Reza Rad (RADACAD) [MVP] published a YouTube video that demonstrates an easy way to merge tables using Power BI and Power Query. The presentation walks viewers through the practical steps of combining data from relational sources and highlights the built-in tools that simplify the process. As a result, the video is aimed at analysts and BI authors who need to prepare unified datasets for reporting and visualization.
Furthermore, the video emphasizes how the Power Query workflow supports a no-code experience while preserving each transformation step. This makes it easier to refresh and audit data flows over time. Consequently, the approach fits teams that want traceable and repeatable data preparation without writing complex code.
Reza demonstrates the merge process primarily through the Power Query Editor, showing how users load primary and secondary tables, then select Merge Queries or Merge Queries as New. After selecting matching columns, the merge dialog lets users pick a join type and then expand the merged column to bring desired fields into the main query. This visual flow reduces manual effort and helps users see intermediate results as they shape data.
He also points out the role of Relationship columns when data comes from relational databases; these columns can be expanded to join related tables without complicated SQL. Consequently, the editor preserves the transformation steps and supports refreshes, which is useful for repeating the process on updated data. This step-by-step visibility improves maintainability and reduces surprises during production refreshes.
The video outlines the six join kinds available in Power Query and explains when to use each one. For instance, a Left Outer join keeps all rows from the first table and only matches from the second, while an Inner join keeps only rows present in both tables. These choices affect result set completeness and must be aligned with your analysis goals.
Moreover, Reza discusses tradeoffs: using broad joins like Full Outer can preserve data but increases row counts and processing time, whereas filtered joins such as Left Anti isolate unmatched rows but may hide relationships you later need. Therefore, selecting a join type requires balancing clarity, model size, and performance. Teams should plan for these tradeoffs to avoid downstream issues like duplication, incorrect aggregations, or inflated memory use.
Reza offers practical tips such as choosing to merge into an existing query when you want incremental changes, or using Merge Queries as New when you want a separate dataset. He also explains the difference between using the Transform tab to change columns in place and the Add Column tab to create new derived fields while keeping originals. These choices influence both model clarity and the ability to roll back or audit transformations.
The video does not shy away from challenges: mismatched data types, inconsistent keys, and high-cardinality joins can break query folding and slow refreshes. Reza emphasizes testing merges on representative samples, validating key uniqueness, and monitoring performance when working with large data volumes. Thus, careful planning and incremental validation help reduce unexpected compute or memory bottlenecks.
According to the video, Power BI’s merge experience has improved in recent releases with more responsive UI behavior and clearer data model layouts. These updates make it easier to visualize relationships and manage complex models. As a result, authors can organize merged tables and their relationships more effectively, which helps when multiple users collaborate on a single model.
Additionally, community content and updated tutorials through 2024 and 2025 have clarified best practices for merging and model design. Nevertheless, teams still face the practical limits of dataset size, refresh windows, and the need to balance between in-model merges versus pushing logic back to source systems. These strategic decisions affect governance, maintainability, and performance over time.
Overall, Reza Rad’s video shows that merging tables in Power BI via Power Query is both accessible and powerful for common BI tasks, especially for users who prefer visual, no-code tools. The method preserves data lineage and offers flexible join options, but it also requires users to consider data quality, key design, and performance tradeoffs. Consequently, analysts should pair the GUI workflow with testing and governance to avoid surprises in production.
In summary, the video provides a concise, practical guide that helps teams choose between different merge strategies while highlighting common pitfalls. Therefore, practitioners will find value in following the step-by-step demonstration and adapting the recommendations to their environment and scale.
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