Difference Between Power Query vs Power Pivot?
Power Query is a data transformation and preparation tool that allows you to connect to various data sources, retrieve data, and perform data cleansing, shaping, and transformation operations while Power Pivot is a data modeling and analysis tool that enables you to create data models within Excel. Power Pivot deals with data analysis and modeling while Power Query is about extraction and loading. Also, in Power Pivot, you can work with data after loading it into Excel’s memory while in Power Query, you work before loading it.
Both Power Query and Power Pivot go hand-in-hand and work very well together. In this article, we will go through some of the key differences between these two tools.
Table of Contents
Key Differences to Note
Both Power Query and Power Pivot offer excellent functionalities to support data analysis, transformation, modeling, and visualization. However, there are a number of crucial differences between these two tools as per the different criteria outlined below:
- Tool Purpose
- Power Query is primarily a data transformation tool that allows you to perform data connection, retrieval, data cleaning, and data Query operations.
- Power Pivot is a powerful data modeling and analysis tool to gain data insights.
- Data Connections
- With Power Query, you can import data from a number of different sources, which includes flat files, databases, cloud (Azure), Online services, etc.
- Power Pivot doesn’t provide any data connectivity functionality as such. However, you can connect to the dataset processed in the Power Query.
- Data Transformation
- Power Query allows you to perform data cleaning, data manipulation, and data operations so that it can be further used for modeling or analysis purposes.
- Power Pivot doesn’t have any transformational capabilities.
- Data Modeling
- Power Query doesn’t offer any data modeling capabilities. However, you can prepare the dataset before processing it for data modeling.
- Power Pivot has data modeling capabilities, and you can also establish the relationship between different tables for your analysis.
- Query Language
- Power Query has a language called M, which is primarily used for any operations you perform in Power Query. For every action you perform, Power Query records the steps, and these steps are visible in the APPLIED STEPS section.
- Power Pivot uses DAX for creating complex calculations and measures for data analysis and visualization.
- Data Visualization
- Power Query doesn’t have any data visualization features.
- Power Pivot has in-built tools such as Pivot table and Pivot chart that can present the aggregated data in a visual format for data trends and patterns.
- Autodata Refresh Schedule
- Power Query provides the option of configuring the data refresh, which means you don’t have to refresh the data from underlying sources manually.
- Power Pivot doesn’t have any auto data refresh option. However, you can manually refresh the Pivot table or Pivot chart to display the latest figures.
What is Power Query?
Power Query is a data transformation and manipulation tool that is part of Microsoft Excel and other Microsoft products like Power BI and Power Automate (formerly known as Microsoft Flow). It is designed to help users import, transform, and shape data from various sources into a format that is suitable for analysis, reporting, or further processing. Power Query provides a user-friendly interface for performing these tasks, without requiring advanced programming or scripting knowledge.
Power Query is highly recommended for data import. It is due to its ability to import and connect with different data sources including the cloud. With Power Query, you can easily transform the dataset to customize your requirements. You can also consolidate multiple datasets into a single view, save your changes, and load it into your data model for further steps.
Some of the key features of Power Query include:
| Key Functionality | Description |
|---|---|
| Data Connection | Allows you to connect with a range of data sources and supports both on-premises and cloud-based data sources |
| Data Transformation | Supports a variety of operations such as merging, appending, sorting, filtering, grouping, etc. |
| Data Cleansing and error handling | Enables you to perform data cleaning, duplicate removals, handling missing or erroneous values and data type changes |
| Advanced queries | Supports a language called M to create calculated columns and customized fields |
| Query parameters | Allows you to change the data sources dynamically |
| Integration Support | Enables integration to Microsoft suites of tools such as Power BI, Excel, etc. |
| Data Refresh | Allows you to schedule data refresh frequency |
What is Power Pivot?
Power Pivot is a data modeling and analysis feature in Microsoft Excel and Power BI, a business intelligence tool from Microsoft. It allows users to create sophisticated data models, perform advanced data analysis, and generate meaningful insights from large and complex datasets. Power Pivot is particularly useful for business analysts, data professionals, and decision-makers.
It is a very useful tool, particularly in cases where users need to combine large volumes of datasets. It can also be used to perform quick data analysis, and share the outcomes with other users. The Power Pivot allows you to create complex data models that can be easily integrated with the Power BI tool.
Power Pivot has a lot of advanced features. They enable you to create calculated columns or measures using the powerful DAX (Data Analytics Expression) formula, and KPIs, and display the aggregated dataset. One of the key features includes hierarchically organizing the data fields and displaying them for data analytics.
Power Pivot comes with a plethora of functionalities that include:
| Key Functionality | Description |
|---|---|
| Data Modelling | Enable users to apply the model and define relationships between different tables |
| DAX feature | Create sophisticated calculations using the DAX formula |
| Data Aggregation | Use data aggregation to perform data analysis and summarization |
| Data visualization | Create data visualization using Power table and Power charts based on data models |
| Data Hierarchy | Display data in a hierarchical and organized manner |
| Drill down capabilities | Support the ability to perform data drill down to a granular level |
| Ability to perform Data Refresh | Allows data refresh as per the latest dataset |
| Integration capabilities | Provides seamless integration to Power BI, Excel, and other tools |
In the next section, we will focus on the key differences between Power Query and Power Pivot. We will do it across different parameters with a side-by-side comparison.
Comparative Table (Key Differences)
There are several similarities and differences between Power Query and Power Pivot. Here we have created a comparative table to highlight the key differences between these two products across different categories.
| Category | Power Query | Power Pivot |
|---|---|---|
| Purpose | Power Query can help you prepare large and complex datasets for analysis | Power Pivot can help you create complex calculations and models for your dataset |
| Script language | Power Query uses a scripting language called M | Power Pivot uses DAX (Data Analysis Expression) |
| Data Connectivity | You can connect with multiple data sources across different formats. You can define Query parameters for flexible data connections. You can also create manual datasets as well | Power Pivot can connect the dataset processed by Power Query for further processing |
| Data Query capabilities | Allows you to fetch or Query data from different sources. | There is no data Query support similar to the Power Query |
| Column Split | Supports column split in Power Query editor | No such support |
| Slicer/Timelines | No support for slicer or timelines | You can insert a slicer or timeline into a Power Pivot |
| Data Merge/Append | Supports data merging, append queries, keeping or removing columns, and even combining different files | No |
| Change Data Type | Allows change in data types of data fields | No |
| Handle exceptions or missing values | You can replace missing values or replace errors | No |
| Reshape data | You can apply different options such as Pivot or unPivot columns, transposing and grouping datasets | No |
| Data Modelling | No | Support creating data models between different tables |
| Establish Relationships | No support for relationship establishment | You can also define the relationship between the tables |
| Data Aggregations | No data aggregation features are available | You can perform data aggregations for data analysis and summarization |
| KPI metrics | You can’t create any KPI metrics | You can create Key Performance Indicators using the Pivot feature |
| Scripting support | Supports R and Python scripts | No support of R or Python |
| Data Visualization | Data visualization is not supported | Supports data visualization through the use of Power Pivot table, Pivot chart |
| Data Drill Down capabilities | No drill-down options are available | You can apply data drill-down functionality to the granular levels |
| Hierarchial Data Representation | No such features are available | With Power Pivot, you can organize the data set and present them in a hierarchical manner |
| Data Consolidation | You can combine multiple datasets into a single view | No |
| Advanced AI Insights | Supports Advanced AI capabilities such as Text Analytics, invoke of Vision (Cognitive Services) and Azure Machine Learning features | No such features are available |
Similarities
Although both Power BI and Excel differ in a number of ways, they have similar capabilities. These include providing business intelligence, data analytics, and data visualization. Let’s examine some of the commonalities between these two as shown in the table below.
| Features | Power Query | Power Pivot |
|---|---|---|
| Calculated columns or measures | You can add columns functionality to create customized columns or clone existing columns. | You can create calculated columns or measures using the DAX functionality. |
| Integration to Power BI | Yes | Yes |
| Tool for Query Editing | Power Query has an editor called Power Query editor | Power Pivot has a similar editor that allows you to edit the data selection and other changes |
| User-friendly interfaces | The Power Query editor interface is quite user-friendly and easy to navigate. | Power Pivot also provides an easy-to-use interface to the users. |
| Data Filter, Sorting | Supports data filter, sorting, and split columns in your dataset | You can filter the dataset and apply sorting of values |
| Support for functions | Supports multiple functions such as scientific, trigonometric, standard, statistical, etc. for data manipulation. | Supports a broad range of functions to perform data aggregation and calculation for data analysis |
| Data Refresh | Yes | Yes |
| Flexibility to change data source | Yes | Yes |
You’ll frequently combine the use of Power Query and Power Pivot. Your data is first prepared and shaped using Power Query. Then it is loaded into Power Pivot for more in-depth analysis. When working with data in Microsoft Power BI, this set of tools offers a potent answer. It is especially for business intelligence and data analysis activities.
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