From the course: Data Science on Google Cloud Platform: Designing Data Warehouses

Google Cloud Storage

From the course: Data Science on Google Cloud Platform: Designing Data Warehouses

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Google Cloud Storage

- [Narrator] Google Cloud Storage is an object store available on GCP. It is similar to S3 on AWS and to some extent HDFS in Hadoop. Data is stored as raw files in whichever format the file is uploaded. Cloud storage supports buckets which have a global link space and hence can be accessed across GCP given that relevant permissions are provided. You can also create a public URL for a bucket or file, and that can be used to access this resource from an external or third party system. Cloud storage supports different storage classes with different pricing structures and usage, so you can choose what matches the use case. Multiple options are available to load data into cloud storage using the Web UI, the cloud shell, REST API and Language SDK's. This provides for flexibility in using cloud storage in either GCP exclusive or Hybrid environments. So, what are the pros and cons of cloud storage? The advantages of cloud storage are, it can support exabytes of data, it has high availability, it supports edge caching within GCP so you can access content quickly from anywhere in the world. It has multiple storage options and you can pick the one that most suits your use case. The shortcomings of cloud storage are, it does not support in place updates to data, you have to delete and insert for any changes needed, it has no schema support, it can quickly get expensive if you choose the wrong storage option. So then, how do people typically use cloud storage? Well, raw dumps of data acquired from sources is a very good use case. It is also ideal for media storage like audio and video files, and it can be used as a backup data store for enterprise data. So, cloud storage is fairly versatile and a scalable solution, however, it lacks schema support. So be sure that your use case calls for cloud storage.

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