ALEXIS MOUSSINE-POUCHKINE: Can you
get a sense of Google Cloud Platform data processing
and data storage products in just a few minutes?
Let's find out.
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It all starts with the ability to store objects or files.
Google Cloud Storage is a fully-managed object storage
service.
It offers different storage classes for multi-regional,
for optimal and user latency, to coldline storage
for lower frequency access.
There is a single unified API to interact with cloud storage.
This makes, for instance, moving objects
across storage classes completely seamless.
Cloud storage offers strong consistency,
and is designed for 11 lines of durability.
Storing data is great, but processing it
is probably even more useful.
In the big data category, a GCP "hello world"
would be to throw an SQL query at a large set of data, hoping
to find useful insights.
Let's do just that with BigQuery.
You could, of course, upload and work with your own data set,
but here we will use sample data from a public data set instead.
What's important here is that at no point
do you need to provision a cluster or storage capacity.
Simply craft a standard SQL query,
and within seconds, BigQuery will
have processed potentially gigantic amounts of data
and produced a result that can be saved in various formats.
For instance, using the public data set of GitHub
commits, here we've asked BigQuery
to provide the list of the top Google employees
fixing stuff on GitHub.
Also with the same data set, we can figure out
which programming languages make developers the most happy.
Notice the regular expression embedded in the query.
The key point here is how much data is processed
in such a short period of time.
You can import or stream data to BigQuery using an API,
but also export just about any data produced
by other GCP products and services,
such as logs to BigQuery.
If you have existing Apache Hadoop or Spark workloads,
you will likely be interested in trying out
Cloud Dataproc, GCP's fully-managed environment that
will spin up a cluster in less than 90 seconds.
Let's talk about relational databases, or RDBMSes SQL
as a language still has unsurpassed qualities,
and RDBMSes, such as MySQL and PostgreSQL are very popular.
This is why Google Cloud SQL is a managed
service that supports both of these relational databases.
This is a great solution for storing
your existing or new application data.
Simply choose between MySQL and PostgreSQL.
Select a machine size with up to hundreds of gigabytes of RAM
and terabytes of storage.
Select a zone, and cloud SQL will take care
of providing the following--
encryption at rest and in transit,
a private IP address, data replication
between multiple zones with automatic failover,
automated backups, as well as point-in-time recovery.
Staying with relational databases,
Google also offers Cloud Spanner,
a horizontally scalable, strongly consistent
relational database as a service.
Make sure to check out this truly innovative technology
that defies the CAP theorem and that many Google services are
built on.
In the NoSQL department, Cloud Firestore
is a highly scalable, strongly consistent database
offering two modes--
the native mode offering real time updates
and offline support for mobile developers,
and the datastore mode for backend developers
looking for a schema-less documents database.
Cloud Bigtable will complement your storage options
with the petabyte scale fully managed,
no escrow database service.
This will be mostly suited for large analytical and
operational workloads as it comes with high throughput
characteristics such as consistent sub-10 millisecond
latency.
In any case, you should visit cloudatgoogle.com/storageoptions
for a decision tree to understand which is the best
database for you.
GCP has numerous other data-related products,
some particularly innovative.
Be sure to consider the following.
Pub/Sub for producing and consuming messages
globally across all GCP zones and regions.
Filestore for applications that require a system interface.
Cloud Memorystore for Redis provides
a fully managed, in-memory data storage service
that is ideal to build application
caches with sub-millisecond data access.
Cloud Dataflow to build and execute
unified batch and streaming pipelines
using the Apache Beam programming model.
Dataprep to make sure your data scientists
are not spending 90% of their time cleaning up data
instead of processing it.
And Data Studio, for interactive dashboards and engaging
reports.
Consider taking free codelabs linked in the description below
to explore any of these GCP products.
Check out the compute overview and machine learning episodes,
and look forward to an upcoming video on DevOps and tooling.
If you like this video, please like, subscribe, comment,
and share.
See you soon for another GCP Essentials video.
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