When working with large datasets in Databricks, it's crucial to follow best practices to avoid memory issues. First, optimize data partitioning to ensure that data is evenly distributed across workers. Use efficient data formats like Parquet for better compression and faster read/write operations. Leverage Spark’s caching and persisting capabilities selectively to store intermediate results in memory. Additionally, consider using Delta Lake for its ACID transaction support and incremental processing features, which can help manage large-scale data efficiently.