Cloud Analytics with Microsoft Azure - Second Edition
by
Cloud Analytics with Microsoft Azure - Second Edition
- Cloud Analytics with Microsoft Azure, Second Edition
- Preface
- 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
- 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
- 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
- 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
- 5. Conclusion
- Index
-
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