Contents
Chapter 1 Understand Azure data solutions
Lambda and kappa architectures
Azure technologies used for data processing
Hybrid ETL with existing on-premises SSIS and Azure Data Factory
Internet of things architecture
Chapter 2 Implement data-storage solutions
Implement non-relational data stores
Implement a solution that uses Cosmos DB, Azure Data Lake Storage Gen2, or Blob storage
Implement a consistency model in Cosmos DB
Provision a non-relational data store
Provision an Azure Synapse Analytics workspace
Provide access to data to meet security requirements
Implement for high availability, disaster recovery, and global distribution
Implement relational data stores
Provide access to data to meet security requirements
Implement for high availability and disaster recovery
Implement data distribution and partitions for Azure Synapse Analytics
Implement dynamic data masking
Encrypt data at rest and in motion
Chapter 3 Manage and develop data processing for Azure Data Solutions
Develop batch-processing solutions using Azure Data Factory and Azure Databricks
Implement the Integration Runtime for Azure Data Factory
Create pipelines, activities, linked services, and datasets
Implement Azure Databricks clusters, notebooks, jobs, and autoscaling
Ingest data into Azure Databricks
Ingest and process data using Azure Synapse Analytics
Stream-transport and processing engines
Implement event processing using Stream Analytics
Select the appropriate built-in functions
Chapter 4 Monitor and optimize data solutions
Monitor Azure SQL Database using DMV
Implement Azure Data Lake Storage monitoring
Implement Azure Synapse Analytics monitoring
Implement Cosmos DB monitoring
Configure Azure Monitor alerts
Audit with Azure Log Analytics
Monitor Azure Data Factory pipelines
Monitor Azure Stream Analytics
Monitor Azure Synapse Analytics
Configure Azure Monitor alerts
Audit with Azure Log Analytics
Troubleshoot data-partitioning bottlenecks
Optimize Azure Data Lake Storage Gen2
Optimize Azure Stream Analytics