Database Reference
In-Depth Information
ecosystem. It is likely that in the coming months and years there will be
additional entries, so just because a tool or service isn't mentioned here
doesn't mean that it doesn't exist. Chapter 2, “BigQuery Fundamentals,”
goes into more detail about the individual components, but this is a quick
survey of the offerings. You can divide the cloud offerings into three
portions: processing, storage, and analytics.
Cloud Processing
The cloud processing components enable you to run arbitrary computations
over your data:
Google Compute Engine (GCE) : The base of Google's Cloud
Platform, GCE is infrastructure-as-a-service, plain and simple. If you
have software you just want to run in the cloud on a Linux virtual
machine, GCE enables you to do so. GCE also can do live migration of
your service so that when the datacenter it is running is turned down for
maintenance, your service won't notice a hiccup.
AppEngine : AppEngine is a higher-level service than GCE. You don't
need to worry about OS images or networking configurations. You just
write the code you actually want running in your service and deploy it;
AppEngine handles the rest.
Cloud Storage
These cloud storage components enable you to store your own data in
Google's cloud:
Google Cloud Storage (GCS) : GCS enables you to store arbitrary
data in the cloud. It has two APIs: one that is compatible with
Amazon.com 's S3 and another REST API that is similar to other Google
APIs.
DataStore : A NoSQL key-value store. DataStore is usually used from
AppEngine, but its REST API enables you to store and look up data
from anywhere.
BigQuery (Storage API) : BigQuery enables you to store structured
rows and columns of data. You can ingest data directly through the
REST API, or you can import data from GCS.
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