Patterns Posts

Error Handling Patterns in Power Query

The Importance of Error Handling

In production environments, data transformations must be resilient to unexpected inputs and conditions. Proper error handling ensures your workflows continue to function even when problems arise.

Pattern: try-otherwise Expressions

The try-otherwise pattern allows you to attempt a transformation and fall back to an alternative if it fails. This is particularly useful for type conversions and operations that might fail with certain inputs.

Pattern: Error Replacement

Sometimes you want to replace error values with meaningful defaults. Learn how to identify and replace errors while preserving the structure of your data.

Pattern: Error Logging

For complex workflows, it's important to track and log errors for later analysis. Discover patterns for capturing error information without disrupting the overall transformation process.

Pattern: Data Validation

Proactive validation can prevent errors before they occur. Learn how to implement validation checks that identify potential issues early in your transformation pipeline.
Read time:10 min
errorhandlingpatterns
Last updated: Jun 17, 2025

Incremental Refresh Patterns

Why Incremental Refresh Matters

As your datasets grow, full refreshes become increasingly expensive in terms of time and resources. Incremental refresh patterns allow you to update only what has changed since the last refresh.

Pattern: Date-Based Incremental Refresh

For time-series data, you can implement incremental refresh based on date ranges. This pattern is particularly useful for transaction data that accumulates over time.

Pattern: Change Detection with Hash Values

When data can be updated rather than just appended, you need a way to detect changes. Learn how to use hash values to efficiently identify and process only the records that have changed.

Implementation Steps

  • Define parameters for refresh ranges
  • Create filter conditions based on these parameters
  • Implement change detection logic
  • Set up appropriate partitioning
  • Configure refresh policies in Power BI

Best Practices and Considerations

Effective incremental refresh requires careful planning. Consider data archiving strategies, handling of late-arriving data, and monitoring refresh performance to ensure optimal results.
Read time:12 min
incrementalrefreshpatterns
Last updated: Jun 17, 2025

Data Reshaping Patterns for Analysis

The Importance of Data Structure

The structure of your data significantly impacts your ability to analyze it effectively. Power Query provides powerful tools for reshaping data to meet your analytical needs.

Pattern: Unpivoting Data

Converting wide data to long format is a common requirement for analysis. Learn how to effectively use the Unpivot transformation to normalize your data structure.

Pattern: Pivoting Data

Sometimes you need to transform long data into a wide format for reporting. Discover how to use the Pivot transformation to create cross-tabular views of your data.

Pattern: Grouping and Aggregating

Summarizing data by categories is fundamental to data analysis. Learn effective patterns for grouping and aggregating data in Power Query.
Read time:10 min
reshapingpivotunpivotanalysis
Last updated: Jun 17, 2025

Common Data Cleaning Patterns in Power Query

Why Data Cleaning Matters

Clean data is essential for accurate analysis. Power Query provides powerful tools for identifying and fixing data quality issues.

Pattern 1: Removing Duplicates

Duplicate data can skew analysis results. Learn how to identify and remove duplicates in Power Query.

Pattern 2: Handling Missing Values

Missing values can cause issues in analysis. Discover strategies for detecting and handling nulls and empty values.
Read time:6 min
data cleaningpatternsquality
Last updated: Jun 17, 2025