join content from cells in a column without losing content from the corresponding columns.

The challenge of consolidating duplicate information while preserving unique data points is one of the most common pain points for anyone working with large datasets in spreadsheets. A recent inquiry from a Google Sheets user managing approximately 170,000 rows illustrates this struggle perfectly: they need to join content from cells in a column without losing corresponding information from other columns, all controlled by a case number identifier. This is a classic data transformation problem that many spreadsheet users encounter, yet the solution landscape remains surprisingly fragmented. Users often find themselves oscillating between workarounds like manual copy-pasting, complex nested formulas, or even contemplating switching to entirely different software—asking whether Microsoft Excel might offer a more suitable approach. The reality is that both platforms offer viable solutions, but the key lies in understanding which functions and techniques are best suited for large-scale data consolidation tasks.

What makes this particular challenge interesting is the scale factor. When working with tens or even hundreds of thousands of rows, traditional approaches that might work fine on smaller datasets can become prohibitively slow or simply fail to execute. Many users discover that formulas which work perfectly on a few hundred rows begin to timeout or crash when applied to datasets of this magnitude. This is where understanding the difference between formula-based approaches and script-based solutions becomes crucial for productivity. Related discussions in the community frequently touch on these scaling challenges, with users sharing experiences about Slow spreadsheet - need troubleshooting and exploring Resources to help you get better at laying out Excel spreadsheets as they seek more efficient workflows.

The underlying question this user is really asking—whether to invest in Excel—deserves a thoughtful response because it speaks to a broader decision many spreadsheet power users face. While Excel does offer certain advantages in handling very large datasets and has some more robust pivot table and Power Query capabilities, the core challenge they're facing can be addressed in Google Sheets using approaches like QUERY functions combined with array formulas, or Google Apps Script for truly massive datasets. The decision should ultimately hinge on specific workflow requirements rather than assuming one platform will automatically solve the problem. In many cases, learning the proper techniques for data consolidation in the platform they already use will deliver faster results than migrating to a new tool and rebuilding workflows from scratch.

Looking at the broader landscape, this type of data consolidation challenge represents a growing pain point as more users work with datasets that have outgrown simple spreadsheet workflows. The emergence of AI-native spreadsheet tools suggests a future where these transformations can happen more automatically, with the system intelligently recognizing patterns and suggesting or applying appropriate consolidations without requiring users to become formula experts. For now, users would benefit from exploring built-in functions like TEXTJOIN combined with FILTER, or for the most demanding scenarios, lightweight scripts that can process data in batches. The key is recognizing that spreadsheet tools are incredibly capable when used correctly—the challenge is often knowing which approach to apply to which problem, and that's where investing time in learning these techniques pays dividends across all future data work.