What is Incremental Refresh in Power BI?

Power BI Incremental Refresh is an architectural solution designed to improve data loading. It works by partitioning the dataset at the semantic model level.

Using Incremental refresh solves the problems that occur when data volume grows into terabytes and removes the need to move large amounts of static historical data across networks

This helps datasets stay within time limits. Power BI Pro licenses have a two-hour timeout for refreshes, and Premium capacities allow up to five hours. Using Power BI Incremental Refresh keeps operations within these limits and also changes the cost profile of data operations. By limiting queries to recent data partitions it lowers costs in cloud data warehouses.

Why Use Incremental Refresh Over Full Refresh in Power BI?

Incremental Refresh in Power BI has major architectural benefits over the standard Full Refresh model. It is the necessary choice for large data analysis that changes the cost of data operations in cloud environments.

1. Full refreshes work well for small projects. But moving huge amounts of old data across networks to add small changes makes little sense. It costs too much money and time.
   
2. In data warehouses like Snowflake or Google BigQuery, computing costs link directly to the data volume scanned. Asking for static history records over and over wastes money.
   
3. Power BI incremental refresh also helps meet Service Level Agreements (SLAs). Power BI Pro licenses have a hard two-hour time limit on refresh actions. Premium capacities allow up to five hours.
   
4. Power BI incremental refresh is the main way to deliver data for datasets close to these limits. Internal tests show that refresh actions longer than 30 minutes fail about 60% more often. This happens because of network issues. Moving to a Power BI incremental refresh framework can raise success rates to 99.7%.

✕

Book a Free Consultation

Share your details and our experts will reach out to discuss your goals.

How to Set Up Incremental Refresh in Power BI (4 Steps)

1. Set Parameters and Data Type Rules

To set up Power BI incremental refresh effectively, you must first set the correct parameters within Power Query. This is the base of the partition design. Key steps include:

• Set RangeStart and RangeEnd: You must make two specific Power Query parameters named RangeStart and RangeEnd. These names care about capitalization. Using names like rangestart or Range_Start will not start the automatic policy creation in the Service.
  
• Use DateTime Data Types: You must configure these parameters as the DateTime data type. The Power BI Service will not see them if the source column is a date like 2023-01-01 or an integer key.
  
• Configure Filter Logic: Apply these parameters to your main date column. The standard filtering pattern must include the start and exclude the end to keep data correct. Use >= RangeStart and < RangeEnd.

2. Check Query Folding Mechanics

When building a visual data frame, you must check that the Power Query engine sends your requests back to the source system. If this folding breaks, incremental refresh fails. Key steps include:

• Check Native Query Translation: Check that the Power Query engine (M engine) can change your filter logic into the native query language of the source. Examples include T-SQL or PL/SQL.
  
• Look for Folding Breaks: Right-click your step in Power Query. Look for View Native Query. If this option is grey, the engine must pull the whole dataset into memory. It then filters locally. This stops incremental refresh from working.
  
• Remove Unsupported Functions: Take out complex M functions or manual SQL overrides. These block the engine from adding dynamic date filters to the WHERE clause.

3. Set the Tabular Object Model (TOM) Policies

Planning your data migration means stating how partitions work within the Tabular Object Model (TOM). Key steps include:

• Set Archive Partitions: Set the rule for history data. For example, store rows for 5 years. These partitions become read-only after processing. This keeps their compression state and keeps history unchanged.
  
• Set Incremental Partitions: Set the changing window. For example, refresh rows from the last 10 days. The system drops and re-processes these during every cycle to get new or modified records.
  
• Automate Lifecycle Management: Use the Power BI Service to handle the moving window. As time passes, incremental partitions turn into archived partitions without manual work.

4. Run Initial Load and Bootstrap

Running the first data move needs careful planning to stop timeouts. This matters most for multi-billion row datasets. Key steps include:

• Check Initial Data: Publish the dataset to the service. The first Power BI incremental refresh tries to process all history partitions in one transaction.
  
• Bootstrapping via XMLA: Sometimes the full history load goes over the 5-hour limit. Use SQL Server Management Studio (SSMS) via the XMLA endpoint to process partitions in batches. Do one year at a time.
  
• Parallelism Control: Use custom TMSL scripts for Premium capacities. This raises partition processing parallelism. It gets the most speed out of your capacity nodes.

What are the Challenges With Power BI Incremental Refresh Setup?

The Native Query Trap and Folding Failures

Moving to Power BI incremental refresh has difficulties. Connecting existing data queries is hard. A common problem reported is the Native Query trap. Users often paste complex SQL statements directly into the Get Data dialog. This disables query folding.

This folding failure causes big problems. The mashup engine tries to download the full history for every single partition instead of sending light queries for small slices of data. This causes huge memory usage and timeouts. 

You should move changes to the Power Query interface. Or wrap them in Value.NativeQuery with EnableFolding=true. This needs advanced M coding skills.

Dictionary Fragmentation in VertiPaq

Moving to a long-running power bi incremental refresh plan is hard because of Dictionary Fragmentation. Power BI shrinks data by making a dictionary of distinct values for each column. 

When partitions refresh, the system adds new unique values. But when old data leaves, the system does not remove the linked unique values automatically.

This leads to dictionary bloat. A dataset size can grow by gigabytes even if the row count stays the same. You must watch this. Schedule fix processes using XMLA commands. These force the engine to rebuild dictionaries and free up memory.

The Black Box of Monitoring

Moving from on-premise tools like SSAS to Power BI brings a challenge. The Power BI incremental refresh process is hard to see inside. The standard Monitor hub in the Power BI Service gives a pass or fail status. It lacks detail on which partitions were processed.

This visibility gap causes doubt. You do not know if the incremental policy works or if the system silently runs full refreshes. Advanced users use verification methods to stay on track. They put timestamps in the source. Or they use DAX Studio to query Dynamic Management Views (DMVs) to check partition properties.

Complex Source Connections (SAP HANA and Delta Tables)

Setting up power bi incremental refresh seems simple. But specific data sources cause trouble. SAP HANA users often use complex native SQL to skip slow ODBC drivers. This breaks query folding. Also, the Delta Lake connector queries folders of Parquet files. This limits standard folding.

You need specific design changes for a smooth move. For SAP HANA, you must make Calculation Views at the database level to bring back folding. For Delta Tables, you must partition the files by date at the storage level. This turns on Partition Elimination. This can lower refresh times from 30 minutes to 26 seconds.

Knowledge Gap in Advanced Designs

A common challenge during the move to incremental refresh is the knowledge gap about advanced designs like Hybrid Tables. This feature lets a single table use mixed storage modes. It uses Import for history and DirectQuery for real-time data.

Setting this up with Power BI incremental refresh requires a deep grasp of the VertiPaq engine and Premium capacity control. Teams might miss the chance to get fast history reporting mixed with real-time analytics without specific training.

What are the Power BI Incremental Refresh Best Practices?

1. Create a Parameter and Source Plan

A structured parameter plan is needed when setting up incremental refresh. You must check that your source system can take the injected filter predicates. This helps teams find problems early when setting up Power BI incremental refresh.

Checking that the source system is ready for date-range queries is key. This includes SQL Server, Oracle, or Snowflake. Using backend tuning plans like index tuning on date columns is good. It lowers report refresh times by up to 40%.

2. Use Hybrid Tables for Real-Time Needs

Power BI Hybrid Tables act as a key tool for needs requiring historical depth and real-time freshness. You configure history partitions to Import mode. You configure the current day partition to DirectQuery mode. This solves the trade-off between speed and freshness.

This makes reporting simpler. It lets users see transactions as they happen without starting a dataset refresh. Using this feature hides the hard work of handling partitions manually. It needs a Premium workspace to work.

3. Use Metadata for Binary File Speed

Making a plan for binary files is key if your data lives in CSVs on Azure Blob or SharePoint. People often think that Power BI incremental refresh only works for structured BI databases. But expert methods using file metadata give big gains.

Filter on the file Date Created metadata before pulling content. The Power Query engine can then skip opening files outside the refresh window. Tests show this method cuts processing time by 83%. It drops refresh times from 94 seconds to 16 seconds.

4. Test Data and Validate with DMVs

Data testing is key when using automated partition control. The Service UI lacks detail. Building a custom admin report that queries the DMVs of your Premium datasets is a good practice.

This process keeps data trustworthy for migration teams. You should check that only the incremental partitions change their Refreshed Time property during standard updates. This proves that the policy is active. It proves the system is not defaulting to a full refresh due to folding errors.

5. Use Detect Data Changes for Polling Speed

Recreating polling logic within Power BI is key for datasets where the incremental window is large. But actual changes are rare. The Detect Data Changes feature gives a second layer of speed. It queries the maximum value of a LastUpdateDate column.

At Entrans, we suggest this for compliance data or audit logs. If the value has not changed, the service skips processing that partition. This lowers the number of processed partitions to zero. It results in very fast refreshes.

6. Create a Solid Maintenance Schedule

Making a solid maintenance plan is key to long-term health. Do not treat incremental refresh as set and forget. You must schedule periodic defragmentation operations using the XMLA endpoint. This stops speed loss.

Run a JSON command like { "refresh": { "type": "defragment" } }. This forces the engine to rebuild dictionaries. A solid maintenance framework keeps your dataset size small. It stops query speed from getting worse over time due to artifact growth.