Combining public datasets with other data sources can be challenging due to differences in data format and gaps in data coverage. However, using techniques such as date normalization and gap filling can help overcome these issues. Date normalization involves reformatting dates to a consistent format, while gap filling uses SQL functions or programming languages like awk to replace missing values with the last observed value for that sensor, by date. By using a PostgreSQL time-series database like TimescaleDB, which excels in gap filling and time_bucket_gapfill functionality, users can efficiently combine analytics and metrics data with other relational data sources to gain insights into their business or world.