High-cardinality data refers to datasets with a large number of unique values, particularly in time-series databases. This can be a challenge for databases like InfluxDB, which has limitations in handling high-cardinality data due to its reliance on hashmaps and log-structured merge trees. In contrast, TimescaleDB uses B-trees to index data, allowing it to scale to high cardinalities with better performance. The authors of TimescaleDB share their experience with high-cardinality datasets and compare the performance of InfluxDB and TimescaleDB in handling such workloads, demonstrating that TimescaleDB outperforms InfluxDB by over 11x at high cardinalities.