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Combine datasets in Power Query: append vs merge explained

Learn the difference between append and merge in Power Query, when to use each, and how to combine marketing datasets reliably in Power BI.

Power Query: Prepare & Clean data

Marketing data rarely lives in a single dataset. Performance data may come from multiple exports, accounts, or platforms, while contextual information often lives elsewhere. Power Query offers two ways to combine datasets, but they serve very different purposes.

Understanding the difference between append and merge is essential to avoid broken models, duplicated rows, or misleading KPIs. This article explains both concepts clearly, shows when to use each one, and walks through how to apply them in Power Query.

Two ways to combine datasets in Power Query

A simple way to remember the difference:

  • Append = add rows
  • Merge = add columns

If you keep this mental model in mind, most decisions become straightforward.

Append queries: stack similar datasets

Appending datasets means placing one dataset on top of another. The structure stays the same, but the number of rows increases.

Typical marketing use cases

  • Monthly or weekly exports of the same report
  • Combining data from multiple ad accounts of the same platform
  • Merging historical data with recent data

Example: Meta Ads performance for January + Meta Ads performance for February.

This works only when datasets share the same columns and meaning.

How to append queries (quick steps)

  1. Open Transform data to access Power Query
  2. Go to Home > Append Queries
  3. Choose the tables you want to append
  4. Validate that columns align correctly
  5. Apply changes
Animated GIF demonstrating how to combine datasets using the Append function in Power Query.

Merge queries: enrich a dataset with additional information

Merging datasets means adding columns from one dataset into another, based on a shared key.

Typical marketing use cases

  • Adding campaign metadata to performance data
  • Joining CRM revenue to leads or conversions
  • Mapping IDs to readable labels (country codes, channel names)

Example: Meta Ads performance + a campaign mapping table.

One dataset acts as the base, and the second provides complementary data.

How to merge queries (quick steps)

  1. Open Transform data
  2. Select the main query
  3. Click Merge Queries
  4. Select the second table
  5. Choose the matching column (key) by clicking on them
  6. Select the join type (usually Left Outer)
  7. Expand the merged columns
Animated GIF demonstrating how to combine data using the Merge function in Power Query.

In this example, we merged Google Search Console data into a Google Analytics 4 dataset using the landing page path as the matching key. This adds Search Console metrics such as Impressions, Clicks, CTR, and Average Position as new columns in the GA4 table when the landing page matches.

Before merging, both datasets were cleaned and standardized so the key fields follow the same format, which helps prevent mismatches and refresh errors.

When to use append vs merge

Question Use Append Use Merge
What are you combining? Datasets with the same metrics and meaning Datasets with different but related information
Column structure Columns are identical or very similar Columns are different and complementary
What changes in the result? You get more rows You get more columns
Typical marketing use cases Monthly or weekly exports, multiple accounts of the same platform, historical + recent data Adding campaign metadata, joining CRM revenue, mapping IDs to labels
Key question to ask yourself Am I adding more data over time or sources? Am I enriching each row with more information?

If you’re unsure, ask yourself: am I adding more rows, or am I adding more information to each row?

Common mistakes

Some errors happen very frequently:

  • Appending datasets with different column structures
  • Merging datasets without a reliable key
  • Creating duplicate rows through incorrect merge logic
  • Using merge when append was the correct choice

These mistakes don’t always trigger errors, but they often lead to incorrect totals and misleading dashboards.

Conclusion

Append and merge are foundational Power Query concepts for marketing reporting. Append helps you scale data over time or across accounts, while merge helps you enrich datasets with additional context. Choosing the right method keeps your data model clean, your KPIs accurate, and your dashboards reliable. Once this distinction is clear, combining marketing datasets in Power BI becomes much more intuitive.

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