This content is a comprehensive crash course for the Google Associate Cloud Engineer certification exam, offering free training and 206 real exam questions to prepare individuals for managing and optimizing Google Cloud Platform (GCP) services.
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Hello and welcome to Tech with Shaping
Pixel. We are bringing you free
certification course. This time we are
bringing Google's associate cloud
engineer certification exam crash
course. We have also included 206 real
exam question answers with this crash course.
Are you ready to evaluate your cloud
skills? This learning path is designed
to prepare you for Google's associate
cloud engineer certification exam, but
it's also perfect for anyone looking to
enhance their cloud expertise. The exam
tests your knowledge in five key areas:
setting up a cloud environment, planning
and configuration solutions, deploying
and implementing those solutions,
maintaining operations, and securing
your environment with access policies.
We start with an overview of Google
Cloud Platform's main products and
services. You will learn how to set up a
development environment and install the
Google Cloud software development kit.
Next, dive into configuring networks and
creating virtual machines. Topics
include autoscaling, load balancing, and
network security. You will understand
concepts like network address
translation and configuring a cloud
virtual private network. Then explore
containers using Google Kubernetes
Engine, App Engine, and Cloud Run. Learn
to use identity and access management to
Cloud offers powerful compute options
from virtual machines to Kubernetes
engine. You can scale your application
seamlessly. These services ensure high
performance and flexibility for your workloads.
workloads.
Google Cloud provides robust storage
solutions whether you need object
storage, file storage or databases.
Their offering like cloud storage and
bigquery cater to diverse data needs efficiently.
efficiently.
Networking in cloud Google cloud is
designed for speed and security with
virtual private cloud cloud load
balancing and cloud CDN. You can
optimize traffic and ensure reliable
connectivity across the globe. Google
Cloud excels in AI and machine learning.
Tools like TensorFlow, AutoML, and AI
platform help you build, deploy, and
scale intelligent applications with
ease, leveraging Google's advanced AI capabilities.
capabilities.
Security and operations are paramount in
Google Cloud. Services like identity and
access management, cloud security,
command center, and operation suit
ensure your infrastructure is secure and
well managed. So stay connected, stay
updated as Google continuously innovates.
Google Cloud Platform or GCP is a public
cloud vendor offering a collection of
virtual non-demand services. It allows
anyone to build, host, and deliver
applications using the same hardware and
software that powers Google services
like search, Gmail, and Google Docs. One
of the main advantages of using GCP is
access to Google's global network and
vast experience in serving applications
to billions of users.
This means you can leverage their
infrastructure without the hustle and
cost of building and maintaining your
own data centers.
With GCP, resources are available on
demand wherever and whenever you need
them. This flexibility is complemented
by a wide range of services including
artificial intelligence, machine
learning, big data, internet of things,
healthcare, and gaming. The options are
almost endless. To make it easier to
navigate, GCP services are divided into
five main categories. compute, storage,
networking, artificial intelligence and
machine learning and security and
operations. In the coming up, we will
explore each category in more detail
giving you a comprehensive understanding
Google Cloud Platform offers a variety
of compute services to run your code.
Whether you need virtual machines,
containers, or serverless options, GCP
has you covered.
For virtual machines, Google's Compute
Engine is the go-to service. It supports
both Linux and Windows with predefined
and custom machine types. Compute Engine
is ideal for building from scratch or
migrating existing infrastructure.
Containers are lightweight alternatives
to virtual machines. Google Kubernetes
Engine RGke
simplifies deploying, maintaining, and
scaling containerized applications. It
includes features for logging,
monitoring, and health management.
For hybrid environments, Anthos allows
you to run containers across multiple
locations, including on premises, GCP,
and AWS. Anthos provides unified command
interface for seamless management.
App Engine is Google's platform as a
service offering. It lets you run web
and mobile applications without managing
the underlying infrastructure. Just
upload your code and GCP handles the
For single container applications, Cloud
Run offers a serverless solution. Upload
your container and cloud run deploys it
as a stateless autoscaling service.
Cloud functions are perfect for small
singlepurpose functions that responds to
events. They add extra functionality
without the complexity of larger
applications. Choose Compute Engine for
Windows applications, App Engine for
Java or Python apps, Cloud Run for
single containers, JKE for multiple
containers, Anthos for hybrid
environments, and Cloud Functions for
Let's explore the main storage services
available on Google Cloud Platform or GCP.
GCP.
Data can be structured like names and
dates or unstructured like music and
photos. Different types of data require
different storage solutions. For
unstructured data, Google Cloud Storage
is ideal. It's fast, secure, and has
almost unlimited capacity. Perfect for
web pages, log files, or data links.
Google Cloud Storage offers four classes.
classes.
Standard for frequently accessed data,
nearline for monthly access, code line
for quarterly access and archive for
annual access. All provide immediate
life file access. Cloud storage uses
object storage meaning no directories
just buckets. For traditional file
systems with block level access, use
file store which supports NFS
compatibility file shares. For
structured data, cloud SQL supports
MySQL, PostgresSQL, and Microsoft SQL
Server for higher performance. Use cloud
spanner for big data analytics. BigQuery
is the go-to solution. In summary,
choose cloud storage for files, file
store for editable files, cloud SQL for
standard databases, cloud spanner for
large workloads, and BigQuery for analytics.
Google Cloud Platform offers virtual
private clouds or VPCs which allow you
to organize and share resources. VPCs
are logically isolated networks that can
group or separate virtual machines and
containers. You can also divide a VPC
into sub networks and define traffic
rules. By default, all incoming traffic
to a VPC is blocked and all outgoing
traffic is allowed. You can create
firewall rules to override this
behavior, blocking outbound traffic or
allowing external access to a public web
server. To connect VPC to an external
network, you can use CloudVPN for secure
encrypted traffic over the internet. For
higher security and reliability, cloud
interconnect provides a direct dedicated
connection to Google. Direct peering is
another option coordinated
with your local internet service
provider. Load balancers distribute
network traffic among resources to
prevent any single part from being
overwhelmed. Cloud Armor works with load
balancers to defend against application
and denial of service attacks, ensuring
Cloud DNS manages millions of DNS
records for both public and private
domains. Cloud CDN accelerates web and
applications. Content delivery using
Google's globally distributed catches
enhancing performance and user
experience. These are the key networking
services on Google Cloud Platform
designed to keep your resources
Google Cloud Platform or GCP offers some
of the most exciting services in
artificial intelligence and machine
learning. Let's dive into what makes
these services stand out. Artificial
intelligence or AI aims to give machine
humanlike intelligence. Machine learning
or ML is a branch of AI focused on
systems that learn from data to improve
over time. On the side, GCP offers the
vision API to detect objects and faces
in images, the video intelligence API to
recognize actions in videos and document
AI to pass data in documents. Language
services include the translation API
which supports over 100 languages and
the natural language API which performs
sentiment analysis to classify messages
as positive, negative or neutral.
Conversation services feature text to to
speech and uh speechtoext APIs and
dialogue flow which generates realistic
dialogue for chat bots and voice bots
perfect for customer support. Structured
data services like recommendations, AI
suggest products based on past purchases
while cloud talent solutions helps match
JS job seekers with the right
opportunities for specific needs. GCP
offers AutoML to train custom models
without deep ML knowledge. For more
advanced requirements, Vortex AI
provides tool for building and deploying
Security services on Google Cloud
Platform or GCP help protect your data.
Privacy is crucial and security is the
method to achieve it. Policies pres
essential to safeguard your customers
and company often shaped by compliance
requirements. GCP offers several
security services. Security command
center provides a centralized control
panel for discovering vulnerabilities
and detecting threats. Secret manager
stores passwords, API keys, and
certificates securely. Data loss
prevention or DLP identifies and scrubs
sensitive data such as credit card
numbers from user records before
responding to database queries.
Operation services on GCP focuses on
monitoring and maintaining your
infrastructure. These tools ensure your
systems run smoothly and efficiently
providing insights and auto automation
to manage your resources effectively.
The Google cloud operation suit include
cloud logging for centralized log
management and cloud monitoring for
tracking metrics like CPU utilization
and network traffic.
Cloud debugger helps find software bugs
while cloud profiler and cloud trace
identify latency issues. Cloud
deployment manager automates resources
provisioning. Cloud build automates code
deployment. Age design secure APIs and
cloud composer manages workflows across
Let's do a quick recap on what we have
learned so far. So, Google Cloud
Platform offers powerful compute
services. Compute Engine manages virtual
machines while Kubernetes engine handles
containers. For hybrid environments,
Anthos is the go-to preferred managed
serverless options.
Use app engines for web apps, cloud run
for single containers, and cloud
functions for event-driven functions.
For storage, Google cloud has you
covered. Cloud storage is the perfect
for file storage. Needs SQL databases,
choose cloudsql, cloud spanner or
bigquery. For NoSQL databases, fire
store, firebase and big table are the
best options. Network is crucial. So,
private network clouds or VPC isolate or
connect your virtual machines. CloudVPN
interconnect and peering link your
company network to Google Cloud
Platform. Load balancer distributes
traffic and cloud armor protects against
Google Cloud excels in AI and machine
learning. Use AutoML to train custom
models. An AI hub for plug-in and play
components. services cover site,
language, conversation, and structured
data, making it easier to integrate AI
into your applications.
Security and operations are vital.
Security Manager stores passwords and
keys securely. Cloud Debugger inspects
running applications while cloud
profiler and cloud trays identify
latency issues. Age helps build
scalable, secure APIs. That's a wrap on
cloud Google cloud platform from compute
to AI. It offers comprehensive
solutions. Explore more courses on our
YouTube channel and enhance your cloud
skills. Thanks for watching.
So before you can start building on
Google Cloud Platform, you need a
project. Projects help organize all your
resources including users, APIs, and
billing information. To create a
project, you log into your Google Cloud
Console, click the project selector
drop-down, and then new project. Name
your project like photo blog and let
Google generate a unique project ID.
Once your project is created, you can
add resources for a photo blog. You
might add a compute engine instance for
WordPress, a cloud SQL database for
post, and a cloud storage bucket for
photos. Organizing resources into
projects prevents confusion and enhances
security. It ensures you won't mix up
production and development databases and
simplifies resources management.
Deleting a project removes all
associated resources. To delete P
project go to IM and admin then manage
resources. Select the project. Click
delete and confirm by entering the
project ID. Projects has marked for
deletion for 30 days allowing recovery
So managing user accounts and roles in
Google cloud platform is essential for
any organization. Different users have
different needs and is crucial to assign
the right permission to ensure smooth
operations. Each project in Google cloud
platform has its own set of users each
with unique permissions. Some users need
full access while others need only need
to view or make changes specific
changes. You can manually create user
accounts or automate the process using
tools like Google Cloud Direct Directory
Sync. This is especially useful for
large companies with hundreds of
thousands of users.
So permissions are managed by assigning
roles to users. Instead of assigning
individual permissions, you group them
into roles. These saves time and ensures
users have the necessary permissions to
perform their tasks. There are three
main types of roles. basic, predefined,
and custom. Basic roles offer broad
permissions. Predefined roles are more
specific, and custom roles allow you to
tailor permissions to your exact needs.
So, understanding and managing your
permissions is crucial. Start by
assigning minimal permission and adjust
as needed. These ensure security and
efficiency in your Google Cloud Platform projects.
To access certain Google Cloud
resources, you need to enable the
corresponding API. Google Cloud APIs
provide access to various services, but
many are disabled by default. Suppose
you want to interact with the users
Google Calendar. First, ensure you have
the correct project selected. Navigate
to APIs and services and select
dashboard. Click enable APIs and
services. Search for Google Calendar API
and click enable. Some APIs require
extra steps beyond enabling. For
instance, you might need to accept terms
of service or create credentials. Verify
the API is enabled by checking the
dashboard. When you no longer need an
API, you can disable it. Select the API
and click disable API at the top of the
page. Confirm it was disabled by
checking the dashboard again. Only
enable the APIs you need to avoid
unnecessary costs. Disabling unused APIs
ensures you don't accidentally use them,
helping you manage your resources efficiently.
Managing billing on Google Cloud
Platform is crucial. You start with a
$300 free trial for 90 days, but
eventually you will need to pay for services.
services.
To add a new billing account, click
billing in the navigation menu, then
manage billing accounts. Click create
account. Assign a name. Select a country
and submit. Now you have multiple
billing accounts. To link a project to a
new billing account, go to account
management. Select the project. Then
modify it to use the new billing
account. Confirm the changes. To disable
billing for a project, click the action
buttons next to the project. Select
disable billing and confirm. Remember,
you are still responsible for
outstanding charges. Creating a budget
helps monitor spending. For the billing
page, click budgets and alerts. Then
create budget. Set a name, scope, and
amount. Defin define uh notification
thresholds to receive alerts when
spending exceed certain percentages.
So to generate a billing export, select
billing export from build the billing
page. Choose to export all cost, confirm
the project and create a new bigquery
data set. These help in detailed analysis.
Let's review how to set up a Google
Cloud Platform environment. First,
create and delete projects to manage and
organize your Google Cloud Platform
accounts. Projects are essential for
structuring your resources. Next, create
user accounts and design permissions via
roles. Remember the three types, basic,
predefined, and custom. Basic roles like
viewer and editor are simple but not
secure. Predefined roles are more
granular. If none fit, create custom
roles. Avoid using basic roles in real environment.
environment.
Then enable APIs to access various
Google services like compute, storage,
and networking. Control project
resources by enabling or disabling the
associated API. Finally, add a billing
account to specify payment methods for
services. Projects can share billing
accounts or have a separate ones. Set
spending alerts by creating a budget and
analyze spending with billing exports.
That's a quick review of setting up your
The Google Cloud Software Development
Kit or Cloud SDK lets you manage your
Google Cloud Platform account through
terminal commands. It simplifies tasks
and automates repeatable process. To get
started, use CloudShell, an online
terminal in the Google Cloud Console. It
comes pre-installed with Cloud SDK. Just
login, click activate cloud shell and
you're ready to go. Cloud SDK includes
several key commands. G G-Cloud for
common cloud tasks. Gsil for Google
storage and BQ for BigQuery. These
commands streamline your workflow significantly.
significantly.
G-Cloud is the main utility for task
like installing components, spinning up
comput engine instances, and deploying
apps to App Engine. It's your go-to for
most cloud operations. GSUTIL
GSUTIL
or Google storage utility helps manage
cloud storage buckets and objects. It's
essential for handling your storage
needs efficiently.
BQ is used for interacting with
BigQuery. Run queries and manipulate
data sets effortlessly with this command.
Installing the Google Cloud SDK on your
machine can be more convenient than
using the integrated CloudShell. Let's
walk through the process of downloading,
installing, and setting up the SDK.
First, visit cloud.google.com/dk/doccks/install
for detailed instructions.
Download the installer for your
operating system, run it, and follow the
prompts. Choose the installation method
that best suits your needs.
After installation, verify it by running
G-Cloud help in your terminal. If you
see the help screen, you're good to go.
If not, revisit the installation
instructions to troubleshoot. Next, run
G-Cloud in it to authorize access to
your Google account. Follow the prompts,
login, and choose a default project.
If you are behind a proxy, configure
your proxy settings as needed. The SDK
comes with default components, but you
can install additional ones. Use G-Cloud
components list to see available
components. For example, install the
Google app engine Java component with
G-Cloud components. Install app/ engine/ java.
Google Compute Engine on Google Cloud
Platform lets you design and build
custom data centers in the cloud.
With Compute Engine, you have almost
complete control. Choose your hardware
operating system and install any
applications you need. It offers deep
customization and control. There are
various preconfigured machine types.
General purpose for website compute
optimized for performance tasks. Memory
optimized for large databases and
accelerator optimized for graphic
rendering and machine learning. Compute
engine can save you money.
You only pay for what you use and there
there are discounts for sustained and
committed use. Spot VMs offers
short-term savings for fall tolerant
workloads. Compute Engine is a versatile
and cost effective making it a great
step for moving to the cloud. Start by
Creating and managing virtual machines
with Google Compute Engine can seem
overwhelming. Let's focus on the main
features to get you started quickly and
efficiently. First, log into the Google
Cloud Platform console. Navigate to
Compute Engine by either using the
navigation menu or the search bar. You
will see a list of your current VMs. To
create a new VM instance, click on
create instance. You can create it from
scratch, a template, or a machine engine.
engine.
So, we will focus on creating one from
scratch. Fill out the form with details
like the instance name, region, and
zone. Choose your machine configuration,
including the type and amount of memory.
Adjust the boot disk and firewall
settings as needed. Once your VM is
running, Google provides a detailed logs
and monitoring tools. These help you
track metrics like CPU utilization and
network traffic. For advanced metrics,
install the ops agent. And that's it.
You have created and configured a VM
instance on Google Compute Engine. With
these basics, you are ready to explore
more advanced features and optimize your machine
machine
Now that you know how to create a basic
virtual machine, let's explore some
advanced options to enhance your next step.
step.
In the networking section, you can
customize your networking interfaces.
Choose the virtual private cloud or VPC
and a sub network for your instance.
These allows for better network
management and segmentation. You can set
your internal IP address to be dynamic
or static. Empiral
IPS change when an instance is stopped
or deleted. For a consistent IP, reserve
a static one. The same options apply to
external IP addresses, including the
option to have one for added security.
In the disk section, you can add and
attach additional disk drives. The
security section offers various options
to enhance your VM security. You can
also add your own secure shell or SSH
keys for custom access. Under
management, you can handle committed use
discounts and switch to spot instances.
Spot instances are cheaper but less
reliable, suitable for specific
workloads. They can save you money if
used correctly. Finally, you can create
a VM using a command line. Click to get
the exact command. paste it into your
terminal with the cloud software
development kit or SDK installed and
Managing a few virtual machines manually
is fine, but what if you need hundreds
of thousands? Let's explore how
templates and machine images can
simplify this process. Start by creating
a VM template. Click create instance
template and fill out the form with your
desired settings. This template saves
all your VM configuration making it easy
to replicate.
Once your template is ready, use it to
create new instances. Select your
template, make any necessary
arrangements, adjustments, and click
create. This method ensures consistency
across multiple VMs. A machine image
goes a step further. It captures the
entire state of a VM including installed
software. Create a machine image by
selecting an existing VM and clicking
create new machine image. To use a
machine image, create a new instance and
create select create from machine image.
These replicates the original VM
including all software and
configurations saving you time and
effort. To delete VM, select the
instances, click more actions and choose
delete. You can delete multiple
instances simultaneously. Streamlining
your management process.
Using templates and machine images makes
Before creating virtual machine
instances on Google Cloud Platform, it's
crucial to understand quotas. Kotas
prevent excessive resources consumption
and cap spending to avoid unexpected
bills. Let's explore how to view and
manage your quotas.
First, log into your GCP console. Search
for quotas and click on all quotas. This
page displays all quotas for your
project, including compute engine and
API gateway. You can filter to view
specific services.
To focus on a particular service, use
the filter option. For example, filter
by compute engine to see related
quoters. These help you monitor your
usage and identify if you're nearing any
limits. If you need to exceed a kota,
request a change, filter for your kota
like the number of virtual machine
instances per region. Select the kota to
change. Then click edit quotas. Fill out
the form specifying the new limit and justification.
justification.
After providing your contact details,
submit the request. Google will review
and respond which might take a day or
two. Now you know how to manage kas
Google Cloud Platform allows you to
build virtual data centers in the cloud.
One crucial component is the underlaying
network. Since you can't physically
access Google's hardware, connectivity
is essential for everything you build in
Google Cloud Platform. With many
customers using Google Cloud Platform,
isolation is key. Company A and Company
B, for example, need separate networks
to avoid conflicts like IP collisions.
Virtual private cloud solves these by
allowing complete isolation while
accessing the same resources.
VPCs let you create private virtual
networks broken down into subn networks
or subnets. You can keep things simple
with one VPC or get complex with
multiple VPCs and subnets.
You have the option to manually manage
settings or let Google handle them.
One standout feature of VPC is their
global nature. Servers across different
regions like the United States, United
Kingdom and China can communicate within
the same VPC.
Alternatively, you can isolate them by
creating a separate VPCs.
Google VPCs balance connectivity with
security. You can create open VPCs for
public servers or lock down VPCs for
private applications.
Using Google private access, you can
disable internet access while
maintaining connectivity to other Google
As your networking needs evolve, you
might need to modify your existing
virtual private cloud or VPC. Let's see
how. First, go to the VPC network page.
Ensure the VPC network option is
selected on the left. Click the network
name you wish to modify, then hit edit.
To edit roots, go to the roots tab,
select a region, then click on the
specific route to modify it. For new
roots, use root management.
In the firewalls tab, you can add,
delete, or edit firewall rules. Click
add firewall rule to create a new one or
select an existing rule to modify or
delete it. The subnets tab allows you to
add, edit, or delete subnets. Click on
the subnet name, then choose edit or
delete to make changes.
To expand a subnet IP range, click edit
and modify the CID range. Remember, the
new range must be a supererset of the
old one and cannot overlap with other
subnets. For example, changing a /16 to
a /15 doubles, the IBS must but must
follow CID rules.
Proper planning is crucial to avoid
issues. Expanding subnets can be done
without downtime, but always ensure your
addressing scheme accommodates future growth.
Google creates multiple copies of
everything. All data is duplicated and
stored in multiple locations. Servers
are replicated across multiple instances
by having redundant copies of
everything. Their services can survive
hardware failure, software glitches, and
even network outages without losing data
or functionality.
Having multiple copies isn't useful
unless you can access one of them.
Google spreads its copies across
different geographic regions. So even
when an entire country goes offline,
Google services are still accessible to
everyone else.
Google has failover mechanisms to
automatically handle most problems. When
a server or data center becomes
unavailable, traffic is instantaneously
redirected to another location. These
ensures uninterrupted service
because its services are distributed.
Google can easily adjust the number of
instances as required. When traffic
increases, servers are added to handle
the additional load. When traffic
decreases, servers are removed to lower costs.
costs.
Cloud load balancers provide a single
point of entry for resources.
They intelligently distribute request
based on server health, capacity, and
location. These ensures no part of your
infrastructure gets overwhelmed.
Optimizing performance and reducing latency.
Google Cloud Platform offers various
load balancers tailored for specific workloads.
workloads.
Let's break them down using four main attributes.
attributes.
Access type.
Load balances can be internal or
external. Internal load balancers use
private IP addresses accessible only
within Google Cloud. External load
balancers use public IP addresses
accessible from anywhere on the
internet. Choose based on whether your
service is internal or public facing.
Next scope load balances can be regional
or global.
Regional load balancers distribute
traffic across multiple jones ensuring
servicing service during jonal outages.
Global load balancers distribute traffic
across multiple regions providing
resonance against both jonal and
regional outages.
Third traffic type. Load balances handle
specific network traffic types. For web
servers, use HTTP or HTTPS load
balances. For V servers, use UDP load balances.
balances.
Understanding your traffic type is
crucial for selecting the right load
balancer. Finally, termination. Proxy
load balancers terminate client
connections allowing advanced
configurations like single SSL certificates.
certificates.
pass through load balances forward
packets directly preserving client IP information.
information.
By understanding these four attributes,
you can select the right Google Cloud
Platform load balancers for any application.
To troubleshoot load balancers, start
with logging. Logs provide detailed
insights into issues. Remember, logging
is not enabled by default. Enable it
during creation or by editing the load
balancer under backend configuration.
Set a sample rate to control log volume.
Use logs explorer to filter and view
specific log entries like http load
balancer. Next, monitoring. Accessing.
Access the monitoring dashboard from the
load balancer page. Click on the load
balancer. Then the monitoring tab for a
high level overview. For detailed
insights, go to the cloud monitoring
page and select Google Cloud load
balances. These dashboards track errors,
latency, and more, helping you identify
patterns and issues.
Finally, create a custom dashboards. On
the cloud monitoring page, click create
dashboard. Add charts and graphs to
track specific metrics like load
balancer backend utilization. Customize
these dashboards to suit your needs and
set up alerts to notify you when metrics
exceed thresholds. This combination of
logging and monitoring ensures
Ever wondered what a VPN is? Let's break
it down. A VPN, a virtual private
network, creates a secure connection
between private networks using the
public internet. A VPN acts like a
single seamless network allowing
resources in different networks to
connect as if they were in the same
network. It keeps your networks private
and secure through encryption.
Modern VPNs use encryption to securely
exchange data over the public internet.
These means your private networks remain
inaccessible to the general public,
ensuring your data stays safe.
VPNs can make your device appear to be
in a different country by changing your
IP addresses. They also allow remote
employees to securely access corporate
networks and connect multiple data
centers across vast instances.
VPNs are cost effective and easy to set
up but rely on internet connectivity.
Slow or unstable internet can affect VPN
performance. While encryption adds
security, older methods can be
vulnerable. For highly sensitive data,
VPNs might not be secure enough. VPNs
offer a practical solution for secure
private connections. However, for higher
reliability and security, consider
alternatives like cloud interconnect or
CloudVPN is a Google Cloud Platform
service that links an external network
with your virtual private cloud. Many
companies have resources spread across
on premises, Google Cloud and other
public clouds. CloudVPN ensures seamless
communication between these environments.
environments.
ClassicVPN was the original offering. It
uses a single interface and external IP
address. While it supports static
routing, a tunnel failure disrupts activities.
activities.
Classic VPN is mostly depra decapricated
as of March 31, 2022, but it's still
useful for older gateways without border
gateway protocol support.
High availability VPN introduced in 2019
offers multiple interfaces and IPs.
If one tunnel fails, another takes over
and shows 99.99%
uptime. This makes it more reliable than
classic VPN. However, it only supports
dynamic routing, not static.
Google encourages migration to high
availability VPN due to its reliability.
Classic VPN is only recommended if your
external network uses an old gateway
that doesn't support border gateway
protocol. For everyone else, high
availability VPN is the better choice.
CloudVPN supports only the IPAC
protocol, making it incompatible with
SSLR wire guard. It's designed for side
to side connections nor client to
gateway or remote access scenarios.
Despite these limitations, it's
excellent for hybrid and multicloud environments.
Google Cloud DNS is a global domain name
service that simplifies domain
management. It allows you to map address
public IP addresses to public domain
names and create private domain names.
One standout feature is the ability to
use internal DNS names instead of hard
coding IP addresses. These flexibility
is invaluable for managing complex
infrastructure, making testing and
deployment much easier across multiple
environments like development, testing,
staging, and production.
With Google Cloud DNS, you don't need to
maintain your own DNS servers or software.
software.
The service is fully managed and highly
scalable, capable of handling millions
of records. It offers 100% availability
and low latency access from anywhere in
the world. Additionally, you can
generate detailed logs for monitoring
and troubleshooting.
Google Cloud DNS ensures high
scalability and reliability. It can
manage millions of records effortlessly,
providing low latency and 100%
availability globally. This makes it a
roadest choice for any business.
If you already have a Google Cloud
Platform account, using Google Cloud DNS
is no barrier. It streamlines domain
management and enhances your
infrastructure flexibility and reliability.
Before diving into cloud DNS, it's
crucial to understand DNS zones.
A DNS zone is a container for DNS
records sharing the same name suffix.
These zones automatically generate
essential records like NS and S SOA.
Cloud DNS offers two types of jones
public and private. Public jones are
visible to the entire internet while
private jones are restricted to
specified virtual private cloud or VPC networks.
networks.
Sometimes you may need both public and
private jones for the same domain. This
setup known as split horizont
allows different results for the same
domain name based on the source IP address.
address.
To set up split DNS, create both the
public and private jone for the same
domain and add the appropriate records.
This way internet users get the public
IP while internal
resources get the private IP.
Public DNS Jones can enforce DNS
security extension or DNS SSC.
DN SSEC authenticates responses for
domain name lookups, ensuring data
integrity and preventing redirection to
harmful servers. DNS forwarding allows
requests for certain domains to be
resolved by another DNS server. DNS
pairing on the other hand forwards
request between VPCs enabling internal
to internal routting. Understanding
these concepts is key to effectively
managing cloud DNS. Whether it's public,
private or split jones, each has its
To fully utilize cloud DNS,
understanding DNS policies is crucial.
These policies allow you to override
default settings, enabling both simple
and advanced configurations.
On premises DNS resolution. Handling all
DNS resolutions on premises involves
adding a cloud DNS policy specifying an
alternative name server.
These bypasses cloud DNS but increases
latency and risks disruptions if the
on-prim connection fails. Using cloud
DNS for all DNS resolution requires a
policy for inbound query forwarding.
This setup shares name resolution
services across networks but also
suffers from higher latency and
potential disruptions if the GCP
connection fails.
A hybrid DNS environment combines both
on-prim and cloud DNS. Set up a
forwarding zone for on-prim resources
and enable inbound DNS forwarding for
GCP access. These approach balances
flexibility and reliability.
While onprim and cloud only strategies
have drawbacks, a hybrid DNS environment
offers the best of both worlds. It's a
recommended practice by Google for
optimal performance and minimal headache.
Securing cloud resources can be challenging.
challenging.
As your systems grow and become more
complex, the total number of potential
vulnerabilities increases.
Minimizing your attack surface is
crucial to maintaining security.
Physically securing a room with a single
door is much easier than securing an
entire building with many entrances.
Similarly, securing a private network is
easier than a public one. Assigning
public IPs only when absolutely
necessary. Cutting of VMs and Kubernetes
clusters from the internet can limit
updates and patches.
Network address translation or NAT
allows you to assign a single IP address
to a group of computers. These enables
internal network request to reach the
internet while blocking incoming requests.
requests.
Setting up NAT in Google Cloud Platform
is straightforward with Google CloudNet.
It works with both compute engine VMs
and cloud Kubernetes engine. Fully
managed, it requires no maintenance of
NAT gateways and is highly scalable and reliable.
reliable.
Google CloudNat offers flexibility with
manual and auto modes. In auto mode,
CloudNat manages everything for you.
Even if a zone goes down, CloudNat
remains available across the region,
ensuring your VM stays secure and upto-date.
CloudNet allows your private virtual
machines to access the internet without
exposing them to public IP addresses.
Let's walk through setting it up. First,
ensure you have two virtual machines, a
public VM with both internal and
external IP addresses and a private VM
with only an internal IP address. Both
should be in the same virtual private
cloud or VPC but different subnets.
Verify the public VM internet access
using the curl method. It should return
your public IP address and load
websites. For the private VM, the curl
command will time out indicating no
internet access.
Navigate to cloudnat and click get
started. Name your gateway. Select your
VPC network and region and create a new
router. The default settings will map
all subnets to the NAT gateway and
assign NAT IP addresses automatically.
Click create. Once the gateway is
created, test the private VMs internet
access again using curl.
It should now connect successfully
showing the NAD public IP address.
Explore advanced configurations like
setting manual IP addresses, enabling
logging and adjusting connection
settings. These options provide greater
control and customization for your
Google Kubernetes Engine, GKE, is a
fully managed Kubernetes service. No
need to manually install Kubernetes.
It's all set up and ready to go,
simplifying your operations.
GKE handles most system management
tasks, offering advanced features like
automatic node scaling, repairing, and
upgrading. This means you can focus on
your applications while GKE takes care
of the heavy lifting.
Kubernetes is an open-source container
orchestration system ideal for running
many containerized applications. It's
especially useful for complex micro
service architectures requiring hundreds
of thousands of containers.
Kubernetes uses a distributed system
deploying containers onto virtual
machines called nodes grouped into
clusters. This setup ensures high
availability and easy scaling as nodes
can take over if one fails.
A Kubernetes cluster has two main
components. The control plane and worker
nodes. The control plane orchestrates
tasks and manages node health while
worker node run the containerized
application. In summary, JKE simplifies
Kubernetes management allowing you to
create cluster with control plane and a
worker node. This setup ensures
efficient, scalable and reliable
Kubernetes is designed to run
containerized applications. While you
might think of containers, Kubernetes
focuses on parts and workloads. Let's
break down what these terms mean. In
Kubernetes, a pod is the closest thing
to a container. A pod can contain one or
more containers bundled together sharing
storage and network resources.
Most pods have a single container making
pod and a container almost
interchangeable. However, multiple
containers in a pod can work together seamlessly.
seamlessly.
You don't directly create pods in
Kubernetes. Instead, you define
workloads which then create the pods.
The workload represents an application
and sets deployment rules for pods.
These includes how many parts to deploy
these hardware requirements and how they
should run workloads ensures flexibility
and scalability in your application.
Initially you will likely create
workloads with a single pod in a
container but Kubernetes allows you to
run pods on multiple nodes providing
redundancy and scalability.
These abstraction helps Kubernetes
manage complex applications efficiently.
When working with Kubernetes, remember
you are deploying workloads. These
workload consist of pods which contain
containers. This structure offers the
flexibility and scalability needed for
So, let's explore how to create and
manage Google Kubernetes Engine clusters.
First, log into the Google Cloud Console
and navigate to the Kubernetes Engine
page. Click on the create button to
start a new cluster. Choose a standard
cluster for more control over configurations.
configurations.
Name your cluster and configure the
worker node, selecting machine types and
the number of nodes.
Node pools allow you to have different
types of nodes within the same cluster.
You can create multiple node pools to
support various workloads. For example,
one pool can have generalpurpose
machines while another can have compute
optimized machines.
These flexibility helps in managing
diverse workloads efficiently.
Google communities engine supports
autoscaling to handle varying workloads.
Vertical pod autoscaling adjusts CPU and
memory resources for your pods.
Horizontal pod autoscaling changes the
number of pods based on resource consumption.
consumption.
Node autoscaling adjusts the number of
nodes in your cluster, ensuring
efficient resource use and cost management.
To troubleshoot your Google Kubernetes
Engine, start by checking the logs. This
is the first step to identify and fixing
any issues with Kubernetes.
Log into the web console and navigate to
the Kubernetes engine page. Click on the
cluster name and then the logs tab. Here
you can filter by severity and search
for the specific test strings.
For more detailed information, click on
the log logs explorer link. This tool
provides an expanded set of records and
filters, allowing you to drill down into
the details and locate error messages.
Monitoring helps identify issues that
aren't immediately obvious on the
clusters page. Click the operations
button. These pop-up includes logs,
metrics, events, and alerts. For more
details, use the cloud monitoring
dashboard. Set up alerts by clicking on
alerting. Choose the metrics to monitor
and set thresholds for notifications.
For example, get notified if memory
usage spikes or if a pod generates many errors.
errors.
By combining logs, monitoring and
alerts, you can effectively troubleshoot
Developing microservices on Kubernetes
involves more than just building and
deploying containers. Effective
communication between containers is crucial.
crucial.
An unreachable
API is useless and failures can cascade
through dependent services.
Name spaces in Kubernetes help organize
your containers. Think of them like
folders in a file system but not
hierarchical. Every pod in a Kubernetes
cluster is assigned to a namespace with
default being the fallback if none is specified.
specified.
In smaller environments, the default
name space might suffice, but in longer
setups, managing hundreds of thousands
of containers in one name space can be chotic.
chotic.
Name spaces help avoid conflicts and
accidental deletions by dividing the
cluster into virtual clusters for
different teams.
Name spaces do not provide isolation.
Containers in different name spaces can
still communicate.
Kubernetes clusters like those in Google
Cities engine come with predefined name
spaces like default cube-
system cube public and cube node
release. It's best to leave the cube
dash name spaces alone. To demonstrate, create a j cluster and a couple of name
create a j cluster and a couple of name spaces. deploy a simple app to each
spaces. deploy a simple app to each namespace.
namespace. This shows how name spaces prevent
This shows how name spaces prevent naming conflicts and help manage
naming conflicts and help manage resources efficiently.
resources efficiently. You can also manage namespaces via the
You can also manage namespaces via the Google cloud console. Name spaces are
Google cloud console. Name spaces are essential for organizing and managing
essential for organizing and managing microservices on Kubernetes, especially
microservices on Kubernetes, especially in large environments.
you have a containerized web server running in a Kubernetes cluster. Each
running in a Kubernetes cluster. Each pod gets an IP address, but these IPs
pod gets an IP address, but these IPs change as parts are created and
change as parts are created and destroyed. These makes direct traffic
destroyed. These makes direct traffic rooting unreliable.
rooting unreliable. Kubernetes services solve this problem
Kubernetes services solve this problem by defining a set of pods and setting a
by defining a set of pods and setting a policy to access them.
policy to access them. Think of services as internal load
Think of services as internal load balances mapping a single IP address to
balances mapping a single IP address to a group of parts ensuring reliable
a group of parts ensuring reliable connectivity.
connectivity. There are five types of Kubernetes
There are five types of Kubernetes services. Cluster IP, headless, node
