Why do we need Power Query when Power Pivot can import from mostly used sources ? Power Bi interview questions and answers010

Why do we need Power Query when Power Pivot can import from mostly used sources ? 

While Power Pivot can import data from many commonly used sources, there are several key reasons why Power Query remains an essential tool in the Power BI workflow, even when using Power Pivot for data modeling:

1. Data Shaping and Transformation:

• Power Query excels at data shaping and transformation. It provides a powerful and intuitive interface for cleaning, filtering, merging, and reshaping your data before it enters your Power Pivot model. This includes tasks like removing duplicates, splitting columns, and applying complex logic to transform data into the desired format for analysis.

• Power Pivot primarily focuses on data modeling and calculations. While it can import data, its strength lies in building relationships between tables and creating calculated measures and columns within the model.

2. Improved Data Quality and Consistency:

• Power Query allows you to ensure data quality and consistency before it reaches your model. You can easily identify and handle missing values, outliers, and inconsistencies in your data, preventing these issues from impacting your analysis and calculations in Power Pivot.

• Power Query provides powerful tools for data cleansing and validation. You can use functions like fill forward, replace values, and conditional formatting to clean and standardize your data, ensuring accurate and reliable analysis in Power Pivot.

3. Enhanced Data Exploration and Discovery:

• Power Query facilitates data exploration and discovery before modeling. You can preview and manipulate your data, identify patterns and trends, and gain valuable insights before building your Power Pivot model. This helps you focus your analysis and create a model that addresses specific questions and hypotheses.

• Power Query offers advanced data profiling and visualization tools. You can analyze data distribution, identify outlier values, and visualize relationships between variables within Power Query, providing valuable insights to inform your Power Pivot modeling decisions.

4. Reusability and Automation:

• Power Query queries can be easily reused and shared across different reports and models. This saves time and effort and ensures consistency in data transformation and cleaning across your Power BI projects.

• Power Query allows you to automate data refresh and transformation processes. You can schedule queries to run automatically, ensuring your reports and models are always based on the latest and most accurate data.

5. Collaboration and Data Governance:

• Power Query's intuitive interface makes data transformation accessible to a wider audience. This facilitates collaboration between data analysts and business users, enabling business users to explore and understand the data more effectively.

• Power Query supports data governance by providing auditing and lineage tracking tools. You can track the source, transformations, and history of your data, ensuring transparency and data quality control within your organization.

In summary, Power Query complements Power Pivot by offering powerful data shaping, transformation, and exploration capabilities. While Power Pivot excels in data modeling and calculations, Power Query sets the stage for accurate and insightful analysis by ensuring data quality, consistency, and exploration before building your Power Pivot model.

Therefore, both tools work together seamlessly to empower you to analyze your data effectively and gain valuable insights from your information.

I hope this explanation clarifies the complementary roles of Power Query and Power Pivot in the Power BI workflow. If you have any further questions or need specific examples of how they work together, feel free to ask!