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- Connect to data sources including Excel, relational databases, and NoSQL stores in Power BI. Learn techniques to optimize performance and resolve common import errors.
By the end of this module, you'll be able to:
- Identify and connect to a data source
- Get data from a relational database, like Microsoft SQL Server
- Get data from a file, like Microsoft Excel
- Get data from applications
- Get data from Azure Analysis Services
- Select a storage mode
- Fix performance issues
- Resolve data import errors
- Use Power Query to clean and prepare data for analysis. Learn to simplify models, change data types, rename objects, pivot data, and profile columns to identify valuable data for deeper analytics.
By the end of this module, you’ll be able to:
- Resolve inconsistencies, unexpected or null values, and data quality issues.
- Apply user-friendly value replacements.
- Profile data so you can learn more about a specific column before using it.
- Evaluate and transform column data types.
- Apply data shape transformations to table structures.
- Combine queries.
- Apply user-friendly naming conventions to columns and queries.
- Edit M code in the Advanced Editor.
- Learn about Power BI model frameworks including import, DirectQuery, and composite models. Understand the benefits and limitations of each to optimize your data models.
By the end of this module, you’ll be able to:
- Describe Power BI model fundamentals.
- Determine when to develop an import model.
- Determine when to develop a DirectQuery model.
- Determine when to develop a composite model.
- Choose an appropriate Power BI model framework.
