Share

What is a Power BI Datamart?

ChatGPT & Chris Wagner • January 3, 2023

And why you should include them in your solution

A blog by ChatGPT with a little help and guidance.

In a data architecture, PowerBI datamarts are usually positioned downstream from other components, such as data warehouses and data lakes. This means data is typically loaded into a datamart after it has been processed and transformed in some way.

For example, data from transactional databases, log files, and web analytics tools may be extracted, transformed, and loaded into a data lake. The data may be further transformed and organized into a data warehouse. Finally, the data may be loaded into a PowerBI datamart for fast querying and analysis.

The online Power BI Architecture has many similarities to the classic Microsoft Business Intelligence (MSBI) toolkit.


SQL Server Integration Services (SSIS) >>> has become >>> Power BI Dataflows

SQL Server Database >>> has become >>> Power BI Datamarts

SQL Server Analysis Services (SSAS) >>> has become >>> Power BI Datasets

SQL Server Report Server (SSRS)  >>> has become >>> Power BI Paginated Reports

New Paragraph

In Power BI, a dataflow is a reusable data pipeline that you can use to extract, transform, and load data into the Power BI service. Dataflows allow you to centralize your data preparation logic, so you can easily share your data with others and reuse it in multiple reports and dashboards.


A dataflow consists of one or more entities defined by a query specifying how to extract and transform data from a source. You can create dataflows by connecting to various sources and using the Power Query language to shape and transform the data. You can then use the data in the dataflow to create reports and dashboards in the Power BI service.

A PowerBI datamart is a specialized database that queries and analyzes data quickly. It is typically used to store large amounts of data from various sources, such as transactional databases, log files, and web analytics tools downstream from the data warehouse.


The primary benefit of using a PowerBI datamart is the ability to quickly and easily access and analyze large amounts of data. With a datamart, you can easily create interactive dashboards and reports that visualize your data in meaningful ways. This can help you identify trends, patterns, and insights that can inform business decisions and strategies.


Another advantage of PowerBI datamarts is their flexibility. You can easily connect to various data sources, such as SQL databases, Excel spreadsheets, and CSV files. This makes it easy to bring all of your data together in one place and analyze it in a unified way.

In Power BI, a dataset is a collection of data imported from one or more sources. It can include tables, columns, and measures (calculations) created from the data. You can use datasets to create visualizations, reports, and dashboards in Power BI.


You can create a dataset by connecting to a data source and importing data or by using the data modeling features in Power BI to shape and transform the data. Once the dataset is created, you can use it as the basis for creating visualizations and reports in Power BI.

Overall, a PowerBI datamart is a powerful tool for storing, querying and analyzing large amounts of data. Whether you're a small business owner looking to gain insights from your sales data, or a large enterprise looking to optimize your operations, a PowerBI datamart can help you make better, data-driven decisions.

CHRIS WAGNER, MBA MVP

Analytics Architect, Mentor, Leader, and Visionary

Chris has been working in the Data and Analytics space for nearly 20 years. Chris has dedicated his professional career to making data and information accessible to the masses. A significant component in making data available is continually learning new things and teaching others from these experiences. To help people keep up with this ever-changing landscape, Chris frequently posts on LinkedIn and to this blog.

Become a Data Engineer

By Christopher Wagner • September 3, 2024
Your guide to becoming a Data Engineer.

Microsoft Fabric vs Databricks

By Christopher Wagner • August 19, 2024
Compare Microsoft Fabric and Databricks, two leading data platforms. Highlights their features, strengths, and unique offerings across various domains like data engineering, data analytics, data science, DevOps, security, integration with other tools, cost management, and governance. Microsoft Fabric is noted for its low-code/no-code solutions and seamless integration with Microsoft tools, making it accessible for users with varying technical skills. Databricks is praised for its high-performance capabilities in big data processing and collaborative analytics, offering flexibility and control for experienced data teams.

Data Wizardry and Epic Adventures: A Day in the Life of a Microsoft Fabric Engineer

By Christopher Wagner • November 15, 2023
In a dynamic data engineering scenario, Sam, a skilled professional, adeptly navigates urgent requests using Microsoft Fabric. Collaborating with Data Steward Lisa and leveraging OneLake, Sam streamlines data processes, creating a powerful collaboration between engineering and stewardship. With precision in Azure Data Factory and collaboration with a Data Scientist, Sam crafts a robust schema, leading to a visually appealing Power BI report.

Power BI usage scenarios: Lakehouse to Power BI

By Christopher Wagner • April 28, 2023
NOTE: This is the first draft of this document that was assembled yesterday as a solo effort. If you would like to contribute or have any suggestions, check out my first public GIT repository - KratosDataGod/LakehouseToPowerBI: Architectural design for incorporating a Data Lakehouse architecture with an Enterprise Power BI Deployment (github.com) This article is NOT published, reviewed, or approved by ANYONE at Microsoft. This content is my own and is what I recommend for architecture and build patterns.
Show More
Share by: