From the course: Data Science on Google Cloud Platform: Designing Data Warehouses
GCP storage options
From the course: Data Science on Google Cloud Platform: Designing Data Warehouses
GCP storage options
- [Instructor] Google Cloud Platform provides a number of data storage options. It is important to choose the right option, based on the requirements for data addition, updates, and querying. The first option we have is Cloud Storage. It is an object storage database similar to Amazon S3. Data is organized as buckets and files and can be accessed by a global URL. The next option is Cloud SQL. It is nothing but a managed version of MySQL, or PostgreSQL. It serves us a great option for storing small- or medium-sized relational data. Also, we have Cloud Spanner. Cloud Spanner is a GCP native RDBMS product that provides data consistency like RDBMS, while having horizontal scalability like NoSQL. Cloud Spanner can be expensive though, so tread carefully. Then comes Cloud BigTable, which is a columnar database like Cassandra and Hbase. It supports a Hbase interface for compatibility. Next, there is Cloud DataStore, which is a document database like MongoDB or ElasticSearch. Data can be stored in flexible JSON formats. We also have Cloud MemoryStore, which is a key value store like Redis. And finally, there is Cloud BigQuery, which is a data warehouse providing a SQL interface. This is just a high-level overview of the options. Let's dig in and explore them further.
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