Power Query

Power Query is a powerful data transformation tool that is widely used across various Microsoft products such as Power BI, Azure Data Factory, Microsoft Fabric, and Excel. Its primary role is to help users connect, transform, and load data from various sources for analysis and reporting purposes. In Power BI, Power Query is an essential component that allows users to easily import data from different sources, clean and transform it, and create data models for visualization. This enables users to build interactive and insightful reports and dashboards. Azure Data Factory also leverages Power Query's capabilities for data transformation and preparation tasks within data pipelines. It enables users to extract, transform, and load data seamlessly across different data sources and destinations in the Azure cloud environment. Microsoft Fabric, a data integration service, also benefits from Power Query's data shaping functionalities to unify and transform data from disparate sources into a structured format for analysis and reporting. In Excel, Power Query provides users with a user-friendly interface to import, transform, and combine data from multiple sources without the need for complex formulas or coding. This makes it a valuable tool for data analysts and business users to perform data shaping tasks efficiently. Moreover, Power Query can be used as an ETL (Extract, Transform, Load) tool for data integration processes. It simplifies the data preparation phase by enabling users to clean, reshape, and combine data from different sources before loading it into a data warehouse or reporting tool. Additionally, Power Query facilitates quick data visualization by allowing users to transform and shape data on the fly, enabling them to create visualizations and gain insights rapidly. Its intuitive interface and robust capabilities make it a versatile tool for data analysts and business users alike.

US Census Data with Power BI

  • Learn advanced Power Query techniques to import US Census Data with Power BI
  • Master auto-naming cryptic census columns and obfuscating private keys
  • Understand how to pivot wide column data into normalized schemas
0m
0
19
Scroll to Top