File Store....

Question 1

File Store

Answer 1

is a managed file storage service for applications that require a file system interface and a shared file system for data.
managed network attached storage with either Compute Engine or Google Kubernetes Engine instances
Applications gain the benefit of features such as scale-out performance, hundreds of terabytes of capacity,
and file locking without the need to install or maintain any specialized plug-ins or client-side software.

Question 2

Filestore use cases.

Answer 2

• expedite migration of enterprise applications.
• For media rendering, you can easily meant filestore file shares on Compute Engine instances, enabling visual effects artists to collaborate on the same file share
• Electronic Design Automation, or EDA, is all about data management.
• Data analytics workloads include Compute complex financial models or analysis of environmental data.
• Genome sequencing
• web content management

Question 3

Cloud SQL

Answer 3

is a fully managed service of either MySQL, PostgreSQL, or Microsoft SQL Server databases.
patches and updates are automatically applied, … but you still have to administer MySQL users with the native authentication tools that come with these databases.

Question 4

Why would you use a Google Cloud service for SQL, when you can install a SQL Server application image on a VM using Compute Engine?

Answer 4

• failover
• automated and on-demand backups with point-in-time recovery.
  (import and export databases using mysqldump, or import and export CSV files.)
• scale up, which does require a machine restart or scale out using read replicas.
  if you are concerned about horizontal scalability, you’ll want to consider Cloud Spanner

Question 5

which clients Cloud SQL supports

Answer 5

Cloud Shell, App Engine and Google Workspace scripts…SQL Workbench, Toad…

Question 6

Cloud SQL performance and scalability

Answer 6

Cloud SQL delivers high performance and scalability with up to 64 TB of storage capacity, 60,000 IOPS, and 624 GB of RAM per instance.
You can easily scale up to 96 processor cores and scale out with read replicas.
Currently, you can use Cloud SQL with either MySQL 5.6, 5.7, or 8.0, PostgreSQL 9.6, 10, 11, 12, 13, or 14, or either of the Web, Express, Standard or Enterprise SQL Server 2017 or 2019 editions.

Question 7

Cloud Sql high availability and failover

Answer 7

within a regional instance, the configuration is made up of a primary instance and a standby instance.
In the event of an instance or zone failure, the persistent disk is attached to the standby instance, and it becomes the new primary instance. Users are then rerouted to the new primary.
This process is called a failover.

Question 8

Performance-based recommendation for connection type to your Cloud SQL

Answer 8

If you’re connecting an application that is
collocated in the same region as Cloud SQL (??within the same network as your Cloud SQL instance??within same project), choosing the Private IP connection will provide you with the most performant and secure connection using private connectivity.
In other words, traffic is never exposed to the public internet.

Question 9

If the application is hosted in another region or project, or if you are trying
to connect to your Cloud SQL instance from outside of Google Cloud, you have 3 options:

Answer 9

Recommendation is using the Cloud SQL Auth Proxy, which handles authentication, encryption, and key rotation for you.
If you need manual control over the SSL connection, you can generate and periodically rotate the certificates yourself.
Otherwise, you can use an unencrypted connection by authorizing a specific IP address to connect to your SQL server over its external IP address.

Question 10

Cloud Spaner

Answer 10

is a service built for the cloud specifically to combine the benefits of relational database structure with non relational horizontal scale.
This service can provide petabytes of capacity and offers transactional consistency at global scale schemas, SQL and automatic synchronous replication for high availability.

Question 11

Cloud Spaner use cases

Answer 11

financial applications and inventory applications traditionally served by relational database technology
transactions and inventory management in the financial services and retail industries

Question 11

Cloud Spaner characteristics

Answer 11

Schema, sql, consistency, availability, scalability, replication

Question 12

architecture of cloud spanner

Answer 12

cloud spanner instance replicates data in end cloud zones which can be within one region or across several regions, database placement is configurable.
Replication of data will be synchronized across zones using Google’s global fiber network. Using atomic clocks ensures adamiscity whenever you are updating your data.

Question 13

Firestore

Answer 13

highly scalable NoSQL database
fast, fully managed, serverless, cloud native, NoSQL, document database that simplifies storing, synking and querying data for your mobile web and IOT apps at global scale.
for serverless apps.
supports ACID transactions
with automatic multi region replication and strong consistency

Question 14

Firestore is next generation of Datastore and can operate in two modes:

Answer 14

Datastore mode:
-compatible with datastore applications
-strong consistency
-no entity group limits
Native mode (for mobile and web apps):
-Strongly consistent storage layer
-Collection and document data model
-Real time updates
-mobile and web client libraries

A general guideline is to use Cloud Firestore in Datastore mode for new server projects and native mode for new mobile and web apps.

Question 15

Difference between firestore and bigtable

Answer 15

BigTable scales up well Firestore scales down well
BigTable has low maintenance overhead scaling up to terabytes
if you don’t require transactional consistency, you might want to consider Cloud Bigtable.depending on the cost or size.

Question 16

Cloud BigTable

Answer 16

fully managed NoSQL database with petabyte-scale and very low latency but
no transactional consistency,

It seamlessly scales for throughput and it learns to adjust to specific access patterns.

Question 17

Cloud BigTable usage

Answer 17

a great choice for both operational and analytical applications, including IoT, user
analytics, and financial data analysis, because it supports high read and write throughput at low latency.
It’s also a great storage engine for machine learning applications.
Cloud Bigtable integrates easily with popular big data tools like Hadoop, Cloud Dataflow, and Cloud Dataproc.

Question 18

BigTable achitecture

Answer 18

Cloud Bigtable stores data in massively scalable tables, each of which is a sorted key/value map.
The table is composed of rows, each of which typically describes a single entity, and columns, which contain individual values for each row.
Each row is indexed by a single row key, and columns that are related to one another are typically grouped together into a column family.
Each column is identified by a combination of the column family and a column qualifier, which is a unique name within the column family.
Each row/column intersection can contain multiple cells, or versions, at different timestamps, providing a record of how the stored data has been altered over time.

Question 19

Cloud Bigtable tables are sparse;

Answer 19

if a cell does not contain any data, it does not take up any space.

Question 20

More BigTable architecture

Answer 20

A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries.
Tablets are stored on Colossus, which is Google’s file system, in SSTable format.
An SSTable provides a persistent, ordered immutable map from keys to values, where both keys and values are arbitrary byte strings.

Question 21

BigTable summary usage

Answer 21

if you need to store more than 1 TB of structured data, have very high volume of writes, need read/write latency of
less than 10 milliseconds along with strong consistency, or need a storage service that is compatible with the HBase API, consider using Cloud Bigtable.
the smallest Cloud Bigtable cluster you can create has three nodes and can handle 30,000 operations per second.
you pay for those nodes while they are operational, whether your application is using them or not.

Question 22

MemoryStore

Answer 22

Memorystore for Redis provides a fully managed in-memory data store service built on scalable, secure and highly available infrastructure managed by Google.

Memorystore also automates complex tasks like enabling high availability, failover, patching, and monitoring.

High availability instances are replicated across two zones and provide a 99.9% availability SLA.
You can easily achieve the sub-millisecond latency and throughput your applications need.

Memorystore can support instances up to 300 GB and network throughput of 12 gigabits per second.

Because Memorystore for Redis is fully compatible with the Redis Protocol, you can lift and shift
your applications from open source Redis