Intermittent refresh failures are some of the most frustrating Power BI issues teams face. The same dataset refreshes successfully for days, sometimes weeks, and then suddenly fails without any obvious change. The next refresh might succeed again, leaving everyone confused.
Because failures are inconsistent, they are often dismissed as temporary glitches. Over time, however, these failures erode trust in reports and create operational noise.
This article explains why Power BI refreshes fail intermittently in real production environments and why these failures are usually symptoms of underlying design or operational problems.
Why Intermittent Failures Are Hard to Take Seriously
When a refresh fails once and then succeeds the next time, teams naturally assume the problem fixed itself. There is no visible pattern, and no recent change to blame.
This mindset is dangerous.
Intermittent failures rarely disappear on their own. They usually become more frequent as data volume, usage, or dependency complexity increases.
Real-World Scenario: “It Worked Yesterday”
A very common situation looks like this:
The issue is closed as a temporary problem.
A month later, refresh failures start happening twice a week. Then almost daily. What was once an occasional annoyance becomes an operational problem.
Data Source Variability
Many Power BI datasets depend on external systems that do not behave consistently.
Examples include:
Databases under heavy load at certain times
APIs with throttling or rate limits
Network latency that spikes unpredictably
When Power BI refresh runs into these conditions, it may fail even though nothing changed in the dataset itself.
Refresh Duration Slowly Exceeds Limits
As data grows, refresh time increases. This growth is gradual, which makes it easy to miss.
Eventually, refresh duration crosses internal or capacity-related thresholds. When this happens, refreshes may fail inconsistently depending on system load.
One day it finishes just in time. The next day it does not.
Resource Contention in Shared Capacity
In shared or heavily used capacities, refresh operations compete for memory and CPU.
Intermittent failures often appear when:
Multiple datasets refresh at the same time
Large reports are being actively used during refresh
Capacity resources are temporarily exhausted
Because capacity pressure fluctuates, refresh outcomes fluctuate as well.
Incremental Refresh Misconfigurations
Incremental refresh is often added to solve refresh performance issues. When configured incorrectly, it introduces new failure modes.
Common problems include:
These issues may only surface under certain data conditions, creating intermittent failures.
Credentials and Access That Expire Quietly
Refresh depends on valid credentials and permissions.
Some authentication methods:
When credentials are near expiration, refresh may fail sporadically before failing consistently.
Why Errors Look Random
Power BI refresh errors are often generic. The same message can appear for multiple root causes.
This makes failures feel random, even when they are not. Without context about data growth, capacity usage, and dependencies, teams struggle to connect the dots.
Advantages of Treating Intermittent Failures Seriously
When teams investigate early:
Root causes are easier to isolate
Failures are resolved before becoming frequent
Trust in reports remains intact
Operational effort stays low
Disadvantages of Ignoring Intermittent Failures
When intermittent failures are ignored:
Issues become regular incidents
Business users lose confidence in data freshness
Teams react instead of preventing
Small problems turn into reliability risks
Summary
Power BI refreshes fail intermittently not because of random glitches, but due to gradual changes in data size, resource usage, dependency behavior, and configuration limits. External system variability, growing refresh duration, shared capacity contention, incremental refresh issues, and expiring credentials all contribute to failures that appear unpredictable. Addressing these signals early prevents minor refresh problems from evolving into persistent reliability issues.