Data Warehousing with Azure: Analytics and Reporting

Data Warehousing with Azure: Analytics and Reporting

• define the essential features and the critical capabilities provided by Azure Analysis Services
• create an Analysis Services server using Azure and PowerShell
• create an Analysis Services server using the Azure portal
• demonstrate how to add models to the Analysis Services Server
• create reports from the model data using Power BI
• demonstrate the approaches of connecting and exploring data using Microsoft Excel and Power BI
• create dynamic reports and mashups to facilitate valuable insights from the data visualizations
• illustrate how to facilitate data warehousing solutions using the AdventureWorks sample
• work with Power BI and Azure Data Lake
• prepare data and create reports using Power BI

Discover how to use Azure Analysis Services and Power BI to prepare reports that can be used to analyze data in SQL Data Warehouse and Azure Data Lake.

Data Warehousing with Azure: Architecture & Modeling Techniques

Data Warehousing with Azure: Architecture & Modeling Techniques

• identify the essential characteristics of data warehouse and compare data warehouse with operational databases
• list the various possible data warehousing architectures that can be implemented
• recall the various essential data warehousing alternatives that are available today apart from Azure
• depict the evolution of data warehouse and illustrate the first generation and second generation data warehouse
• illustrate the various types of data warehouse that are being implemented by various businesses
• list the critical features of the federated data warehouse, compare federated data warehouse with the centralized approach, and list advantages and disadvantages of this approach and implementation scenarios
• identify the critical advantages and disadvantages associated with the star schema and data mart approaches of implementing data warehouse
• define the essential lifecycle phases of data warehousing implementation and data movement
• illustrate the essential concepts of metadata, types of metadata, and its roles in data warehouse implementation
• compare the essential approaches of processing, integrating, and managing structured and unstructured data
• compare the essential approaches of processing, integrating, and managing structured and unstructured data with real time examples
• specify the critical analytical and reporting mechanisms that are implemented in data warehouse
• define the benefits of implementing cloud data warehouse and compare cloud data warehouse with on-prem implementation
• describe the benefits and how to implement Hybrid architectures using on-premise capabilities
• define the critical features, characteristics, and advantages of implementing Federated and Hybrid data warehouse

Explore the fundamentals of data warehousing and the essential architectures and components being implemented to manage data.

Data Warehousing with Azure: Data Lake Implementation Using Azure

Data Warehousing with Azure: Data Lake Implementation Using Azure

• list the essential elements of a data lake and specify the features and goals of data lakes
• describe the basics of an Azure Data Lake
• identify the architectural components of Azure Gen1 Data Lake Storage
• identify the architectural components of Azure Gen2 Data Lake Storage and compare the differences between the features of Gen1 and Gen2 data lake
• set up Gen1 Azure Data Lake Storage using the Azure portal
• create Gen1 Azure Data Lake Storage using PowerShell
• upload data on Gen1 Azure Data Lake Storage using VSCode and CLI
• use Azure storage explorer to manage Azure Gen1 Data Lake Storage
• create Gen2 Azure Data Lake Storage
• load data in the Azure Data Lake using the Azure Data Factory
• load data from the Azure Data Lake Storage to SQL Data Warehouse
• compare the essential features and capabilities provided by V1 and V2 Azure Data Factory
• create Gen 1 and Gen 2 Azure Data Lake Storage using the Azure portal and PowerShell

Explore the fundamentals of data lakes and approaches for building and using data lakes. How to build and use an Azure Data Lake using Gen1 and Gen2 implementation approaches is also covered.

Data Warehousing with Azure: Implementing Azure SQL Data Warehouse

Data Warehousing with Azure: Implementing Azure SQL Data Warehouse

• list the essential features and architectural components of Azure SQL Data Warehouse
• specify the distributed management system and the workload management system of Azure SQL data warehouse
• demonstrate how to create Gen2 SQL Data Warehouse on Azure using the portal and PowerShell
• connect and submit queries to SQL Data Warehouse using SQL Operations Studio and SSMS
• demonstrate the steps of creating a SQL data warehouse and invoking queries using PowerShell
• specify the various steps involved in designing a productive data warehouse solution
• recall the best practices that should be adopted when working with SQL Data Warehouse
• demonstrate how to pause and resume SQL Data Warehouse using the portal and PowerShell
• scale the compute capability of SQL data warehouse in Azure
• define the steps for implementing ELT for SQL Data Warehouse
• connect to PowerShell and create SQL Data Warehouse using PowerShell

Explore the practical implementation of Azure SQL Data Warehouse. Examine how to design, model, and apply ELT approaches of extracting loads and transforming data.

Data Warehousing with Azure: Managing Azure Data Lake

Data Warehousing with Azure: Managing Azure Data Lake

• define the benefits of using Azure Data Lake Storage to facilitate big data solutions
• ingest large amounts of data into a data store
• describe the different types of data that can be managed on Azure Data Lake
• recall the different approaches of managing huge amounts of data using the Azure ExpressRoute
• specify the different approaches of moving data from one source to another
• move data from a specified source to another destination using AdlCopy
• copy offline data to the Azure Data Lake Storage
• describe the various security mechanisms that can be implemented to secure Azure Data Lake
• secure data that is stored in the Azure Data Lake Storage
• specify the various tuning tasks that can be applied on a data lake to enhance performance
• recall the role of log analytics in creating service alerts and controlling the cost of Azure Data Lake
• implement log analytics to facilitate alerts on identified thresholds
• prepare and move offline data to the Azure Data Lake

Explore the advanced features of Azure Data Lakes with additional focus on managing various scenarios of data ingestion. Securing and tuning an Azure Data Lake for performance enhancement is also covered.

Data Warehousing with Azure: Working with SQL Data Warehouse Objects

Data Warehousing with Azure: Working with SQL Data Warehouse Objects

• identify the important factors that can impact the design decisions when developing a distributed data warehouse
• recall the key terms and concepts of data warehouse modelling
• illustrate the concept of tables and creating tables to implement in SQL Data Warehouses
• work with the essential commands that are generally used to create tables in SQL Data Warehouse
• recall the various unsupported features that differentiate SQL Data Warehouses from the traditional databases
• define the concepts of distributed tables in SQL Data Warehouse
• list the various approaches of loading data in SQL Data Warehouse
• load data to SQL Data Warehouse
• load data to SQL Data Warehouse using the Azure data factory
• load data to SQL Data Warehouse using SQL Server Integration Services
• import and export data to and from SQL Data Warehouse using Microsoft Excel
• work with loops in SQL Data Warehouse
• specify the various clauses that are generally used in stored procedures while working with SQL Data Warehouse
• illustrate the essential transaction management features with isolation level and transaction state in SQL Data Warehouse
• create architectural abstraction using views
• identify the various performance issues and workload management in SQL Data Warehouse
• create tables and load data using Azure data factory and SQL Server Integration Services

Explore how to create and utilize SQL Data Warehouse objects and work with T-SQL to implement tables of diversified categories.