First published on MSDN on Nov 29, 2018
In this sixth blog entry, we'll continue to explore the Azure Data Architecture Guide. The entries for this blog series are:
- Azure Data Architecture Guide – Blog #1: Introduction
- Azure Data Architecture Guide – Blog #2: On-demand big data analytics
- Azure Data Architecture Guide – Blog #3: Advanced analytics and deep learning
- Azure Data Architecture Guide – Blog #4: Hybrid data architecture
- Azure Data Architecture Guide – Blog #5: Clickstream analysis
- Azure Data Architecture Guide – Blog #6: Business intelligence (this one)
- Azure Data Architecture Guide – Blog #7: Intelligent applications
- Azure Data Architecture Guide – Blog #8: Data warehousing
- Azure Data Architecture Guide – Blog #9: Extract, transform, load (ETL)
Like the previous post, we'll work from a technology implementation seen directly in our customer engagements. The example can help lead you to the ADAG content to make the right technology choices for your business.
Business intelligence
Azure offers a rich data and analytics platform, so you can build scalable BI and reporting solutions. Create rich, interactive reports with Power BI by having it connect to Azure Analysis Services, which uses a highly optimized in-memory engine to provide responses to queries against user-friendly semantic models, within a fraction of a second. The underlying data is provided by Azure SQL Data Warehouse, which acts as a central repository of integrated data from one or more disparate sources.
Highlighted services
Related ADAG articles
- Traditional RDBMS workloads
- Technology choices
- Cross-cutting concerns
Please peruse ADAG to find a clear path for you to architect your data solution on Azure:
Azure CAT Guidance
"Hands-on solutions, with our heads in the Cloud!"