Power Query: Fast Approximate Match Tips

Key insights

• This is a summary of a YouTube video by Excel Off The Grid about Approximate Match techniques in Power Query.
  It compares two approaches and tests which one runs faster.
• The video shows two methods: Table-based transformation and Row-by-row transformation.
  Each method is demonstrated step by step so you can reproduce the results.
• Core workflow: load your tables, use Merge Queries with fuzzy matching, set a similarity threshold, and expand matched rows.
  This returns similar but not exact matches and handles typos or naming differences.
• Key benefits: you can return multiple matches, match on multiple criteria, and tune matching strictness with the threshold.
  These features make Power Query better than simple lookups for messy data.
• Matching details: Power Query scores matches using measures like Jaccard similarity and a common default threshold is about 0.8.
  Lower the threshold to find more loose matches; raise it to require closer matches.
• Performance tip: the video times both approaches and finds a clear winner for speed.
  To maximize performance, keep queries simple, reduce intermediate steps, and expand matched rows only when needed.

Overview of the video

The YouTube video by Excel Off The Grid titled "Approximate Match in Power Query – The FASTEST Way Revealed!" explores practical ways to perform flexible lookups when data values do not match exactly. The presenter outlines two concrete approaches and then runs a timed comparison to determine which performs best in real-world scenarios. Importantly, the video also provides a downloadable example file to help viewers follow along and reproduce the results.

The piece aims to help analysts who face common issues like typos, name variations, or partial matches when joining tables. Moreover, it situates the problem within Power Query workflows, where users can decide between built-in fuzzy merges and custom transformations. This makes the content relevant for both beginners and experienced users who want to optimize speed and reliability.

Method 1: Table-based transformation

In the first approach, called Table-based transformation, the author creates a structured dataset that precomputes the relationships needed for the match. This method leans on set-based operations that transform and join entire tables at once instead of handling one row at a time. Because Power Query and the engine behind it are optimized for table operations, this approach often reduces the number of applied steps and leverages vectorized processing.

As a result, the table-based method can be more complex to design upfront, since you need to think about grouping, sorting, and pre-joining logic. However, once implemented, it tends to scale better for larger datasets because the query engine processes batches of rows together. The video walks through the exact steps, showing how to shape the lookup table to support approximate matches before expanding the joined results.

Method 2: Row-by-row transformation

The second approach, known as Row-by-row transformation, evaluates matches individually for each record, often using custom functions that test similarity between values. This method is conceptually simpler because you implement the matching logic in a way that reads like procedural code: take a row, compare it to candidates, pick the best match, and return the result. Many analysts like this style because it is easier to reason about when debugging or when the matching rules are highly bespoke.

Nevertheless, the author notes that row-by-row processing can suffer from performance penalties when datasets grow, since it prevents some of Power Query’s internal optimizations from taking effect. Additionally, maintaining and testing custom functions can become time-consuming if the logic grows complex or if matching criteria change often. The video demonstrates how to construct these functions and highlights where the slower behavior typically appears.

Performance comparison and results

After implementing both methods, Excel Off The Grid runs timed tests to determine which approach finishes fastest under the same conditions. Contrary to some expectations, the results are not always straightforward; while table-based methods often win on larger datasets, specific dataset shapes and matching rules can change the outcome. The presenter times each approach objectively and shows the raw differences so viewers can judge impact for their own files.

The video also points out that Power Query’s native Merge Queries with fuzzy matching remains a strong option because it encapsulates similarity logic and supports options like the similarity threshold. Yet the tests reveal situations where carefully designed table transformations can outperform fuzzy merges, especially when the join keys and candidate sets are tightly bounded. Thus, performance depends on both algorithm choice and how well you limit work inside the query.

Tradeoffs and implementation challenges

Balancing speed, maintainability, and accuracy involves tradeoffs that the video discusses candidly. For example, using a high similarity threshold improves precision but risks missing legitimate matches, whereas lowering the threshold returns more candidate rows and increases processing time. Similarly, table-based approaches favor speed at scale but require more upfront design and testing, while row-by-row approaches offer clarity but can become bottlenecks.

The author also explores practical hurdles such as memory limits, refresh time in shared environments, and how Power Query’s internal behavior can change results across versions. These challenges mean that no single method fits every case; instead, the best choice depends on dataset size, the acceptable error rate, and how frequently the matching rules will change. The video encourages testing on representative data and iterating on thresholds and pre-filtering rules.

Practical takeaways and conclusion

In conclusion, the video by Excel Off The Grid offers a usable roadmap: start by profiling your data, then build a lightweight proof-of-concept for both methods and time them under realistic conditions. Additionally, consider combining techniques—use pre-filtering and table transforms to reduce candidate sets and then apply fuzzy logic for final selection—to get the best mix of speed and accuracy. This hybrid path often balances the tradeoffs discussed earlier.

Ultimately, the lesson is pragmatic: measure, compare, and choose the approach that fits your operational constraints and accuracy needs. The walkthrough and example file in the video make it easier for analysts to reproduce the tests and adapt the solutions to their own work. Therefore, anyone wrestling with non-exact joins in Power Query will likely find actionable guidance and a clear basis for choosing the fastest method for their situation.

Keywords

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