Advantages of using DataFlows compared to DataSets

[mkjit256]

Hello

i have been using power bi in the following manner:

1- Connect to data source from power bi desktop

2- transform Data using Power Query

3- Build measures/tables/columns in dax if needed

4- publish to a workspace, to get the report and its underlying dataset

if i need to build another report on the same dataset, i would connect to the published dataset. I am aware that i the connection will be a directquery and i can't access power query when connecting to the dataset. 

Other colleagues have used Dataflows, which according to my understanding is Power Query Online, and then they connect to the dataflow from power bi desktop if they need to build new reports. I am aware that when connecting to the dataflow you are importing it and you can naviaget to power query for further transformation. 

My question is about what other benfits/advantages can i get from dataflow that is not avaiable in a dataset that was published from power bi desktop? and which method is more efficient, if this is applicable. 

Thanks

[audreygerred · Accepted solution]

Hello! When you connect to a published dataset, this is a live connection. One advantage of a dataflow over a dataset is that you can define different refresh schedules for different tables. For example, I once had a very large table where the data only changed once a quarter - if I have a dataset and publish to service, everytime the dataset refreshes, it refreshes all the tables. In Dataflows, I can specify that the table that only gets fresh data once a quarter doesn't get refreshed every day like the transactional fact table would.

Additionally, you can create many tables in a dataflow and expose them to be used by others so they can connect back to it with build access and bring in the tables they need and make joins they need. If you had the same tables in a dataset, a person can connect to the dataset as a whole. For example, I have a date table loaded into a dataflow and then anytime I need it, I bring it into my model from the dataflow, so it gets used when and where it is needed.

[audreygerred · Accepted solution]

Hello! When you connect to a published dataset, this is a live connection. One advantage of a dataflow over a dataset is that you can define different refresh schedules for different tables. For example, I once had a very large table where the data only changed once a quarter - if I have a dataset and publish to service, everytime the dataset refreshes, it refreshes all the tables. In Dataflows, I can specify that the table that only gets fresh data once a quarter doesn't get refreshed every day like the transactional fact table would.

Additionally, you can create many tables in a dataflow and expose them to be used by others so they can connect back to it with build access and bring in the tables they need and make joins they need. If you had the same tables in a dataset, a person can connect to the dataset as a whole. For example, I have a date table loaded into a dataflow and then anytime I need it, I bring it into my model from the dataflow, so it gets used when and where it is needed.

[mkjit256]

Thanks for your reply, i have a follow up question about what have you said, about the connection of a published dataset or dataflow being a live connection.
One difference that i have noticed when connecting to dataset/dataflow is that it is not possible to transform data using power query in the former case.
Shouldn't that make the connection to dataflow to be import connection while that for dataset to be live connection? 

Thanks 

[123abc · Accepted solution]

The distinction between "import" and "live" connections can sometimes be a bit nuanced in Power BI, and it's essential to understand how they work in different contexts.

1. Import Connection: When you connect to a DataFlow or dataset and perform data transformations using Power Query in Power BI Desktop, it is indeed an "import" connection. In this scenario, you are importing the data into the Power BI file, and any changes made to the data (e.g., data cleansing, modeling) are stored within the Power BI file itself. This approach allows you to work with the data offline and apply extensive transformations using Power Query.

2. Live Connection: In the case of connecting to a published dataset, it is typically a "live" connection. This means that Power BI Desktop sends queries to the dataset in the Power BI service, and the data transformations are performed in the service. You cannot access Power Query transformations because they are not available in this context. Changes to the data source are immediately reflected in your report, but you don't have the same flexibility for data shaping as you do with an import connection.

The terminology can be a bit confusing because, in both cases, you are "connecting" to the data, but the key distinction is whether you are importing the data into your Power BI Desktop file (import) or directly querying it from the service (live).

Regarding your question about whether connecting to a DataFlow should be considered an "import" connection, you are correct. When you connect to a DataFlow from Power BI Desktop, it is essentially importing the data into your report, and you can perform further transformations using Power Query. The ability to use Power Query for data transformation makes it more similar to an "import" connection in terms of data preparation flexibility.

So, to clarify:

• Connecting to a DataFlow: Import connection (with Power Query capabilities).
• Connecting to a published dataset: Live connection (limited data transformation capabilities in Power BI Desktop).

Your choice between these connections depends on your data transformation needs and whether you require the ability to perform extensive data shaping in Power Query during the report development phase.

[123abc]

I understand your point, and it's a valid observation. The terminology and behavior can be a bit confusing, so let's clarify this:

1. **DataFlow Connection:** When you connect to a DataFlow from Power BI Desktop, you are indeed importing the data into your report. This means that you can perform further transformations using Power Query in Power BI Desktop. So, in this case, you can transform data using Power Query after connecting to a DataFlow. It's an import connection because the data is brought into your report for further processing.

2. **Dataset Connection:** When you connect to a dataset created in Power BI Desktop, it is typically a live connection. This means that you can't perform further data transformations using Power Query in Power BI Desktop because you are directly querying the dataset without importing the data. The transformations are expected to be done in the Power Query Editor before publishing the dataset.

So, to summarize:

- Connecting to a DataFlow allows you to import data and use Power Query in Power BI Desktop for additional transformations.
- Connecting to a dataset usually establishes a live connection, and data transformations are expected to be completed before publishing the dataset.

Your observation is correct, and it highlights the distinction in behavior between connecting to DataFlows and datasets in Power BI. This distinction is mainly due to the different purposes of DataFlows (data preparation) and datasets (reporting) in the Power BI ecosystem.

[123abc]

Using Dataflows in Power BI offers several advantages compared to using Datasets published directly from Power BI Desktop. Here are some key benefits of using Dataflows:

1. Data Transformation and Preparation: Dataflows provide a more flexible and scalable environment for data transformation and preparation using Power Query Online. You can create and manage data transformations centrally, making it easier to ensure consistency and maintain data quality across multiple reports and datasets.

2. Reusability: Dataflows can be used across multiple reports and datasets. This reusability reduces duplication of effort and helps maintain consistency in data transformations. If you need to make changes to data transformations, you can update the dataflow once, and all connected reports benefit from the changes.

3. Incremental Data Refresh: Dataflows support incremental data refresh, which means you can refresh only the new or changed data, reducing the overall data refresh time and improving performance. This is especially useful when dealing with large datasets.

4. Data Integration: Dataflows allow you to integrate data from various sources into a single dataset. You can consolidate data from different databases, Excel files, cloud services, and more into a unified dataflow, simplifying data management.

5. Data Profiling and Data Quality: Power Query Online in Dataflows offers data profiling capabilities, which help you understand your data better. You can identify data quality issues and inconsistencies, making it easier to clean and prepare the data effectively.

6. Parallel Development: Dataflows support parallel development, allowing multiple users to work on different parts of the data transformation process simultaneously. This collaborative approach enhances productivity.

7. Data Lineage and Documentation: Dataflows provide built-in documentation and data lineage capabilities. You can add descriptions, annotations, and metadata to make it easier for users to understand the data and its transformation history.

8. Dataflow Optimization: Power Query Online includes performance optimization features that can help you tune and optimize data transformation queries. This can result in faster data processing.

9. Hybrid Scenarios: You can combine both Dataflows and Datasets in a single Power BI solution. For example, you can use Dataflows for data preparation and transformation and then create Datasets for specific reports, combining the strengths of both approaches.

10. Dataflow Templates: Power BI allows you to create and share Dataflow templates, which can be used as starting points for common data preparation tasks, further improving efficiency.

In terms of efficiency, the choice between using Dataflows or directly connecting to a published dataset depends on your specific use case. Dataflows are generally more efficient when it comes to data preparation, transformation, and maintenance across multiple reports. However, if you have a simple report that doesn't require extensive data transformation and will only be used once, connecting directly to a published dataset may be sufficient and quicker to set up.

In summary, Dataflows offer a robust and efficient way to handle data preparation and transformation tasks, especially in scenarios where data needs to be reused across multiple reports or where data quality and consistency are crucial. However, the choice between Dataflows and direct dataset connections should be based on your specific project requirements and objectives.