Author
Released
10/23/2019- Transactional databases vs. data warehouses
- Star and snowflake schemas
- Creating a data warehouse
- Designing tables and views
- Rebuilding columnstore indexes
- Creating an Azure SQL Data Warehouse
- Establishing control flow beyond ETL
- Enforcing data quality
- Configuring Master Data Services
- Consuming data from the warehouse in BI services
Skill Level Intermediate
Duration
Views
- [Adam] It's been said that information is power and in the world of business, having more information and being able to access it quickly can give you a competitive advantage. There an has been an explosion in recent years of the importance of business intelligence and one of the key components of a BI system is a single, complete, and trustworthy repository of raw data, the data warehouse. Hello, I'm Adam Wilbert and I've spent the last decade helping organizations get the most value from their data. I'm excited to introduce you to the concepts of data warehousing. In this course I'll show you how to consolidate a number of data sources into a single data warehouse so that they're more accessible and consistent. And you'll see how the relational model of data storage used by transaction databases gets transformed into the dimensional model of facts and measures. With these transformations applied to your data, you'll spend less time analyzing information and have more time to make and implement decisions based off of your insights. So join me in my LinkedIn Learning course to see how to develop data warehouses in SQL Server 2019 in order to provide a robust trustworthy platform to serve all of your BI reporting and analysis workloads.
Related Courses
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Learning Microsoft SQL Server 2019
with Adam Wilbert1h 19m Beginner -
Relational Databases Essential Training
with Adam Wilbert2h 12m Intermediate
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Introduction
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Set up the example databases2m 32s
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1. Data Warehouse Foundations
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Data warehouse core concepts3m 58s
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Dimensions and facts5m 1s
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Star and snowflake schemas3m 50s
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Hardware and infrastructure2m 35s
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2. Create a Data Warehouse
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Design dimension tables5m 46s
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Design fact tables3m 33s
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Create an indexed view3m 28s
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3. Columnstore Indexes
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4. Implement an Azure SQL Data Warehouse
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5. Extract, Transform, and Load (ETL)
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Understand data flow3m 12s
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Establish control flow1m 54s
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6. Enforce Data Quality
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Cleanse data with DQS6m 48s
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7. Master Data Services
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Install MDS and IIS4m 37s
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Deploy a sample MDS model6m 35s
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Install the MDS Excel add-in2m 28s
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Update master data in Excel3m 40s
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8. Consume Data from the Warehouse
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Conclusion
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Next steps1m 3s
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Video: Store information in a data warehouse