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Course Outline
Module 1: Modern Data Warehousing & Business Intelligence Fundamentals
- The evolving ecosystem of Data Warehousing (DW) and Business Intelligence (BI)
- Cloud-native data warehousing solutions (Azure Synapse Analytics, Azure SQL Data Warehouse)
- Contemporary Data Warehouse architectures (Lambda Architecture, Kappa Architecture)
- Core data modeling concepts (Star Schema, Snowflake Schema)
- Introductory overview of the Data Vault methodology
- Essential BI concepts: ETL/ELT, OLAP, OLAP, DWH, Data Governance
- Survey of the Microsoft BI ecosystem: SQL Server (T-SQL, SSIS, SSAS, SSRS), Azure Synapse Analytics, Azure Analysis Services, Azure Data Factory, Power BI
Module 2: Modern ETL/ELT with SQL Server Integration Services (SSIS)
- Core SSIS components (Integration Services, Connection Managers, Data Flow, Control Flow)
- Modern data access methods (ADO.NET, OLE DB, ODBC, Python Script Task)
- Cloud integration strategies (loading/unloading data with Azure Blob Storage, Azure SQL Database/DW, Azure Data Lake Storage Gen2)
- Data transformation techniques (Derived Column, Lookup transformations, Aggregate transformations, Conditional Split, Script Component)
- Managing Big Data within SSIS (Integration with Azure Databricks, PolyBase)
- Implementing error handling, logging, and debugging in SSIS
- Deployment and scheduling mechanisms (SQL Agent, Azure Automation Runbooks)
Module 3: Building Analytical Models with SQL Server Analysis Services (SSAS - Tabular)
- Introduction to the Tabular Model (contrasted with Multidimensional)
- Fundamentals of the DAX (Data Analysis Expressions) language (Context, Calculations, Aggregations)
- Model design principles: Relationships, Hierarchies, Perspectives, Roles, Security
- Utilizing Time Intelligence functions within DAX
- Managing and deploying Tabular Models (using BIML, SSDT)
- Performance tuning strategies for SSAS Tabular Models
Module 4: Cloud Analytics with Azure Analysis Services (AAS)
- Introduction to Azure Analysis Services (AAS)
- AAS deployment options (PaaS - Azure App Service Plan, Dedicated Compute Instance)
- Connecting to Azure databases (Azure Synapse Analytics, Azure SQL Database, Azure Analysis Services)
- Model authoring in the Azure environment (using Azure Purview or Azure Analysis Services Studio)
- Ensuring scaling and high availability with AAS
- Security implementation in AAS (Role-Based Security)
Module 5: Querying and Analyzing Data with T-SQL and DAX
- Advanced T-SQL for data analysis (CTEs, Window Functions, PIVOT/UNPIVOT, MERGE)
- Deep dive into DAX (Row Context vs Filter Context, Iterators, Time Intelligence, KPIs, Q&A)
- Integrating T-SQL and DAX (PolyBase queries, linked servers)
- Leveraging AI-enhanced analytics (Azure Synapse Analytics Machine Learning Services)
Module 6: Data Discovery and Visualization
- Getting started with Power BI (connecting to data sources, Query Editor)
- Designing effective visualizations (Charts, Graphs, Maps)
- Applying DAX in Power BI (Calculated Columns, Measures)
- Report design and formatting techniques in Power BI
- Introduction to Azure Synapse Studio for BI applications
Module 7: Course Review, Advanced Concepts & Hands-on Labs
- Advanced data transformation patterns (Slowly Changing Dimensions, Type 1/2)
- Integration of Data Quality Services (DQS) (overview)
- Performance optimization and troubleshooting (Query Store, Execution Plans)
- Extending BI capabilities (Power Query, Power Automate)
- Practical hands-on labs covering end-to-end BI scenarios (ETL, Model Building, Reporting)
Requirements
Familiarity with Windows operating systems and a foundational understanding of SQL and relational database principles.
14 Hours
Testimonials (2)
Abhi has excellent knowledge of Alteryx and he explained things very clearly. He understood our goals and created bespoke demo datasets that were relevant to our organisation, which was very impressive. The training was well-structured and delivered at a good pace, with time for questions.
Samuel Taylor - Manchester Metropolitan University
Course - Alteryx for Data Analysis
Deepthi was super attuned to my needs, she could tell when to add layers of complexity and when to hold back and take a more structured approach. Deepthi truly worked at my pace and ensured I was able to use the new functions /tools myself by first showing then letting me recreate the items myself which really helped embed the training. I could not be happier with the results of this training and with the level of expertise of Deepthi!
