Creating dataflows in Power BI - Mechanics & Internals

Overview - Creating dataflows

What is it?

Creating dataflows in Power BI means building reusable data preparation processes in the cloud. These dataflows extract, clean, and transform data from various sources before loading it into Power BI reports or dashboards. They help organize and centralize data preparation so multiple reports can use the same cleaned data. This makes data management easier and more consistent across an organization.

Why it matters

Without dataflows, every report creator would prepare data separately, causing repeated work and inconsistent results. Dataflows solve this by letting teams prepare data once and share it widely. This saves time, reduces errors, and ensures everyone works with the same trusted data. It also supports better collaboration and faster decision-making in businesses.

Where it fits

Before learning dataflows, you should understand basic Power BI concepts like datasets, reports, and Power Query for data transformation. After mastering dataflows, you can explore advanced topics like dataflow linked entities, incremental refresh, and integrating dataflows with Azure Data Lake for enterprise-scale solutions.

Mental Model

Core Idea

A dataflow is a cloud-based pipeline that prepares and stores data once so many reports can use it without repeating the work.

Think of it like...

Creating a dataflow is like cooking a big batch of soup and storing it in the fridge so everyone in the family can serve themselves later, instead of each person cooking their own soup every time.

┌───────────────┐      ┌───────────────┐      ┌───────────────┐
│ Data Sources  │─────▶│ Dataflow      │─────▶│ Power BI      │
│ (Files, DBs)  │      │ (Transform &  │      │ Reports &     │
│               │      │  Clean Data)  │      │ Dashboards    │
└───────────────┘      └───────────────┘      └───────────────┘

Build-Up - 7 Steps

1

Foundation

Understanding dataflows basics

Concept: Dataflows are cloud-based data preparation tools in Power BI that let you clean and transform data once for reuse.

Dataflows use Power Query online to connect to data sources like Excel files, databases, or web data. You apply steps like filtering, merging, or adding columns. The cleaned data is saved in the cloud as entities that reports can use.

Result

You get a reusable, cleaned dataset stored in Power BI service, ready for multiple reports.

Knowing that dataflows centralize data prep helps avoid duplicated work and inconsistent data across reports.

2

Foundation

Setting up your first dataflow

Concept: Creating a dataflow involves connecting to data, applying transformations, and saving the result as entities.

In Power BI service, you create a new dataflow, choose your data source, and use the Power Query editor to clean and shape data. After saving, the dataflow runs to load data into the cloud storage.

Result

A dataflow entity appears in your workspace, ready to be linked to reports.

Hands-on creation shows how dataflows fit into the Power BI ecosystem and the ease of cloud-based data prep.

3

Intermediate

Linking dataflows to Power BI reports

🤔Before reading on: Do you think reports connect directly to data sources or to dataflows? Commit to your answer.

Concept: Reports can connect to dataflows as their data source, using the cleaned data entities instead of raw sources.

In Power BI Desktop, you connect to Power BI dataflows by choosing 'Power Platform' > 'Power BI dataflows'. You select the entity you want, and it loads as a dataset for your report visuals.

Result

Reports use consistent, pre-cleaned data from dataflows, simplifying report building.

Understanding this connection clarifies how dataflows improve data consistency and reduce report complexity.

4

Intermediate

Managing dataflow refresh and dependencies

🤔Before reading on: Should dataflows refresh automatically or only when manually triggered? Commit to your answer.

Concept: Dataflows can be scheduled to refresh data regularly, and some dataflows depend on others for their data.

You set refresh schedules in Power BI service to keep dataflows up to date. When one dataflow uses entities from another, it creates a dependency chain that refreshes in order.

Result

Data stays current without manual intervention, and dependencies ensure correct data load order.

Knowing refresh and dependencies prevents stale data and refresh errors in complex dataflow setups.

5

Intermediate

Using computed entities in dataflows

Concept: Computed entities let you create new data entities by referencing and transforming existing dataflow entities.

Within a dataflow, you can create an entity that uses Power Query to combine or transform other entities. This avoids duplicating data and supports modular data prep.

Result

You build layered dataflows that are easier to maintain and update.

Using computed entities promotes reusability and cleaner data architecture in Power BI.

6

Advanced

Incremental refresh in dataflows

🤔Before reading on: Do you think dataflows reload all data every refresh or only new data? Commit to your answer.

Concept: Incremental refresh lets dataflows update only new or changed data, saving time and resources.

You configure parameters in Power Query to filter data by date or other criteria. Power BI then refreshes only recent data during scheduled refreshes.

Result

Faster refreshes and reduced load on data sources, especially for large datasets.

Understanding incremental refresh is key for scaling dataflows in enterprise environments.

7

Expert

Integrating dataflows with Azure Data Lake Storage

🤔Before reading on: Do you think dataflows store data only inside Power BI or can they integrate with external storage? Commit to your answer.

Concept: Power BI dataflows can store data in Azure Data Lake Storage Gen2, enabling enterprise-grade data management and integration.

By linking dataflows to an Azure Data Lake, organizations gain direct access to dataflow entities as files. This allows advanced analytics, data science, and integration with other Azure services.

Result

Dataflows become part of a larger data ecosystem beyond Power BI, supporting complex workflows.

Knowing this integration unlocks powerful enterprise scenarios and bridges BI with big data platforms.

Under the Hood

Dataflows use Power Query Online to run data transformation scripts in the cloud. When a dataflow refreshes, it connects to source systems, applies the query steps, and stores the results as entities in Azure Data Lake Storage behind the scenes. These entities are stored in a standardized format (CDM folders) that Power BI and other tools can access. Refresh schedules trigger these processes automatically, and dependencies between dataflows ensure correct load order.

Why designed this way?

Dataflows were designed to separate data preparation from report building, enabling reuse and collaboration. Using cloud storage and Power Query Online allows centralized management and scalability. The choice of Azure Data Lake Storage as backend supports enterprise data governance and integration. Alternatives like local data prep or report-embedded queries were less scalable and caused duplication.

┌───────────────┐       ┌─────────────────────┐       ┌───────────────┐
│ Data Sources  │──────▶│ Power Query Online  │──────▶│ Azure Data    │
│ (Files, DBs)  │       │ (Transformations)   │       │ Lake Storage  │
└───────────────┘       └─────────────────────┘       └───────────────┘
                                                        │
                                                        ▼
                                               ┌─────────────────┐
                                               │ Power BI Service │
                                               │ (Dataflow Access)│
                                               └─────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do dataflows replace datasets completely? Commit to yes or no.

Common Belief: Dataflows replace datasets, so you only need dataflows for all data work.

Reality: Dataflows prepare and store data, but datasets in Power BI Desktop still model and visualize data. Both work together.

Why it matters: Thinking dataflows replace datasets leads to confusion about where to build reports and how to model data.

Quick: Do dataflows refresh instantly when source data changes? Commit to yes or no.

Common Belief: Dataflows always have the latest data immediately after source updates.

Reality: Dataflows refresh on schedule or manual trigger; they do not update instantly with source changes.

Why it matters: Assuming instant refresh causes wrong expectations and stale data in reports if refresh is not managed.

Quick: Can dataflows connect to any data source Power BI Desktop can? Commit to yes or no.

Common Belief: Dataflows support all data sources that Power BI Desktop supports.

Reality: Dataflows support fewer connectors than Power BI Desktop; some sources require datasets instead.

Why it matters: Trying to use unsupported sources in dataflows wastes time and causes errors.

Quick: Do dataflows always improve performance of reports? Commit to yes or no.

Common Belief: Using dataflows always makes reports faster and better.

Reality: Dataflows can add overhead if not designed well; sometimes direct queries or datasets are faster.

Why it matters: Blindly using dataflows without design consideration can hurt report performance.

Expert Zone

1

Dataflows store data in Common Data Model (CDM) folders, enabling interoperability with other Microsoft and Azure services.

2

Computed entities in dataflows allow modular and layered data preparation, reducing duplication and easing maintenance.

3

Incremental refresh in dataflows requires careful parameter setup and understanding of source data change patterns to avoid data gaps.

When NOT to use

Avoid dataflows when you need real-time data updates or when your data source is unsupported by dataflows. In such cases, use direct query datasets or live connections. Also, for very simple or one-off reports, building queries directly in Power BI Desktop may be faster.

Production Patterns

In enterprises, dataflows are used as a central data preparation layer feeding multiple reports and dashboards. They are often combined with Azure Data Lake Storage for data governance and advanced analytics. Teams schedule refreshes during off-hours and monitor dependencies to ensure data freshness. Computed entities and linked dataflows create modular pipelines that scale with business needs.

Connections

ETL (Extract, Transform, Load)

Dataflows implement ETL processes in a cloud-native way within Power BI.

Understanding ETL helps grasp why dataflows separate data prep from reporting and how they improve data quality.

Cloud Storage Systems

Dataflows store data in Azure Data Lake Storage, a cloud storage service.

Knowing cloud storage concepts clarifies how dataflows scale and integrate with other cloud tools.

Batch Cooking

Dataflows batch process data preparation once for many uses, similar to batch cooking meals.

This connection highlights efficiency gains from preparing data once and reusing it multiple times.

Common Pitfalls

#1 Trying to refresh dataflows too frequently causing failures.

Wrong approach:

Setting dataflow refresh schedule to every 5 minutes without considering source limits.

Correct approach:

Scheduling dataflow refreshes at reasonable intervals like daily or hourly based on data change frequency.

Root cause:

Misunderstanding dataflow refresh limitations and source system capacity.

#2 Using unsupported data sources in dataflows causing errors.

Wrong approach:

Connecting dataflow to a data source only supported in Power BI Desktop, like certain ODBC sources.

Correct approach:

Using datasets or direct queries for unsupported sources, or checking connector availability before building dataflows.

Root cause:

Assuming all Power BI Desktop connectors work in dataflows.

#3 Duplicating data preparation steps in multiple dataflows.

Wrong approach:

Copying the same transformation logic into several dataflows instead of reusing entities.

Correct approach:

Using computed entities or linked dataflows to share common transformations.

Root cause:

Not leveraging modular design features of dataflows.

Key Takeaways

Dataflows centralize data preparation in the cloud, enabling reuse and consistency across Power BI reports.

They use Power Query Online to transform data and store results in Azure Data Lake Storage as entities.

Connecting reports to dataflows ensures all reports use the same cleaned data, reducing errors and duplicated work.

Proper refresh scheduling and understanding dependencies keep dataflows up to date and reliable.

Advanced features like incremental refresh and Azure Data Lake integration unlock enterprise-scale data management.