Dig into the details in each storage or data option. Learning the difference between BigQuery and Cloud Bigtable and which one is a managed service and which one is serverless is not something you want to be doing during the exam. BigQuery is an inexpensive datastore for tabular data. It's cost comparable with cloud storage, so it makes sense to ingest into BigQuery and leaves the data there. Ingesting the data depends on where it's coming from. Cloud logs, gap, can directly be ingested into BQ. From pub sub, you have an API. In the most general case, you can use Dataflow and write code to ingest data in batch or stream. You could also use open source tools like Spark or Hadoop to do the processing in which case you'd use Cloud Dataproc. The analysis itself is done by BigQuery. Results can be visualized in iPython notebook like in datalab or CoLab or in third-party tools. So, BigQuery's role is in both storage and in analysis. In other words, it's a data warehousing solution. Cloud Bigtable is not a relational database. It does not support SQL queries or joins nor does it support multi-row transactions. Also, it's not a good solution for small amounts of data like less than one terabyte. If you need full SQL support for an online transaction processing system, that's OLTP, consider Cloud SQL and Cloud Spanner. Cloud Spanner is particularly suited for databases larger than about two terabytes and databases that will be written to by global clients. If you need to store immutable blobs larger than 10 megabytes such as large integers or movies, consider cloud storage. If you need to store highly structured objects or if you require support for acid transactions and SQL-like queries, consider Cloud Datastore. If you need interactive querying in an online analytical processing or OLAP system, consider BigQuery. You should probably commit this table to memory and be able to use it backwards. For example, if the exam question contains data warehouse, you should be thinking BigQuery is a candidate. GoogleCloud Platform delivers various storage service offerings which remove much of the burden of building and managing storage and infrastructure. Like Google's other cloud services, storage services free you to focus on doing what you do best and differentiating at the application or a services layer. Google's storage offerings range across the spectrum. You can use different types of storage in the same project. Cloud SQL gives you fully managed MySQL so you have relational DB and a more traditional approach to queries, cloud Datastore provides a nearly infinitely scalable schema-less solution. If you want a disk, you can mount persistent disk as a block store that can be used by a Compute Engine or just pure data and blobs, use cloud storage when that will deliver what you need. Cloud Bigtable offers companies a fast fully managed infinitely scalable NoSQL database service, ideal for web mobile and IOT applications. BigQuery is recommended as a data warehouse. BigQuery is the default storage solution for tabular data. Use CloudSQL if you need transactions, and use Cloud Bigtable if you want low latency and high throughput. Firebase differs from Cloud Datastore in many significant ways. Firebase is a mobile platform that provides features beyond storage including authentication, notifications, and real-time synchronization of clients. Cloud Datastore is a NoSQL database. Cloud Bigtable can scale to massive amounts of data, Cloud Bigtable queries can be more sophisticated than Firebase queries. The top one is a scalable web app with mobile support. Cloud Functions drive a Cloud Bigtable back-end and the bottom one is a mobile client app. Mobile clients interact with Firebase, and App Engine provides back-end processing.