Cloud Digital Leader Study Cram | Google Certified CDL | Pass Exam

0:01 Hello everyone, welcome to Tech with Shaping Pixel. Today in this video we are going to cover cloud digital leader studygram. If you haven't subscribed to our channel, please subscribe to Deco Shaping Pixel. Let's start our video. Cloud computing. What is cloud computing? It is the ondemand availability of computing resources as services over the internet. It eliminates the need for individuals and businesses to self-manage physical resources themselves and only pay for what they

0:50 use. Types of cloud computing deployment models: public cloud, private cloud, and hybrid cloud. Types of cloud computing services. Platform as a service, software as a service, and infrastructure as a service. Use cases infrastructure scaling, disaster recovery and data storage. Primary benefits of cloud

1:37 computing. Faster time to market. Scalability and flexibility. Cost savings. Better collaboration. Advanced security and data loss prevention. Types of cloud computing services. What is public cloud? In this kind of cloud, the whole infrastructure is situated at the cloud vendor, also known as the business that sells its services to the general public. In the case of the public cloud, we just need to go to the website of the relevant cloud provider and build or administer the resources. Others can use the resources

2:22 that we previously utilized once we destroy them. Examples Microsoft Azure, Amazon Web Services. What is private cloud? This type of cloud is similar to a public cloud, but the difference is that the infrastructure and requirements are reserved for a single organization only. The resources are isolated and can't be used by other organizations. The cloud can be located on premises as well. Examples: Sure stack, Amazon private cloud. What is hybrid cloud? A hybrid

3:08 cloud solution is a solution that features an element of different types of IT deployment models ranging from onremises to private cloud and public cloud. It combines both public and private clouds. This is much more complex than public or private clouds. In this type of cloud, the public cloud is used for non-critical tasks and the private cloud is used to carry out critical tasks. Public cloud versus private cloud versus hybrid cloud.

4:01 Cloud computing models infrastructure as a service. It's a service that provides compute, networking, and storage services or resources on demand within the virtual environment. The cloud vendor manages the infrastructure and the service but the user itself manages the operating system middleware runtime etc. Examples: Digital Ocean, AWS, Microsoft Azure, Azure VMs, Google Cloud Platform. Users are system administrators. Platform as a service,

4:47 it is a service that allows you to manage and develop applications without worrying about client's underlying infrastructure. In this service, the cloud vendor manages infrastructure, operating systems, middleware, etc. Examples of platform as a service, Heroku, Google App Engine, Azure Web Apps and Open Shift. Users are developers. Software as a service. It is a model where the software is centrally hosted and accessed by the user through a web browser using the internet. With this service, you don't

5:33 need to download or install any software on your personal computers. The cloud vendor manages everything. You simply need to concentrate on how you can get the most out of it. Examples of software as a service are Office 365, Google Docs, Google Sheets, Dropbox, etc. And the users are and customers. Cloud technology versus onremises.

6:20 Google Cloud Platform Google Cloud overview. It is a Google cloud computing service that offers a variety of infrastructure and services for hosting, managing, and deploying applications and data in the cloud. Businesses and developers use it to use Google's infrastructure and tools for a variety of computing needs. Services offered by Google Cloud are categorized into compute, data transfer, internet of things, management tools, big data, cloud, AI,

7:10 identity and security, networking, storage and database Four is digital transmission with GCP. Digital transmission with GCP refers to the secure and efficient digital data transfer using Google Cloud Platforms networking and data transfer services. Benefits of digital transformation. Modernize infrastructure. Manage data. Gain insight. Break down team silos, solve business problems and realize cost

8:02 savings. Business transformation in Google cloud. Cloud transformation. The process of migrating work to the cloud including data, apps and software programs is known as cloud transformation. Seven difficulties in adopting new technology. Business plan. Limited resources. Skill challenges. Technology challenges. The disruptive impact of new technology. End user resistance and ownership issues.

8:47 Infrastructure modernization. Migrating applications to the cloud helps you avoid expensive refresh cycles. Utilize our suit of migration solutions to carry out your migration after understanding the cost benefit of shifting to the cloud. Utilizing discovery and evaluation tools. Benefits of infrastructure modernization. Improved app performance leads to increased user and customer satisfaction. Maintain app security by encrypting data both in motion and at rest. Save money with no upfront costs. Improve density and right sizing

9:38 advice. Data democratization. Data democratization is the process of making data and data related tools and resources available to a larger audience within an organization to allow more people to make datadriven decisions and perform data analysis. Benefits of cloud technology in business digital transformation. Scalability. Compute Engine provides autoscaling to automatically add or remove VM instances from a managed instance group based on increases or decreases in load. Autoscaling allows your apps to

10:24 handle traffic growth gracefully and it reduces cost when resource requirements are low. You can autoscale MIG based on its CPU usage, cloud monitoring metrics, schedules or load balancing serving capacity. Flexibility. Cloud computing gives your business more flexibility. You can quickly scale resources and storage to meet business demands without investing in physical infrastructure. Companies don't have to pay for or build the infrastructure needed to support their highest load levels. Likewise, resources deplete quickly if they are not

11:13 used. Agility. Moving to the cloud gives organizations of all shapes and sizes the ability to move faster, be more agile, and innovate their businesses. Security cloud computing strengthens your security posture due to the depth and breadth of security features, automated management and centralized management, cost effective and savings. Whichever cloud service model you choose, you only pay for the resources you actually use. This helps you avoid overbuilding and overprovisioning your data center and gives your IT teams back

12:00 valuable time to focus on more strategic work. Shared responsibility model. With the shared responsibility model, responsibilities like physical infrastructure, security and software updates get shared between the cloud provider and the consumer. In traditional infrastructure, the company is responsible for maintaining the physical space and security, maintaining servers and infrastructure and replacing servers. The IT department is responsible for maintaining all the infrastructure and storage required to keep the data center up and running and

12:47 keeping all systems patched to the correct version. In shared responsibility model, the cloud provider is responsible for physical infrastructure like security, power, cooling and network. The consumer is responsible for data and information stored in the cloud and managing security access. Fundamental cloud concepts. Scalability. The ability to increase or decrease IT resources as needed to meet changing demand is referred to as cloud scalability in cloud

13:33 computing. Flexibility. Users are able to adjust services to their precise needs, personalize applications, and use cloud services from any location given an internet connection. Reliability. The probability that a system will meet certain performance standards and produce the desired output for a specified period is referred to as reliability. Elasticity. It is also known as the automatic scaling of resources. It can dynamically scale resources. for example, recovering from failures and continuing to function. Agility. Cloud agility refers

14:21 to the ability to rapidly develop, test, and deploy cloud-based applications. Capital expenditure versus operational expenditure. When comparing IT infrastructure models, there are two types of expenses to consider. Capital expenditure and operational expenditure. Capital expenditure is defined as funds used by organizations to obtain, upgrade, and maintain physical assets such as data centers. Example, building a data center, buying a company vehicle, and repaving the parking lot.

15:07 Operational expenditure is defined as funds used by organizations for their day-to-day operations. Example, renting a building, leasing a company vehicle, or signing up for cloud services. Capital expenditure versus operational expenditure. The purpose of capital expenditure is invested funds with a longerterm advantage to the organization. Purpose of operational expenditure is ongoing expenses to run the day-to-day business. The way of purchasing capital expenditure is one-time purchase and the way of purchasing

15:52 operational expenditure is pay as you go approach. Accounting treatment in capital expenditure can't be fully deducted in the accounting period. They are depreciated or amotized over time. But for operational expenditure, it's fully deducted in the accounting period. Examples of capital expenditure purchasing office buildings, equipment, vehicles and intellectual property assets. Examples of operational expenditure consumables, wages, rent, maintenance and repair of machinery. Cloud computing falls under operational expenditure because it

16:39 operates on a consumption based model. Total cost of ownership and return on investment. Total cost of ownership. Cloud TCO refers to the total costs associated with your new cloud technology which is often compared to the total costs of your former server or data center deployment. The consumer's total cost of ownership of IT services changes while shifting to cloud computing. Consumers aim to reduce and optimize the total cost of ownership expenditure and increase value to business by leveraging new cloud

17:24 services. Transitioning to new cloud services commonly increases in transition and then falls as incremental opex model is adopted. Return on investment. Return on investment is perhaps the most widely used measure of financial success in business. Return on investment is the proportionate increase on the value of an investment over a period. Return on investment is calculated by taking the actual or estimated income from a project by subtracting the actual or estimated costs. Network infrastructure

18:10 terminology. IP address. A unique numerical identifier assigned to each device on a network used to identify and communicate with other devices. Bandwidth. Bandwidth in networking refers to the maximum data transfer rate of a network or internet connection. Throughput is the amount of data successfully transferred over a network. Region. A region is a particular geographic area where your resources can be hosted. Three or more zones make up a region. Within a region, a zone is a deployment location for Google Cloud

18:57 resources. Zones within a region should be regarded as a single failure domain. Network edge location. A network edge is where a computer network interfaces with the internet. A network connected to the internet may consist of one or more network edges in data centers around the world. Google Cloud provides connections to Google Cloud services from more than 100 locations in the following metropolitan areas. locations Africa, Europe, Asia-Pacific, Oceanania, North and South

19:43 America. Internet service provider. An internet service provider is a company that provides access to the internet. ISPs can provide this access through multiple means including dialup DSL cable wireless and fiber optic connections. DNS domain name system a system that translate domain names into IP addresses allowing devices to locate and connect to websites and other network resources. SubC cable. Subc cable refers to any electrical cable laid on the seabed. Through the term is frequently used to

20:28 refer to cables laid on the bottom of large bodies of fresh water. Network latency. Network latency is the lag in communication across networks. It displays the amount of time it takes for data to transfer across the network. Networks with a longer delay or lag have high latency while those with quick response times have low latency. Fiber optics. Fiber optics also known as optical fiber is a technology that transmits data as light pulses along a glass or plastic fiber.

21:14 Exploring data transformation with Google Cloud. Basics of data engineering and cloud computing. A data engineer designs and builds systems that collect and transform the data used to inform business decisions. Google Cloud Platform offers services for getting value from data. Compute Services, Compute Engine, Kubernetes Engine, App Engine, and Cloud Functions, Storage Services, Big Table, Cloud Storage, CloudSQL, Cloud Spano, and Cloud Data

22:04 Store, Big Data, BigQuery, Pub or Sub Data Flow, Data Brock, and data lab, machine learning services, natural language API, vision API, machine learning, speech API and translate API. Role of data and digital transformation. Digital transformation drives businesses and industries forward. Organizations of all sizes from startups to global enterprises are opting for digital transformation not only to make scaled improvements but

22:51 also to drive significant change and fully embrace the digital age. This requires a strong commitment from both businesses and IT teams as well as a willingness to support the resulting changes. The journey to digital transformation incorporates flexible technology to build, deploy and manage applications more quickly in the cloud. Experimentation to embrace new ideas and gain new insights on customer and market demands. Measurement of experimentation results with data analysis to guide decision making. Collaboration across organizational

23:37 boundaries with tools for quick knowledge sharing, customer focus and data analysis to deliver greater value to customers and agility to scale and accelerate without hesitation to succeed in the digital age. Identify common Google Cloud solutions for data management. Types of data, structured data and unstructured data. Structured data. Structured data is organized and formatted in a specific way, typically by a well- definfined schema or model. It is simple to query,

24:26 examine, and modify. Examples in GCP include Google CloudSQL, BigQuery, and Cloud Data Bre. Unstructured data. Unstructured data is more difficult to work with when using conventional database techniques because it lacks a defined format or structure. It can consist of text, pictures, audio, video, and other types of data. Examples in GCP include Google Cloud Storage, Google Cloud Vision, Google Cloud Natural Language, Google Cloud Video

25:12 Intelligence, Google Cloud Speechtoext and texttospech, BigQuery ML and AI platform. Business use cases for databases, data warehouses and data links. What is a database? A database is set of organized data that is simple to access and administer. Examples of database services in GCP are cloudSQL cloud spano, BigQuery, Cloud Bigtable and Fire Store. What is a data warehouse? A data

26:00 warehouse is an important component since it can contain both recent and historical data in one place and was designed to offer a long-term perspective of data across time for business intelligence. Examples BigQuery, data proc and data flow. Use cases of data warehouse. Multiple sources of disparate data. Big data analysis and visualization both as synchronously and in real time. Data mining, machine learning models and other AIdriven

26:46 processes, streaming analytics, custom report generation and ad hoc analysis. What is a data leak? Massive amounts of organized, semistructured and unstructured data can be stored, processed and secured centrally in a data lake. It can store data in its native format and process any type of data regardless of size. Some data lake use cases, media and entertainment, telecommunications and financial services. Benefits of storing data in

27:34 cloud. Data security, scalability, cost and resource savings, archives and backup data, accessibility and redundancy, ability to collaborate on documents and files, compliance with legislation and regulations for storing data. Basics of GCP data products. Data analytics. Bitquery. Looker. Data proc. Data flow. Data form. Pub or sub cloud data

28:20 fusion. Data catalog. Cloud Composer, Data Prep, Data Plex, Analytics Hub, Luca Studio, Earth Engine, Big Lake and Google Marketing Platform, Databases, Alloy DB for Postgres SQL, Bare Metal Solution, Cloud for Bigtable, Cloud Spanner, CloudSQL, Database Migration Service, Fire Store, Memory

29:05 Store, Data Stream, and Firebase Realtime Database. Lucer. Lucer is a cloud-based platform that allows businesses to analyze and visualize their data, making it easier to make datadriven decisions. Benefits: Improve decision making with smart data access. Insights where you work at your speed. Enterprise class business intelligence. BigQuery. Bitquery is a fully managed enterprise data warehouse that helps you

29:52 manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. Benefits of BigQuery. High availability and zero redundancy on a fully managed platform that does not require downtime for upgrades for very large data sets. Low storage costs and industry-leading performance are combined. You can query data from the Azure AWS and Google cloud platforms using BigQuery only. In addition to using artificial intelligence to automatically optimize

30:37 storage, it excels at analyzing massive data volumes. Cloud Spanner. Cloud Spanner is a highly scalable database that combines unlimited scalability with relational semantics such as secondary indexes, strong consistency, schemas, and SQL. All while providing 99.999% availability in a single simple solution. As a result, it can handle relational and non-reational workloads. Benefits of cloud spano. Fully managed

31:23 security. Highly available. Strong consistency. Payer use. Standard tools and languages. Horizontal scaling for relational data. CloudSQL. CloudSQL is a fully managed relational database service offered by Google Cloud Platform. It allows you to easily set up, maintain, manage, and administer relational databases in the cloud. Benefits: Backups, High availability and failover, network connectivity, export and import, maintenance and updates.

32:11 monitoring and logging. Google Cloud Storage. Google Cloud Storage refers to a service that provides scalable and highly available storage infrastructure for storing and retrieving data in the cloud. Benefits: Elasticity, flexibility, total cost of ownership, security, sustainability and redundancy, Google Cloud Storage options, object storage, cloud

32:57 storage, block storage, persistent disk, local SSD and hyperdisk cloud-based storage services for your businesses all running on Google Cloud infrastructure. file storage, file store, parallel store and Google cloud net tab volumes. Archival storage cloud storage. Choosing storage options. Storage class. Standard storage. Nearline

33:44 storage. Cloudline storage and archive storage. Innovating with Google Cloud artificial intelligence. Identify Google Cloud solutions for machine learning and artificial intelligence. Artificial intelligence. It is the capability of a computer system to mimic human cognitive functions such as learning and problem solving. Through artificial intelligence, a computer system simulates human decision making by using math and logic to learn from fresh

34:34 knowledge. Machine learning, it is an application of AI. It is the use of mathematical models and data to assist a computer in learning without direct instruction. This allows a computer system to learn and improve on its own over time. Types of machine learning. Supervised learning. Supervised learning is a machine learning model that uses label training data to map a specific feature to a label. Unsupervised learning. Unsupervised learning is a machine learning model that uses unlabelled data to learn

35:20 patterns. Reinforcement learning. Reinforcement learning is a machine learning model that learns by doing through a series of trial and error experiments. Google Cloud differences with regard to artificial intelligence and machine learning. Machine learning options on GCP. Google Cloud has a robust portfolio of artificial intelligence, machine learning or deep learning capabilities. The main focus area for this exam for services are vision API AI

36:06 platform, Tensor and TensorFlow, Tensor Processing Unit. There are other services as well. Cloud Natural Language, Cloud Speech API, Cloud Translation API, Cloud Video Intel, AI infrastructure, artificial intelligence versus machine learning goals. AI system goals focus on having a machine complete a complex human task efficiently. Such tasks may involve learning, problem solving, and pattern recognition. ML goals are to have a machine analyze large volumes of data.

36:52 The machine will use statistical models to identify patterns in the data and produce a result. The result has an associated probability of correctness or degree of confidence. Google Cloud Vision API. The Google Cloud Vision API allows developers to easily integrate vision detection features within applications, including image labeling, face and landmark detection, optical character recognition, and tagging of explicit content. Detect objects and faces. Read printed and handwritten text. and build valuable metadata into your image

37:39 catalog meant for ML teams with expertise. AI platform or Vertex AI, formerly ML engine, then rebranded to AI platform, now rebranded as Vert.ex AI. Support for all opensource frameworks. Build, deploy, and scale ML models faster with pre-trained and custom tooling within a unified AI platform. Bring your own code and submit training jobs using supported ML frameworks such as TensorFlow, Scikitlearn, XG Boost, Keras,

38:26 etc. Pre-trained models. A model that has independently learned predictive relationships from training data often using machine learning. Pre-trained modeling provides solution for classification and categorization that is not easily done with classical programming. Google Cloud's pre-trained APIs are a great option if you don't have your own training data. AutoML. It enables developers to train highquality models specific to their business needs. AutoML natural language. AutoML tables, AutoML video

39:14 intelligence and AutoML vision are now available in the new unified Vortex AI. Bring your data set and GCP automatically picks the best model for you. meant for developers with limited ML knowledge. Tensor or TensorFlow. Tensor is a term and set of techniques known in machine learning in the training and operation of deep learning models can be described in terms of tensors. TensorFlow is an open-source framework for developing machine learning applications. Tensor Processing Unit Cloud TPU is a

39:59 customdesigned machine learning as that powers Google products like translate, photos, search, assistant, and Gmail. Built for AI on Google Cloud. Cloud TPU is designed to run cuttingedge machine learning models with AI services on Google Cloud. Modernize infrastructure and applications with Google Cloud. Basics of Google Cloud applications, compute, storage and database, networking, big

40:46 data, developer tools, identity and security, internet of things, cloud AI management tools and data transfer. Basics of Google Cloud infrastructure. Google Cloud consists of a set of physical assets such as computers and hard disk drives and virtual resources such as virtual machines located in Google data centers around the world. Each data center location is in a region. regions are available in Asia,

41:32 Australia, Europe, North America, and South America. Each region is a collection of zones that are isolated from each other within the region. Each zone is identified by a name, combining the area name with an alpha numeric identifier. For example, zone A in the East Asia region is named Asia East 1A. Google Cloud Platform global scope. Accessing resources through services. In cloud computing, what you

42:17 might be used to thinking of as an software and hardware products become services. These services provide access to the underlying resources. The list of available Google Cloud services is long and it keeps growing. When you develop your website or application on Google Cloud, you mix and match these services into combinations that provide the infrastructure you need and then add your code to enable the scenarios you want to build. Virtual machines, containers, and serverless computing. What is a virtual machine? A VM is a virtualized instance of a computer that can perform almost all of

43:02 the same functions as a computer, including running applications and operating systems. What is a container? Containers are lightweight packages of your application code together with dependencies such as specific versions of programming language runtimes and libraries required to run your software services. What is serverless computing? Serverless computing is a cloud computing execution model that allocates machine resources on an asused basis. Under a serverless model, developers can build and run applications without having to manage

43:49 any servers and pay only for the exact amount of resources used. Difference between containers, virtual machines and serverless computing within business use cases. Planning and configuring compute resources. The term compute refers to the hosting model for the resources on which your application runs. And we need to select appropriate compute choices based on the workloads. comput engine virtual machines that are

44:36 reserved with a configuration of CPU, memory, disk and GPUs as well as the operating system and additional software to run Kubernetes engine. Kubernetes clusters that are managed. Kubernetes is an open-source system for automating the deployment, scaling, and management of containerized applications. You set up a cluster and decide which containers to run. Kubernet manages scaling updates and connectivity. Cloud Run a fully managed serverless platform that runs individual containers. You provide Cloud Run with

45:22 code or a container and it hosts and autoscales as needed to respond to web and other events. Cloud functions. They are serverless functions that are triggered by events. You write individual function code and cloud functions calls it when events occur. Planning and configuring comput resources. Compute Engine overview. Compute Engine is a computing and hosting service that lets you create and run virtual machines on Google

46:08 infrastructure. It offers scale, performance, and value that lets you easily launch large compute clusters on Google's infrastructure. There are no upfront investments, and you can run thousands of virtual CPUs on a system that offers quick, consistent performance. Compute Engine provides tools to help you bring your existing applications to the cloud. You can have your applications running on Compute Engine within minutes while your data migrates transparently in the background. Bring your existing applications from your physical servers, VMware, Vspair, Amazon EC2, and Azure VMs.

46:59 Compute Engine overview. Planning and configuring network resources. Differentiating load balancing options. What is a load balancer? A load balancer distributes user traffic across multiple instances of your applications. By spreading the load, load balancing reduces the risk that your applications experience performance issues. Differentiating load balancing

47:45 options. Load balancer type. Application load balancers and traffic type is HTTP or HTTPS. Pass through network load balancers for traffic types TCP or UDP. The external load balancers also support other IP protocol traffic such as ESP, GRE, ICMP and ICMP V6. Proxy network load balancers and traffic type is TCP with optional SSL load. Differentiate between hybrid and multicloud infrastructures.

48:33 Hybrid cloud. The term hybrid cloud describes a setup in which common or interconnected workloads are deployed across multiple computing environments, one based in the public cloud and at least one being private. Multicloud. The term multicloud describes setups that combine at least two public cloud providers. The most common example of hybrid cloud is combining a private computing environment, usually an existing onremises data center, with a public cloud computing environment. A multi cloud arrangement may also contain private computing

49:21 environments. Differentiate between hybrid and multicloud infrastructures. Google Kubernetes Engine, Anthos and App Engine. Benefits of Google Cubernates Engine. Accelerates productivity of developers. Bolsters security into software supply chain. Creates new opportunities with a platform approach. Delivers always on experiences for customers. Enables cost optimization and savings for organizations. Fuels growth with focus

50:08 on business innovation. Gives freedom from proprietary tools for IT. Benefits of Anthos. Increase velocity with team based management. Secure and manage traffic. Improve observability. Manage multiple clusters as one entity. Enforce compliance policies with ease. Lower total cost of ownership. Securely run enterprise workloads and deploy configurations from Git. Benefits of App Engine,

50:56 customizable infrastructure, performance options, native feature support, and managed virtual machines. Understand the value of APIs. How application programming interfaces can modernize legacy systems. Legacy applications and services hold critical data that can enable new mobile, web, and voice experiences for your customers. To do this at an accelerated pace, you can modernize legacy services via restful interfaces, unlocking them to be consumed by new cloud services and apps.

51:44 An API abstraction and modernization layer insulates client-f facing applications from shifting backend services. Legacy backends hold critical data that is required for client applications and therefore must be unlocked in order to be scaled out to new cloud services and apps. With an API management platform, you can add security, analytics, and scalability to legacy services. Describe how APIs can create new business value, drive efficiency, accelerate time to market, unlock new business models and revenue

52:30 opportunities, activate data and inject intelligence into business processes. Improve resiliency and future proofing. Explain the benefits of Apogee, API discoverability, API insights and API reliability, cloud storage migration types. What is data transfer? Data transfer is a process of moving data without transforming it. For example, moving files as they are into objects. Google Cloud Storage Transfer Service for Cloud Data is a free Google

53:17 Cloud service that's intended to streamline the process of uploading data into Google Cloud Storage buckets. With Google Cloud Storage Transfer Service, we can move data from the following from onremises from GCP buckets and from other cloud providers. The six Rs of cloud migration. Rehost also known as lift and shift is moving from onremises to the cloud. Replatform or rebuild, also known as lift and

54:03 optimize, where we make improvements but require some coding. Refactor or rearchitect, also known as move and improve where we would move to higher performing hardware or software. Retain, revisit or do nothing at this time. Repurchase. Buy again to move to a new product common in software as a service deployments. Retire. Remove the service. Trust and security with Google

54:49 Cloud. Google Cloud security and operations. Basics of cloud security. Shared responsibility model, identity and access management, data encryption, network security, data backups and disaster recovery, security groups and ACL, zero trust security model, Basics of cloud operations. Service provisioning and deployment. Resource monitoring and

55:36 management. Scalability and elasticity. Cost management. Resource tagging and organization. Access control and identity management. Change management and automation, backup and disaster recovery, security and compliance and logging and auditing. Introduction to Google Cloud Security and Operations.

56:24 Financial governance in the cloud. How adopting cloud technology affects the total cost of ownership. Organizations often perform a cloud total cost of ownership analysis or TCO analysis when they are considering moving to the cloud. This analysis aims to weigh the cost of cloud adoption against the cost of running their current onremises systems. For onremises, TCO is associated with assessing the cost of static resources throughout their lifetime. A common mistake that organizations make when attempting to calculate cloud TCO

57:09 is to directly compare the running costs of the cloud against their onremises system. These costs are not equivalent. The cost of onremises infrastructure is dominated by the initial purchase of hardware and software. But cloud computing costs are based on monthly subscriptions or pay-peruse models. It's also important to consider all of the operational costs of running your own data center such as power, cooling, maintenance, and other support services. Encryption. Encryption translates data into another form or code so that only people with access to a secret key or

57:55 password can read it. Encrypted data is commonly referred to as cipher text. Unencrypted data is called plain text. Public key cryptography. Public key cryptography is an encryption technique that uses a paid public and private key algorithm for secure data communication. symmetric cipher and asymmetric key. Two types of encryption supported on GCP. Encryption at rest. Encryption at rest is about protecting data that is stored on disk or other

58:44 media. Encryption in transit. Encryption that is protecting data that is moving from one device to another or is in use. Identify the cost of management strategy needed in a given business scenario. Get to know billing and cost management tools. Only pay for the compute you need. Optimize cloud storage costs and performance. Tune your data warehouse and filter that network

59:29 packet. Identify the cost of management strategy needed in a given business scenario. Google Cloud security benefits, greater visibility, easy backups and recovery, cloud data compliance, data encryption, lower costs, advanced incident detection and response. Google Cloud security

1:00:19 benefits. Identify today's top cyber security challenges and threats to data privacy. Weak passwords, stolen passwords, vulnerable applications, malware, social engineering, and brute force attacks. How organizations can control and manage access to cloud resources. Google Cloud offers identity and access management which lets you give more granular access to specific Google Cloud resources and prevents unwanted access to other resources. IM lets you adopt the

1:01:06 security principle of least privilege so you grant only the necessary access to your resources. IM lets you control who has what access to which resources by setting IM policies. IM policies grant specific role to a user giving the user certain permissions to control access to resources. Google Cloud requires that accounts making API requests have appropriate IM roles. IM roles include permissions that allows user to perform specific actions on Google cloud resources. For example, the resource

1:01:52 manager.organizations.get permission allows a user to get details about their organization resource. Scaling with Google Cloud. Planning and estimating Google Cloud product. Pricing calculator. Once you have an idea of which Google Cloud products you'll use in your applications, you can use the pricing calculator to estimate your monthly charges if you switch to a paid account. Product pricing. This page has complete details on the cost of using Google Cloud

1:02:40 products. Linking projects to a billing account. Cloud billing account. Cloud billing account is used to define who pays for provided resources and is linked to one or more projects. Project users will be charged to the linked cloud billing account. In most cases, you configure billing when you create a project. If you're managing billing for projects in a cost center or if you want to close your cloud billing account, you need to know which projects are linked to your cloud billing account. View the projects linked to a billing account. Permissions required to complete this task.

1:03:26 To view the list of projects linked to a cloud billing account, you need a role that includes the following permission on your cloud billing account. Billing.resource associations dolist to view project associations for the billing account. To gain this permission, ask your administrator to grant you one of the following cloud billing IM roles on your cloud billing account. billing account viewer, billing account costs manager or billing account administrator. Google customer care support case priority and status. Google Cloud customer care priority. Google uses priority levels to

1:04:13 determine the initial response time for a support case. The priority levels are P1 critical impact service unusable in production. P2 high impact service use severely impaired. P3 medium impact service use partially impaired. P4 low impact service is functional and usable. Escalation need to code the case number and provide a reason for the escalation. Escalation process. Go to

1:04:59 Google Cloud support center and click the escalate case button which appears 30 minutes after a case is created. Google Cloud customer case status. Status new case not assigned. Assigned assigned to specialist and progress specifies who is working on support case. Waiting on customer response or action specify support needs you to complete something or provide more information. Solution offered. Solution has been provided. Customer can reopen

1:05:44 if solution does not fix issue. Closed. Case is resolved. Site reliability engineering and devops. S principles. Use automation to perform operations to scale with load. Cap the operational load. Spend 50% of the time on toil and 50% of the time on improvements. Share 5% of the operations work with the development team. Any excess operation workflows to the development team. Have an SLA or SLO for the service and measure against

1:06:30 it. Create an error pure to control velocity. Balance effective self- regulation of features against stability. Practice observability including the four golden signals latency, traffic, errors, and saturation. Use actionable symptom based alerts to govern actions. Use automated runbooks. Hold a blameless postmortem for every event. Hire only developers and use a common staffing pool for S and development. Site reliability

1:07:16 engineering and DevOps. Describe the operational challenges that DevOps solves. Challenge one, accelerating time to market fuels continuous integration, deployment and software delivery. Ensures frequent and smaller deployments to production. Enables you to deliver new features straight through to test and production environments. drives productivity across development and operations and enables you to deliver better services faster and at less

1:08:02 cost. Ensures increased frequency and pace of releases. Challenge two, reducing development cost reduces process management costs as the overheads generated due to manual interventions get eliminated. helps overcome the overhead of launching and updating new services or features. Minimizes the costs associated with product development and deployment. Enables you to leverage best practices like continuous integration and continuous delivery and achieve the required levels of scalability and reliability.

1:08:52 Challenge three, ensuring shorter release cycle. Reduces process management costs as the overheads generated due to manual interventions get eliminated. Helps respond quickly to market demands. Drives frequent and smaller deployments to production and reduces production time. pushes frequent product upgrades, enabling you to sustain in a competitive market. Empowers a collaborative workforce that can easily and quickly look into changing customer demands. Challenge four, aligning it with

1:09:38 business. Transforms IT to deliver innovation and agility. Spurs better collaboration, automation, and process improvement, delivering results based on business needs. Enables you to introduce products faster into the market. Ensures quality is always a priority with every team. drive substantial improvement in overall business performance. Challenge five, quality product with continuous testing. Facilitates automated and continuous quality monitoring. Enables automated

1:10:24 provisioning of virtualized test environments. helps in integrated build, deployment, end-toend test automation and reporting. Differentiate service availability requirements in the cloud versus on premises environments. Scalability and elasticity. Google Cloud offers scalability and elasticity. Resources can be scaled up or down based on demand, ensuring high availability even during traffic spikes. Scaling on premises infrastructure often requires significant upfront investment and planning. It may not be as flexible or

1:11:10 quick as cloud-based solutions. Redundancy and fall tolerance. Google Cloud has built-in redundancy and fall tolerance. Services are distributed across multiple data centers to ensure continuity even if one or more data centers experience problems. To achieve comparable levels of redundancy and fault tolerance on premises, significant investments in backup systems, failover mechanisms and geographically dispersed data centers are required. Service level agreements. Cloud providers typically provide service level agreements that guarantee a certain level of service

1:11:55 availability. If these SLAs are not met, compensation is usually provided. SLAs in onremises environments that are challenging to enforce and might not provide the same level of assurance as cloud-based SLAs. Maintenance and updates. Cloud providers handle maintenance and updates transparently, ensuring that services are up to date and secure without disrupting customer operations. On premises environments require manual maintenance and updates which can lead to downtime. Ensuring high availability during maintenance periods is more complex and often involves redundant systems.

1:12:44 Disaster recovery cloud providers offer comprehensive disaster recovery solutions allowing data and services to be replicated across regions for robust backup and recovery options. Setting up a robust disaster recovery system on premises is costly and complex. It often involves secondary data centers and intricate replication mechanisms. Security and compliance. Cloud providers invest heavily in security measures and compliance certifications ensuring data integrity and confidentiality. While on premises environments can be secured, maintaining compliance and security standards often require substantial expertise and

1:13:35 resources. Cost structure. Cloud services typically operate on a pay as you go model allowing organizations to align costs with actual usage. This can be more cost effective for variable workloads. Onrovisis infrastructure requires significant upfront capital expenditures. Predicting future capacity needs can be challenging potentially leading to either underutilized resources or insufficient capacity during peak times. Thank you for watching this video. If you haven't subscribed to our channel, please subscribe to Tech with Shapi Pixel.

1:14:21 Yeah. [Music]