Dataflow entities in Power BI - Deep Dive

Overview - Dataflow entities

What is it?

Dataflow entities are structured tables created and managed within Power BI dataflows. They store cleaned and transformed data that can be reused across multiple Power BI reports and dashboards. Think of them as reusable data building blocks that help organize and prepare data before analysis. They simplify data management by centralizing data preparation in the cloud.

Why it matters

Without dataflow entities, each report would need to load and transform raw data independently, causing repeated work and inconsistent data. Dataflow entities solve this by enabling data reuse and consistency across reports. This saves time, reduces errors, and improves collaboration in teams. Without them, managing large or complex data sources would be slow and error-prone.

Where it fits

Before learning dataflow entities, you should understand basic Power BI concepts like datasets, queries, and data transformation with Power Query. After mastering dataflow entities, you can explore advanced topics like incremental refresh, dataflows linked entities, and enterprise data governance in Power BI.

Mental Model

Core Idea

Dataflow entities are reusable, cloud-stored tables that centralize and standardize data preparation for multiple Power BI reports.

Think of it like...

Imagine a bakery that bakes batches of dough in advance (dataflow entities) so different bakers can use the same dough to make various pastries (reports) without starting from scratch each time.

Power BI Dataflow
┌─────────────────────────────┐
│        Data Sources         │
└─────────────┬───────────────┘
              │
       Dataflow Entities
┌─────────────┴───────────────┐
│  Cleaned & Transformed Data  │
└─────────────┬───────────────┘
              │
      Power BI Reports
┌─────────────┴───────────────┐
│   Visualizations & Insights  │
└─────────────────────────────┘

Build-Up - 7 Steps

1

Foundation

What Are Dataflow Entities

Concept: Introduce the basic idea of dataflow entities as tables stored in Power BI dataflows.

Dataflow entities are tables created inside Power BI dataflows. They hold data that has been cleaned and shaped using Power Query online. These entities live in the cloud and can be used by many reports, so you don't have to repeat data preparation steps in each report.

Result

You understand that dataflow entities are reusable tables stored in Power BI service, separate from individual reports.

Knowing that dataflow entities exist outside reports helps you see how data preparation can be centralized and reused.

2

Foundation

Creating Entities with Power Query Online

Concept: Learn how to create and shape dataflow entities using Power Query in the Power BI service.

Inside Power BI service, you create a dataflow and add entities by connecting to data sources. You use Power Query Online to clean, filter, and transform data. Once saved, these entities become tables ready for use in reports.

Result

You can create a dataflow entity that holds transformed data from a source like Excel or SQL database.

Understanding that Power Query Online works similarly to Power Query Desktop but in the cloud clarifies how dataflows prepare data.

3

Intermediate

Reusing Entities Across Reports

🤔Before reading on: Do you think each report needs its own copy of data, or can they share dataflow entities? Commit to your answer.

Concept: Explore how multiple Power BI reports can connect to the same dataflow entities to share data.

Reports connect to dataflow entities as their data source instead of raw data sources. This means many reports can use the same cleaned data, ensuring consistency and saving time. Changes in the dataflow entity update all connected reports after refresh.

Result

Reports show consistent data and require less maintenance because they share dataflow entities.

Knowing that dataflow entities enable data reuse prevents duplicated work and reduces errors across reports.

4

Intermediate

Linked Entities and Dataflow Dependencies

🤔Before reading on: Do you think dataflow entities can only come from raw data, or can they also come from other dataflows? Commit to your answer.

Concept: Learn about linked entities that let one dataflow use entities from another dataflow, creating dependencies.

Linked entities allow a dataflow to reference entities from another dataflow instead of raw sources. This creates a chain of dataflows where one prepares data that others build upon. It helps organize complex data preparation in layers.

Result

You can build modular dataflows where entities depend on others, improving manageability.

Understanding linked entities reveals how to build scalable and maintainable data preparation pipelines.

5

Intermediate

Refreshing Dataflow Entities

Concept: Understand how dataflow entities get updated with fresh data and how refresh schedules work.

Dataflow entities refresh by re-running their Power Query transformations on the source data. You can schedule refreshes in Power BI service to keep data current. Refresh failures affect all reports using those entities, so monitoring is important.

Result

Dataflow entities hold up-to-date data that reports rely on for accurate insights.

Knowing how refresh works helps you plan data update frequency and troubleshoot data freshness issues.

6

Advanced

Incremental Refresh in Dataflow Entities

🤔Before reading on: Do you think dataflow entities always reload all data, or can they load only new data? Commit to your answer.

Concept: Explore how incremental refresh lets dataflow entities update only new or changed data to improve performance.

Incremental refresh in dataflows allows entities to load only recent data changes instead of full reloads. This reduces refresh time and resource use, especially for large datasets. It requires setting date/time parameters and configuring policies.

Result

Dataflow entities refresh faster and more efficiently, enabling timely data availability.

Understanding incremental refresh unlocks performance optimization for large-scale dataflows.

7

Expert

Dataflow Entities in Enterprise Data Architecture

🤔Before reading on: Do you think dataflow entities are just for small projects, or can they support enterprise-wide data governance? Commit to your answer.

Concept: Learn how dataflow entities fit into enterprise data strategies for governance, security, and collaboration.

In large organizations, dataflow entities serve as standardized, governed data layers. They enable data stewards to control data quality and access centrally. Integration with Azure Data Lake Storage Gen2 allows advanced data management and lineage tracking. This supports compliance and collaboration across teams.

Result

Dataflow entities become key components in enterprise BI, ensuring trusted and secure data for all users.

Knowing the enterprise role of dataflow entities helps you design scalable, compliant BI solutions.

Under the Hood

Dataflow entities are stored as tables in Azure Data Lake Storage Gen2 behind the scenes. When you create or refresh an entity, Power Query Online runs the transformation steps and writes the output as parquet files in the data lake. Power BI service manages metadata and access. Reports query these parquet files via the Power BI engine, enabling fast, consistent data retrieval.

Why designed this way?

This design separates data preparation from reporting, allowing reuse and scalability. Using Azure Data Lake Storage Gen2 provides a secure, scalable, and cost-effective storage layer. Power Query Online in the cloud enables centralized data transformation without needing local resources. Alternatives like embedding transformations in reports were less efficient and harder to manage at scale.

┌───────────────────────────────┐
│       Power BI Service         │
│ ┌───────────────┐             │
│ │ Power Query   │             │
│ │ Online Engine │             │
│ └───────┬───────┘             │
└────────┬┴────────┬────────────┘
         │         │
         │         │
┌────────▼─────────▼───────────┐
│ Azure Data Lake Storage Gen2 │
│  (Parquet files for entities)│
└────────┬─────────┬───────────┘
         │         │
         │         │
┌────────▼─────────▼───────────┐
│    Power BI Reports & Dashboards│
│   Query dataflow entities here │
└───────────────────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Do you think dataflow entities automatically update in reports without refresh? Commit yes or no.

Common Belief: Dataflow entities update instantly in all reports as soon as source data changes.

Reality: Dataflow entities update only after a scheduled or manual refresh runs in Power BI service.

Why it matters: Assuming instant updates can cause confusion when reports show outdated data, leading to wrong decisions.

Quick: Do you think dataflow entities store data locally on your computer? Commit yes or no.

Common Belief: Dataflow entities store data locally on the user's machine like Excel files.

Reality: Dataflow entities store data in the cloud on Azure Data Lake Storage Gen2, not locally.

Why it matters: Thinking data is local can cause misunderstandings about data sharing, refresh, and access permissions.

Quick: Do you think linked entities create copies of data or just references? Commit your answer.

Common Belief: Linked entities duplicate data from other dataflows, increasing storage use.

Reality: Linked entities reference existing entities without duplicating data, saving storage and ensuring consistency.

Why it matters: Misunderstanding this can lead to inefficient dataflow designs and unnecessary storage costs.

Quick: Do you think incremental refresh is enabled by default on dataflow entities? Commit yes or no.

Common Belief: Incremental refresh happens automatically for all dataflow entities.

Reality: Incremental refresh must be explicitly configured with parameters and policies; it is not automatic.

Why it matters: Assuming automatic incremental refresh can cause slow refreshes and performance issues.

Expert Zone

1

Dataflow entities support computed entities that derive data from other entities within the same dataflow, enabling complex transformations without external dependencies.

2

The storage format (parquet) used by dataflow entities allows efficient columnar storage and compression, improving query performance in Power BI reports.

3

Dataflow entities can be secured with role-level security at the dataflow level, but this requires careful planning since it differs from dataset-level security.

When NOT to use

Dataflow entities are not ideal when you need real-time data updates or very low latency, as refresh cycles introduce delays. In such cases, direct query or live connection to source systems is better. Also, for very simple or one-off reports, creating dataflows may add unnecessary complexity.

Production Patterns

In production, teams use dataflow entities to create a centralized data preparation layer, often integrating with Azure Data Lake for enterprise data governance. They build layered dataflows with raw, cleaned, and business entities. Automated refresh schedules and monitoring ensure data freshness. Dataflows are version-controlled and documented to support collaboration.

Connections

ETL (Extract, Transform, Load)

Dataflow entities implement ETL processes in the cloud within Power BI.

Understanding ETL helps grasp how dataflow entities extract raw data, transform it with Power Query, and load it as reusable tables.

Data Lake Storage

Dataflow entities store data in Azure Data Lake Storage Gen2, linking BI to big data storage.

Knowing about data lakes clarifies how dataflow entities scale and integrate with enterprise data platforms.

Modular Programming

Dataflow entities promote modular design by separating data preparation into reusable components.

Recognizing modularity in dataflows helps design maintainable and scalable BI solutions, similar to software engineering.

Common Pitfalls

#1 Using dataflow entities without scheduling refresh, expecting data to update automatically.

Wrong approach:

Create dataflow entity and connect reports without setting any refresh schedule.

Correct approach:

Create dataflow entity and configure scheduled refresh in Power BI service to keep data updated.

Root cause:

Misunderstanding that dataflow entities require explicit refresh to update data.

#2 Duplicating dataflow entities by copying data instead of linking, causing storage bloat.

Wrong approach:

Create multiple dataflows each importing the same raw data instead of using linked entities.

Correct approach:

Use linked entities to reference existing dataflow entities and avoid duplication.

Root cause:

Not knowing linked entities exist or how to use them properly.

#3 Trying to apply incremental refresh without defining required parameters.

Wrong approach:

Enable incremental refresh on dataflow entity without setting date/time parameters in Power Query.

Correct approach:

Define date/time parameters in Power Query and configure incremental refresh policy accordingly.

Root cause:

Lack of understanding of incremental refresh prerequisites and configuration steps.

Key Takeaways

Dataflow entities are reusable tables stored in the cloud that centralize data preparation for Power BI reports.

They enable consistent, efficient data reuse across multiple reports, saving time and reducing errors.

Power Query Online is used to create and transform dataflow entities within Power BI service.

Linked entities allow building modular, layered dataflows by referencing entities from other dataflows.

Proper refresh scheduling and incremental refresh configuration are essential for keeping dataflow entities current and performant.

Practice

1. What is the main purpose of a Dataflow entity in Power BI?

easy

A. To prepare and store data centrally for reuse across reports

B. To create visual charts and graphs

C. To write DAX formulas for calculations

D. To manage user permissions in Power BI

Solution

1. Step 1: Understand what Dataflow entities do

   Dataflow entities are designed to prepare and store data centrally so it can be reused in multiple reports.

2. Step 2: Compare options with this purpose

   Only To prepare and store data centrally for reuse across reports matches this purpose; others describe different Power BI features.

3. Final Answer:

   To prepare and store data centrally for reuse across reports -> Option A

4. Quick Check:

   Dataflow entities = Central data prep and reuse [OK]

Hint: Dataflow entities store reusable data, not visuals or formulas [OK]

Common Mistakes:

• Confusing dataflows with report visuals
• Thinking dataflows manage permissions
• Mixing dataflows with DAX calculations

2. Which syntax correctly describes how to create a new entity in a Power BI Dataflow using Power Query online?

easy

A. Upload Excel files without any transformation

B. Write a DAX formula in the Dataflow editor

C. Use SQL commands directly in the Dataflow interface

D. Select 'New Entity' and use Power Query editor to define data transformations

Solution

1. Step 1: Identify how entities are created in Dataflows

   Entities are created by selecting 'New Entity' and using Power Query online to transform data.

2. Step 2: Check other options for correctness

   DAX formulas and SQL commands are not used directly in Dataflows; uploading files without transformation is incomplete.

3. Final Answer:

   Select 'New Entity' and use Power Query editor to define data transformations -> Option D

4. Quick Check:

   New Entity + Power Query = Correct syntax [OK]

Hint: Dataflow entities use Power Query, not DAX or SQL directly [OK]

Common Mistakes:

• Trying to write DAX in Dataflow creation
• Assuming SQL commands run in Dataflow editor
• Ignoring the need for data transformation steps

3. Given a Dataflow entity that filters sales data for the year 2023 using Power Query, what will be the output if the filter step is removed?

medium

A. All sales data including years other than 2023 will be included

B. Only sales data for 2023 will be shown

C. No data will be returned because filter is mandatory

D. The Dataflow will show an error and not load

Solution

1. Step 1: Understand the role of the filter step

   The filter step limits data to sales from 2023 only.

2. Step 2: Predict output without the filter

   Removing the filter means no restriction, so all sales data from all years will be included.

3. Final Answer:

   All sales data including years other than 2023 will be included -> Option A

4. Quick Check:

   Removing filter = All data included [OK]

Hint: No filter means no restriction on data rows [OK]

Common Mistakes:

• Assuming filter removal causes error
• Thinking filter is mandatory for dataflow
• Believing only 2023 data remains without filter

4. You created a Dataflow entity but it fails to refresh with an error about missing credentials. What is the best way to fix this?

medium

A. Delete and recreate the Dataflow entity

B. Change the entity name to a simpler one

C. Update the data source credentials in the Dataflow settings

D. Remove all transformations from the entity

Solution

1. Step 1: Identify cause of refresh failure

   Missing credentials error means Power BI cannot access the data source due to invalid or missing login info.

2. Step 2: Apply correct fix

   Updating the data source credentials in Dataflow settings restores access and allows refresh.

3. Final Answer:

   Update the data source credentials in the Dataflow settings -> Option C

4. Quick Check:

   Fix missing credentials by updating login info [OK]

Hint: Check and update credentials when refresh fails [OK]

Common Mistakes:

• Recreating entity unnecessarily
• Changing names does not fix credentials
• Removing transformations won't fix access issues

5. You want to create a Dataflow entity that combines customer data from two different sources and removes duplicates. Which approach is best?

hard

A. Write a DAX measure to merge and deduplicate data

B. Use Power Query to append both sources, then apply 'Remove Duplicates' step

C. Load both sources directly into Power BI without transformation

D. Create two separate entities and join them in Power BI Desktop

Solution

1. Step 1: Understand combining data in Dataflow

   Power Query in Dataflows can append multiple sources into one entity.

2. Step 2: Remove duplicates in Dataflow

   Applying 'Remove Duplicates' step in Power Query cleans the combined data before reuse.

3. Step 3: Evaluate other options

   Joining in Power BI Desktop or using DAX is less efficient; loading without transformation keeps duplicates.

4. Final Answer:

   Use Power Query to append both sources, then apply 'Remove Duplicates' step -> Option B

5. Quick Check:

   Append + Remove Duplicates in Dataflow = Best practice [OK]

Hint: Combine and clean data early in Dataflow with Power Query [OK]

Common Mistakes:

• Trying to merge data only in Power BI Desktop
• Ignoring duplicates before loading data
• Using DAX measures for data prep instead of Dataflow