Dataflows for Incremental Data Loads

Introduction

In the world of data analytics and business intelligence, managing large datasets efficiently is crucial. As organizations accumulate more data from various sources, ensuring timely and optimized data refreshes becomes a key requirement. One powerful feature in Power BI that supports this need is Dataflows, especially when combined with incremental data loads.

Incremental data loading allows you to update only the new or changed records instead of refreshing the entire dataset. This drastically reduces refresh times, conserves resources, and improves overall performance. Whether you’re working with millions of rows or integrating data from cloud-based or on-premise sources, combining dataflows with incremental refresh is a game-changer for modern analytics solutions.

In this article, we’ll dive deep into Dataflows for incremental data loads—what they are, how they work, how to configure them, and best practices for implementation.

What Are Power BI Dataflows?

Power BI Dataflows are a self-service data preparation feature in the Power BI service that allows users to ingest, transform, and load data from various sources using Power Query Online. They operate in the Power BI cloud environment and can be reused across multiple datasets and reports.

In simpler terms, dataflows are like centralized ETL (Extract, Transform, Load) pipelines that use the same familiar Power Query engine found in Power BI Desktop, but operate in the Power BI Service. Once configured, dataflows can refresh data on a scheduled basis and feed that data into Power BI datasets.

Dataflows are especially useful when:

You want to prepare data once and reuse it in multiple reports.
You need to separate data transformation logic from report creation.
You’re working in a collaborative, enterprise-scale BI environment.

What is Incremental Data Loading?

Incremental data loading is the process of only importing new or modified data during a refresh, rather than loading the entire dataset every time. This approach is highly beneficial when working with large datasets because:

It significantly reduces refresh duration.
It lowers the load on data sources.
It minimizes resource consumption in Power BI.

Power BI supports incremental refresh for both datasets and dataflows, but when implemented in dataflows, it adds flexibility and scalability by separating the ETL from the model and report.

Why Use Incremental Data Loads in Dataflows?

Using incremental data loads in dataflows makes perfect sense for organizations managing high-volume or frequently updated datasets. Here are some of the core benefits:

1. Improved Performance

By only processing new or changed data, the refresh time is significantly faster, which can be especially valuable when dealing with time-sensitive analytics.

2. Efficient Resource Usage

Full data loads consume more memory, bandwidth, and processing power. Incremental loads reduce this overhead and can help you stay within Power BI’s service limits.

3. Scalable Architecture

As your data grows, a scalable approach becomes essential. Incremental dataflows allow you to build solutions that scale efficiently without degrading performance.

4. Data Consistency and Accuracy

By focusing only on the changing parts of your data, you reduce the chance of introducing inconsistencies or duplication, especially when dealing with historical data.

How Incremental Refresh Works in Dataflows

To enable incremental refresh in Power BI dataflows, you need to:

Define a Date/Time Column
The table used in the dataflow must contain a column with date/time data, typically representing when the record was last updated or inserted.
Use Reserved Parameters
Power BI requires two parameters to control the incremental logic:
- RangeStart – the beginning of the date range for the refresh
- RangeEnd – the end of the date range for the refresh
Filter the Data Using These Parameters
In Power Query, you must apply a filter to the date/time column using the RangeStart and RangeEnd parameters.
Enable Incremental Refresh in Settings
After saving the dataflow, go into the dataflow settings and enable incremental refresh, specifying:
- How much historical data to store
- How often to detect changes in existing rows (optional)

Once configured, Power BI will manage refreshes intelligently, appending new data and optionally refreshing only recent changes.

Step-by-Step: Setting Up Incremental Data Loads in a Dataflow

Let’s walk through the process of configuring a dataflow with incremental refresh:

Step 1: Create a New Dataflow

In Power BI Service, navigate to your workspace.
Click New > Dataflow.
Choose Add new tables.

Step 2: Connect to Your Data Source

Choose a data source (e.g., SQL Server, Azure Blob Storage, SharePoint, etc.). Load your table into Power Query.

Step 3: Add Date Parameters

Click Manage Parameters, and create two parameters:

RangeStart: DateTime type, default to a past date (e.g., 1/1/2020)
RangeEnd: DateTime type, default to current date (e.g., =DateTime.LocalNow())

Step 4: Filter Your Data

Apply a filter to your date/time column using the two parameters:

= Table.SelectRows(Source, each [ModifiedDate] >= RangeStart and [ModifiedDate] < RangeEnd)

Step 5: Save the Query

Finish your transformations and click Next, then name and save the table.

Step 6: Configure Incremental Refresh

Once the dataflow is saved:

Open the dataflow settings.
Click Incremental refresh.
Select the table and enable the toggle.
Define:
- How much historical data to store (e.g., 5 years)
- How much of the recent data to refresh (e.g., last 5 days)

Step 7: Refresh the Dataflow

Schedule your dataflow refresh. Power BI will handle the logic behind the scenes, refreshing only the necessary partitions.

Tips and Best Practices

1. Ensure Reliable Timestamps

Incremental refresh depends on a trustworthy date/time column. Ideally, use a column that represents the last modified time or transaction time to avoid missing updates.

2. Avoid Transformation After Filter

Apply the RangeStart/RangeEnd filter as early as possible in the Power Query steps. Applying it later may force the engine to evaluate the full dataset, defeating the purpose of incremental refresh.

3. Use Partition Awareness

Behind the scenes, Power BI partitions the data by time. Understanding this helps you troubleshoot or optimize refresh behavior.

4. Combine with Linked Entities

In enterprise scenarios, you can use linked entities to consume dataflows across multiple workspaces, allowing you to reuse incremental dataflows in multiple reports and models.

5. Monitor and Optimize

Use Power BI Premium‘s refresh history and metrics app to monitor refresh performance. Keep an eye on durations and failure rates to make adjustments as needed.

Limitations and Considerations

Despite the benefits, there are some limitations you should be aware of:

Premium Required for Large Models: Incremental refresh in datasets over 1 GB requires Power BI Premium.
Parameter Naming Is Strict: You must use the exact parameter names RangeStart and RangeEnd.
Data Types Matter: Your filtering column must be of type DateTime—not just Date or Text.
M Language Only: Transformations must be supported in Power Query (M), which limits some advanced logic.
No Refresh for Deleted Records: If a row is deleted in the source, incremental refresh won’t detect it. Consider full refreshes periodically to reconcile.

Use Cases for Incremental Dataflows

1. Financial Transactions

Track only new financial transactions added daily, avoiding reprocessing months of historic data.

2. Web and App Analytics

Load fresh user activity or session logs while preserving historical data untouched.

3. CRM or ERP Systems

Ingest customer, product, or order records updated in the last 24 hours instead of reloading the entire table.

4. IoT Data

Continuously stream and store new sensor readings while efficiently managing storage and refresh schedules.