Cloud Analytics with Microsoft Azure

✍ Scribed by Has Altaiar, Jack Lee, Michael Peña

• Publisher: Packt
• Year: 2021
• Tongue: English
• Leaves: 185
• Edition: 2
• Category: Library

✦ Table of Contents

Cover
FM
Table of Contents
Preface
Chapter 1: Introducing analytics on Azure
The power of data
Big data analytics
Internet of Things (IoT)
Machine learning
Artificial intelligence (AI)
DataOps
Why Microsoft Azure?
Security
Cloud scale
Top business drivers for adopting data analytics in the cloud
Rapid growth and scale
Reducing costs
Driving innovation
Why do you need a modern data warehouse?
Bringing your data together
Creating a data pipeline
Data ingestion
Data storage
Data pipeline orchestration and monitoring
Data sharing
Data preparation
Data transform, predict, and enrich
Data serve
Data visualization
Smarter applications
Summary
Chapter 2: Introducing the Azure Synapse Analytics workspace and Synapse Studio
What is Azure Synapse Analytics?
Why do we need Azure Synapse Analytics?
Customer challenges
Azure Synapse Analytics to the rescue
Deep dive into Azure Synapse Analytics
Introducing the Azure Synapse Analytics workspace
Free Azure account
Quickstart guide
Introducing Synapse Studio
Launching Synapse Studio
Provisioning a dedicated SQL pool
Exploring data in the dedicated SQL pool
Creating an Apache Spark pool
Integrating with pipelines
The Monitor hub
Summary
Chapter 3: Processing and visualizing data
Power BI
Features and benefits
Power BI and Azure Synapse Analytics
Features and benefits
Quick start guide (Data modeling and visualization)
Machine learning on Azure
ML.NET
Automated machine learning
Cognitive services
Bot framework
Azure Machine Learning features and benefits
Software Development Kit (SDK)
Designer
AutoML
Flexible deployment targets
Accelerated Machine Learning Operations (MLOps)
Azure Machine Learning and Azure Synapse Analytics
Quick start guide (Azure Machine Learning)
Prerequisites
Creating a machine learning model using Designer
Summary
Chapter 4: Business use cases
Use case 1: Real-time customer insights with Azure Synapse Analytics
The problem
Capturing and processing new data
Bringing all the data together
Finding insights and patterns in data
Real-time discovery
Design brainstorming
Data ingestion
Data storage
Data science
Dashboards and reports
The solution
Data flow
Azure services
Azure Data Lake Storage Gen2
Azure Synapse Analytics
Azure Synapse Hybrid Integration (Pipelines)
Power BI
Azure supporting services
Insights and actions
Reducing waste by 18%
Social media trends drive sales up by 14%
Conclusion
Use case 2: Using advanced analytics on Azure to create a smart airport
The problem
Business challenges
Technical challenges
Design brainstorming
Data sources
Data storage
Data ingestion
Security and access control
Discovering patterns and insights
The solution
Why Azure for NIA?
Solution architecture
Azure services
Azure Synapse Analytics
Azure Cosmos DB
Azure Machine Learning
Azure Container Registry
Azure Kubernetes Service
Power BI
Supporting services
Insights and actions
Reducing flight delays by 17% using predictive analytics
Reducing congestion and improving retail using smart visualization
Conclusion
Chapter 5: Conclusion
Final words
For further learning
Index