Power BI Dataflows vs Datamarts: When to Use Each One

To help you decide on the best option between Power BI Dataflows and Power BI Datamarts, we delve into some use cases in this blog post authored by Marthe Moengen (Data Ascend). The article poses a crucial question: when should one opt for which tool? The potential answers are broken down based on specific organizational and data management scenarios.

Power BI Dataflows shine when dealing with tables that are frequently reused across an organization, such as dimension tables or master data tables. While Datamarts can also be used, there isn't a significant need to construct a Datamart for this specific use case.

For organizations eager to utilize Azure Machine Learning and Azure Cognitive Services in their BI process, Dataflows again lead the way. The Power BI feature offers an easy, user-friendly drag-and-drop solution for integrating with these services. Interestingly, Datamarts at this time do not readily offer this capability.

When tackling large datasets that are consuming too much memory, Dataflows also have a leg up as they allow for incremental refreshes based on specified date ranges. Datamarts, unfortunately, lack this feature unless built on top of a Dataflow already configured for incremental refresh.

However, Datamarts have an edge when it comes to ad-hoc SQL querying and data exploration. While you can explore your data via a Dataflow, running SQL queries remains off-limits. As for Self-Service Data Modeling, Datamarts score big as they assist you in shaping your star schema right in the Power BI Service, so you don't need to hold up for data warehouse alterations.

Lastly, Dataflows falter when there's a need to connect to your data in Power BI Service via a SQL endpoint, a scenario where Datamarts come into their own. The provision of a SQL endpoint by Power BI Datamarts is a boon for developers and data analysts who need database tool connection capabilities.

Extended Discussion

We must remember that choosing between Dataflows and Datamarts depends on many factors. These include, but are not limited to, the size and nature of data, the need for Azure integrations, and the necessity for SQL query and self-service data modeling capabilities. In an era where data usability drives strategic decision-making, the decision to use either Dataflows or Datamarts must be carefully weighed and balanced. Ultimately, the best choice for your enterprise boils down to what best serves its data handling needs and overall strategic goals.

Read the full article When should you use Power BI Dataflows vs Power BI Datamarts?

Learn about When should you use Power BI Dataflows vs Power BI Datamarts?

As a dedicated Microsoft expert specializing in Power BI, the first thing to understand is when to utilize Power BI Dataflows compared to Power BI Datamarts. Both are features of the influential Power BI Service which offer different options for data processing and visualizations. In this blog post, we will be dissecting this comparison, so that you have a clear guideline of when to use each component.

You might be dealing with tables that are frequently used throughout your organization. For instance, dimension tables, master data tables, or reference tables. Power BI Dataflows are excellently designed for such occurrences. On the other hand, you could also utilize Power BI Datamarts; however, it's not a necessity to develop a datamart to mitigate this use case.

There are other scenarios too. Suppose you intend to leverage Azure Machine Learning and Azure Cognitive Services in your Power BI Service. This is made conveniently attainable through Dataflows. Besides, Dataflows integrate seamlessly with these services to present a user-friendly drag-and-drop solution for non-technical users. A premium license is necessary for this, although there's no need for an Azure subscription. You are less likely to find this functionality in Power BI Datamarts.

An aspect of Dataflows I find interesting is the option to incrementally refresh your data based on certain parameters, specifying a date range. It's helpful if you're working on huge datasets that are consuming all your memory. But again, you'll require a premium license to use this feature.

Meanwhile, Datamarts excel if you want to embark on ad-hoc querying or extensive exploration of your data. You can sort, filter, and perform basic aggregation visually or through expressions defined in SQL.

On one hand, Power BI Dataflows doesn't support the setting up of relationships between tables, writing DAX, or building measures. On the other hand, a notable advantage with Power BI Datamarts is that you can model your star schema. This eliminates the wait time for your data warehouse to make smaller or larger improvements or changes to your data model.

In scenarios where you need to connect your data through a SQL endpoint in your Power BI Service, a brilliant feature of Power BI Datamarts comes handy - it provides a SQL end-point to your data. This feature is incredibly useful for developers or data analysts. Consequently, you can connect to your datamart as any other DB, and run queries using popular database tools like Microsoft SQL Server Management Studio (SSMS).

With this comparison, we hope to have shed some light on the unique features of Power BI Dataflows and Power BI Datamarts to help you make informed decisions based on your use case. Please feel free to let us know what you think and if there are any other use cases you feel should be compared. Also, we recommend taking a few training courses on Power BI to further enhance your understanding of its features and functionalities.

• What, How, When and Why on Power BI Dataflows
• What, How, When and Why on Power BI Datamarts

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