.1.1 Pipelines
        17.1.2 Compute resources
        17.1.3 Deploy
        17.1.4 Publish
    17.2 Choosing an algorithm
        17.2.1 Predict values
        17.2.2 Generate recommendations
        17.2.3 Predict between several categories
        17.2.4 Extract information from text
        17.2.5 Discover structure
        17.2.6 Find unusual occurrences
        17.2.7 Predict between two categories
        17.2.8 Image classification
    17.3 Azure ML pipelines
    17.4 Automated ML
        17.4.1 Regression model
        17.4.2 Time series
        17.4.3 Understanding ML results
    17.5 Overfitting challenges
    17.5 Imbalanced data
    17.6 MLOps
        17.6.1 Scoring
        17.6.2 Retrain model on new data
    17.7 Interpretability
    17.8 Deep learning
    17.9 Azure AI gallery
    17.10 Examples to try
        Example 1: Forecasting model with Auto ML
        Example 2: Classification model for handwritten digits with Keras library
        Example 3: Simple logistic regression model using R
        Example 4: Multi-class image classification
    17.11 Integration with other services
    Conclusion
    Multiple choice questions
        Answers
18. Sample Real-Time and Batch Architectures and Synergies
    Structure
    Objective
    18.1 Real-time streaming
        18.1.1 Apache Kafka
        18.1.2 Apache Storm on HDInsight
    18.2 Real-time analytics
        18.2.1 Real-time anomaly detection
        18.2.2 Frozen foods movement
        18.2.3 Clickstreams real-time analysis
        18.2.4 Live match analytics
    18.3 Feedback into systems
    18.4 Batch ingestion and processing
    18.5 Lambda architecture
    18.6 Kappa architecture
    Conclusion
    Multiple choice questions
        Answers
19. Azure Data Catalog
    Structure
    Objective
    19.1 Data asset
    19.2 Data democratization
    19.3 How Data Catalog works
    19.4 Enrich
    19.5 Discover
    19.6 Consume
    19.7 Business glossary
    Conclusion
    Multiple choice questions
        Answers
20. Azure Active Directory
    Structure
    Objective
    20.1 On-prem to Azure AD sync
    20.2 User principal
    20.3 Application/service principal
    20.4 Single sign-on
    20.5 MFA (Multi-Factor Authentication)
    20.6 Add subscription to the tenant
    20.7 Conditional access
    Conclusion
    Multiple choice questions
        Answers
21. Azure Web Apps
    Structure
    Objective
    21.1 Platforms and types of apps
    21.2 Load balancing
    21.3 CI/CD
    21.4 Security and networking
    21.5 App with DB
    21.6 Web app in analytics
    Conclusion
    Multiple choice questions
        Answers
22. Power Apps 
    Structure
    Objective
    22.1 Power apps basics
    22.2 Connectors
    22.3 Canvas apps
    22.4 Model-driven apps
    22.5 Portals
    22.6 AI Builder
    22.6 Power apps plans
    Conclusion
    Multiple choice questions
        Answers
23. IoT Time Series Insights
    Structure
    Objective
    23.1 Data Flow
    23.2 Integration
    Conclusion
    Multiple choice questions
        Answers
24. Azure Cognitive Services
    Structure
    Objective
    24.1 Vision APIs
    24.2 Speech APIs
    24.3 Search APIs
    24.4 Language APIs
    24.5 Decision APIs
    Conclusion
    Multiple choice questions
        Answers
25. Azure Logic Apps
    Structure
    Objective
    25.1 Connectors
    25.2 Actions
    25.3 Triggers
    25.4 Workflows
    25.5 Pricing
    25.6 Logic Apps for B2B capabilities
    Conclusion
    Multiple choice questions
        Answers
26. Azure Virtual Machine
    Structure
    Objective
    26.1 Machine learning server
    26.2 Data science virtual machines
    26.3 GPU virtual machines
    26.4 Virtual machines tiers
    26.5 Usage as integrated runtimes and OPDGs (on-premise data gateway)
    26.6 VM images for 3P software
    26.7 Other concepts
    Conclusion
    Multiple choice questions
        Answers
27. Azure Functions
    Structure
    Objective
    27.1 Serverless compute
    27.2 Stateless
    27.3 Machine learning
    27.4 IoT Edge and Functions
    27.5 Functions Pricing plans
    Conclusion
    Multiple choice questions
        Answers
28. Azure Containers
    Structure
    Objective
    28.1 Container registry
    28.2 Pipelines
    28.3 Geo-replication and scaling
    28.4 Patching images
    28.5 Use cases
    Conclusion
    Multiple choice questions
        Answers
29. Azure Kubernetes
    Structure
    Objective
    29.1 Serverless Kubernetes
    29.2 Security
    29.3 CI/CD Continuous Integration Continuous Deployment
    29.4 Cloud migration
    29.5 Compute for analytics workloads through Azure ML
    29.6 IoT Edge device deployment
    29.7 Streaming
    29.8 Ease and benefits of using AKS
    Conclusion
    Multiple choice questions
        Answers
30. Use Case 1
    Structure
    Objective
    30.1 Problem
    30.2 Analysis
    30.3 Solution
        30.3.1 Design
        30.3.2 Implementation
            30.3.2.1 Data Lake
        30.2.3 Descriptive and diagnostic reports
    Conclusion
    Multiple choice questions
        Answers
31. Use Case 2
    Structure
    Objective
    31.1 Problem
    31.2 Analysis
    31.3 Solution
        31.3.1 Recommendations
        31.3.2 Community building
        31.3.3 Sentiment analysis
    Conclusion
    Multiple choice questions
        Answers

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