We compared BigTable and BigQuery in previous post. Big Table is a NoSQL database where the latency for data access is kept low even in the face of petabytes of data and millions of operations per second. Data is retrieved using scan operations. It is read and written under 10 milliseconds. The limits for best practice include 4KB per key for data keys, about 100 families per table, 16KB qualifier per column, 10MB per cell, and 100MB for all values in a row. BigTable is known to power Analytics, Maps and GMail.
BigQuery is a data warehouse that can store terabytes of data and allows queries to be written in SQL. It can power a wide variety of functionalities for an analytics dashboard. It supports relational database model as primary and  key-value store as secondary and with append-only tables. It can query large amounts of data for analysis in less time but requires more time to query small specific transactional data. Query execution time can be in the order of seconds.  Big query has two forms of costs - storage cost and query cost.
We did not talk about unstructured data. Generally such data is stored for GCP in Cloud Storage. Only if you need a mobile SDK, you would use  Firebase storage Similarly for structured data that is not relational and requires a Mobile SDK, you would use a Firebase Realtime DB from GCP.
Firebase is a comprehensive mobile development platform. It has all the monitoring needed for app analytics and performance. Mobile backend code can be run without managing servers as Cloud Functions and the app data can be stored and synced at global scale.