As a developer tools analyst, I've compared two prominent open-source time-series databases, QuestDB and TDengine, highlighting their momentum, community size, and apparent use cases for senior engineers. **Momentum and Community Size**: TDengine (taosdata/TDengine) currently exhibits stronger momentum, with 24,791 stars and a notable 78 stars gained in the last 30 days, indicating a more rapid recent adoption or interest surge. In contrast, QuestDB (questdb/questdb) has 16,789 stars, with a more modest 10 stars added in the same period, suggesting a slower but still present growth. **Apparent Use Cases**: While both are high-performance time-series databases, their focuses diverge. QuestDB is positioned as a general-purpose time-series database, suitable for a broad range of applications requiring high performance, such as financial analytics, monitoring, and logging. TDengine, with its emphasis on Industrial IoT (IIoT) scenarios, appears to be more tailored towards handling the specific demands of IoT data, including potentially higher ingestion rates and more complex sensor data management. This specialization might make TDengine more appealing for smart manufacturing, energy management, and similar IIoT-focused projects. QuestDB's versatility, on the other hand, could make it a preferred choice for organizations with diverse time-series data needs across different departments or applications. Both databases cater to senior engineers seeking scalable, high-performance solutions, but the choice between them may hinge on whether the project's primary focus aligns more closely with general time-series data challenges or the unique demands of IIoT.