Google Cloud infrastructure

Summary

Google Cloud's infrastructure is built on three layers: networking and security, compute and storage, and data and AI/ML products. It offers a variety of computing services, including Compute Engine, Kubernetes Engine, App Engine, Cloud Functions, and Cloud Run, catering to different needs from server management to serverless execution. Google's Tensor Processing Units (TPUs) provide specialized hardware for machine learning, enhancing efficiency and speed. For storage, Google Cloud provides options like Cloud Storage with various classes, and databases such as Bigtable, Cloud SQL, Spanner, Firestore, and BigQuery, tailored to structured and unstructured data needs.

Takeaways

• 🌐 Google Cloud has been evolving since 1998, offering secure and flexible cloud computing and storage services from its launch in 2008.
• 📚 The Google Cloud infrastructure is structured into three layers: networking and security, compute and storage, and data and AI/ML products.
• 🔒 Networking and security form the foundational layer of Google Cloud, supporting all infrastructure and applications.
• 💻 Compute and storage are decoupled in Google Cloud, allowing them to scale independently based on demand.
• 🧠 The top layer focuses on AI and machine learning, providing tools for data ingestion, storage, processing, and business insights.
• 🛠️ Compute Engine offers IaaS with virtual resources similar to a physical machine, providing maximum flexibility for server instance management.
• 🚀 Google Kubernetes Engine (GKE) runs containerized applications in the cloud, differing from Compute Engine's individual virtual machine approach.
• 📦 App Engine is a PaaS offering that binds code to libraries for infrastructure needs, allowing developers to focus on application logic.
• 🎯 Cloud Functions is a serverless execution environment that executes code in response to events without the need for server management.
• 🏃 Cloud Run is a fully managed platform for running stateless workloads, abstracting infrastructure management and automatically scaling.
• 💡 Google introduced TPUs in 2016 to overcome scaling limitations for ML workloads, offering higher efficiency and speed over CPUs and GPUs.
• 🗃️ Google Cloud offers various database and storage services, including Cloud Storage, Bigtable, SQL, Spanner, Firestore, and BigQuery, each suited to different data types and business needs.
• 📈 Cloud Storage has four storage classes catering to different access frequencies and cost considerations: standard, nearline, coldline, and archive.
• 🔑 Choosing the right Google Cloud service depends on whether the data is structured or unstructured and the specific business requirements.

Q & A

What is the history of Google's involvement with data and artificial intelligence?

Google has been working with data and artificial intelligence since its early days as a company, starting in 1998, and launched Google Cloud in 2008 to provide secure and flexible cloud computing and storage services.

How does Google Cloud infrastructure organize its services?

Google Cloud infrastructure is organized into three layers: networking and security at the base, compute and storage in the middle, and data and AI/machine learning products at the top.

What is the significance of separating compute and storage in Google Cloud?

Compute and storage are decoupled in Google Cloud to allow them to scale independently based on need, providing flexibility and efficiency for users.

What are the different compute services offered by Google Cloud?

Google Cloud offers Compute Engine (IaaS), Google Kubernetes Engine (for containerized applications), App Engine (PaaS), Cloud Functions (serverless execution), and Cloud Run (fully managed compute platform).

What is the purpose of Google's Tensor Processing Unit (TPU)?

TPUs are custom-developed ASICs used to accelerate machine learning workloads, providing higher efficiency and performance for AI and ML applications compared to CPUs and GPUs.

How does Cloud Run differ from other compute services in terms of infrastructure management?

Cloud Run is a fully managed compute platform that abstracts away all infrastructure management, allowing users to focus on writing code and automatically scaling up and down from zero.

What is the role of hardware in providing processing power for Google Cloud services?

The processing power comes from hardware, such as computer chips like CPUs, GPUs, and Google's custom TPUs, which are designed to meet the specific computation needs of domains like machine learning.

Why is it important to decouple compute and storage in cloud computing?

Decoupling compute and storage allows for proper scaling capabilities, enabling compute and storage to scale separately according to the application's requirements.

What are the different storage classes offered by Google Cloud Storage and their use cases?

Google Cloud Storage offers standard storage for frequently accessed data, nearline storage for infrequently accessed data, coldline storage for data accessed less than once every 90 days, and archive storage for data accessed less than once a year.

How does one choose between Google Cloud's database and storage services?

The choice depends on the data type (unstructured or structured), the nature of the workloads (transactional or analytical), and whether SQL access is needed.

What is the difference between transactional and analytical workloads in the context of databases?

Transactional workloads require fast data inserts and updates for maintaining a system snapshot with standardized queries, while analytical workloads involve reading entire datasets and performing complex queries like aggregations.

Outlines

00:00

🌐 Google Cloud Infrastructure Overview

This paragraph introduces Google Cloud's infrastructure, highlighting its evolution since 1998 and the launch of Google Cloud in 2008. The infrastructure is structured into three layers: networking and security, compute and storage, and data and AI/ML products. The compute layer includes services like Compute Engine, Google Kubernetes Engine, App Engine, Cloud Functions, and Cloud Run, each tailored to different computing needs. The introduction of Tensor Processing Units (TPUs) in 2016 is emphasized, showcasing Google's commitment to providing efficient hardware for machine learning workloads. The paragraph concludes by discussing the decoupling of compute and storage for scalable cloud solutions.

05:05

💾 Google Cloud Storage Solutions

This paragraph delves into Google Cloud's storage offerings, emphasizing the decoupling of compute and storage for scalable solutions. It outlines the four primary storage classes available in Cloud Storage: standard, nearline, coldline, and archive, each designed for different access frequencies and cost considerations. The paragraph also differentiates between unstructured and structured data, explaining the suitability of Cloud Storage for unstructured data. For structured data, it discusses transactional and analytical workloads, and how to choose between Google Cloud's database services such as Cloud SQL, Spanner, Firestore, and BigQuery based on SQL access needs and the nature of the workload. Bigtable is also mentioned for its real-time, high-throughput capabilities.

Keywords

💡Google Cloud

Google Cloud refers to the suite of cloud computing services that Google offers to businesses, developers, and individuals. It includes infrastructure, platform, and software services. In the video, Google Cloud is central to the discussion as it provides the infrastructure and services for data and AI/ML applications, showcasing its role in modern computing.

💡Infrastructure as a Service (IaaS)

IaaS is a form of cloud computing where the service provider offers virtualized computing resources over the internet. In the context of the video, Google Compute Engine is an example of IaaS, providing virtual machines and associated infrastructure to users, allowing them to manage server instances as if they were physical machines.

💡Containerization

Containerization is the process of packaging software applications and their dependencies into a container that can run consistently across different computing environments. The video mentions Google Kubernetes Engine (GKE), which facilitates running containerized applications in a cloud environment, emphasizing the efficiency and portability of containers.

💡Platform as a Service (PaaS)

PaaS is a category of cloud computing services that provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure typically associated with developing and launching an app. Google App Engine is highlighted as a fully managed PaaS, abstracting away infrastructure management and enabling developers to focus on application logic.

💡Serverless Computing

Serverless computing is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers. The video describes Google Cloud Functions as a serverless environment, where users can execute code in response to events without managing servers, emphasizing the ease of use and cost-effectiveness of this model.

💡Tensor Processing Unit (TPU)

A Tensor Processing Unit is an application-specific integrated circuit developed by Google specifically for accelerating machine learning workloads. The video explains that TPUs are more efficient than traditional CPUs and GPUs for AI and ML applications due to their domain-specific architecture, which is tailored for tasks like matrix multiplication.

💡Cloud Storage

Cloud Storage is a service where data is maintained, managed, and backed-up in cloud storage data centers. In the video, Google Cloud Storage is discussed as a solution for storing unstructured data, with different storage classes catering to various access needs and cost considerations.

💡Structured Data

Structured data refers to information that is organized into a formatted repository, typically a database, where it can be easily accessed, managed, and updated. The video contrasts structured data with unstructured data and discusses how different Google Cloud services are suited to handle structured data based on transactional or analytical workloads.

💡BigQuery

BigQuery is a fully-managed data warehouse solution that enables scalable analysis of petabyte-scale datasets. The video positions BigQuery as an ideal service for analytical workloads that require SQL commands, highlighting its ability to handle large-scale data analysis.

💡Firestore

Firestore is a transactional, NoSQL, document-oriented database service offered by Google Cloud. It is mentioned in the video as a suitable option for transactional data that will be accessed without SQL, emphasizing its real-time capabilities and scalability.

💡Cloud SQL

Cloud SQL is a fully-managed relational database service for various database engines. The video discusses Cloud SQL as an option for transactional data that requires SQL access, noting its suitability for local to regional scalability.

Highlights

Google has been working with data and artificial intelligence since 1998.

Google Cloud was launched in 2008 to provide secure and flexible cloud computing and storage services.

Google Cloud infrastructure is structured in three layers: networking and security, compute and storage, and data and AI/machine learning products.

Compute and storage are decoupled in Google Cloud to scale independently based on need.

Google offers a range of computing services including Compute Engine, Google Kubernetes Engine, App Engine, Cloud Functions, and Cloud Run.

Compute Engine provides maximum flexibility for managing server instances.

Google Kubernetes Engine runs containerized applications in a cloud environment.

App Engine is a fully managed PaaS offering that binds code to libraries for infrastructure access.

Cloud Functions executes code in response to events in a serverless environment.

Cloud Run is a fully managed platform for running stateless workloads without infrastructure management.

Google introduced the Tensor Processing Unit (TPU) in 2016 to accelerate machine learning workloads.

TPUs are custom-developed ASICs for higher efficiency in AI and ML applications compared to CPUs and GPUs.

Cloud TPUs are integrated across Google products, making advanced hardware available to Google Cloud customers.

Compute and storage in cloud computing can scale separately, unlike desktop computing.

Google Cloud offers fully managed database and storage services tailored to different data types and business needs.

Cloud Storage has four primary storage classes for different access needs: standard, nearline, coldline, and archive.

Structured data can be managed using SQL-based services like Cloud SQL and Spanner, or NoSQL solutions like Firestore and Bigtable.

BigQuery is Google's data warehouse solution for analyzing large datasets with SQL commands.

Transcripts

Let’s explore Google Cloud infrastructure.

Google has been working with data and artificial intelligence since its early days as a company

in 1998.

Ten years later, in 2008, Google Cloud was launched to provide secure and flexible cloud

computing and storage services.

You can think of the Google Cloud infrastructure in terms of three layers.

At the base layer is networking and security, which lays the foundation to support all of

Google’s infrastructure and applications.

On the next layer sit compute and storage.

Google Cloud separates, or decouples, as it’s technically called, compute and storage so

they can scale independently based on need.

The top layer includes data and AI/machine learning products, which enable you to perform

tasks to ingest, store, process, and deliver business insights, data pipelines, and ML

models.

Thanks to Google Cloud, these tasks can be accomplished without a need to manage and

scale the underlying infrastructure.

Let’s begin with compute.

Organizations with growing data needs often require lots of compute power to run data

and AI jobs.

And as organizations design for the future, the need for compute power only grows.

Google offers a range of computing services.

The first is Compute Engine.

Compute Engine is an infrastructure as a service, or IaaS, offering which provides compute,

storage, and network resources virtually that are similar to a physical machine.

You use the virtual compute and storage resources in the same as you would manage them locally.

Compute Engine provides maximum flexibility for those who prefer to manage server instances

themselves.

The second is Google Kubernetes Engine, or GKE, GKE runs containerized applications in

a cloud environment, as opposed to on an individual virtual machine like Compute Engine.

A container represents code packaged up with all its dependencies.

The third computing service offered by Google is App Engine, a fully managed PaaS, or platform

as a service, offering.

PaaS offerings bind code to libraries that provide access to the infrastructure application

needs.

This allows more resources to be focused on application logic.

Then there is Cloud Functions, which executes code in response to events, like when a new

file is uploaded to Cloud Storage.

It’s a completely serverless execution environment, which means you don’t need to install any

software locally to run the code and you are free from provisioning and managing servers.

Cloud Functions is often referred to as Functions as a Service.

And, finally, there is Cloud Run, a fully managed compute platform that enables you

to run requests or event-driven stateless workloads without having to worry about servers.

It abstracts away all infrastructure management so you can focus on writing code, and it automatically

scales up and down from zero, so you never have to worry about scale configuration.

Cloud Run charges only for the resources you use, so you never pay for over-provisioned

resources.

Where does the processing power come from?

It’s from the hardware: from computer chips.

However, traditional computer chips, like central processing units, or CPUs, and even

the more recent graphics processing units, or GPUs, may no longer scale to adequately

reach the rapid demand for ML.

To help overcome this challenge, in 2016, Google introduced the Tensor Processing Unit,

or TPU.

TPUs are Google’s custom-developed application-specific integrated circuits (ASICs) used to accelerate

machine learning workloads.

TPUs act as domain-specific hardware, as opposed to general-purpose hardware like CPUs and

GPUs.

This allows for higher efficiency by tailoring the architecture to meet the computation needs

in a domain, such as the matrix multiplication in machine learning.

TPUs are generally faster than current GPUs and CPUs for AI and ML applications.

They are also significantly more energy-efficient.

Cloud TPUs have been integrated across Google products, making this state-of-the-art hardware

and supercomputing technology available to Google Cloud customers.

Let’s now examine storage.

For proper scaling capabilities, compute and storage are decoupled.

That is one major difference between cloud and desktop computing.

With cloud computing, compute and storage can scale separately.

Most applications require a database and storage solution of some kind.

Google Cloud offers fully managed database and storage services.

These include: Cloud Storage Cloud Bigtable Cloud SQL Cloud

Spanner Firestore And BigQuery How do you choose from these products and services?

Well, it depends on the data type and business needs.

Let’s look at the data type, which includes unstructured versus structured data.

Unstructured data is information stored in a non-tabular form such as documents, images,

and audio files.

Unstructured data is usually suited to Cloud Storage.

Cloud Storage has four primary storage classes.

The first is standard storage.

Standard storage is considered best for frequently accessed, or “hot,” data.

It’s also great for data that is stored for only brief periods of time.

The second storage class is nearline storage.

This is best for storing infrequently accessed data, like reading or modifying data once

per month or less, on average.

Examples include data backups, long-tail multimedia content, or data archiving.

The third storage class is coldline storage.

This is also a low-cost option for storing infrequently accessed data.

However, as compared to nearline storage, coldline storage is meant for reading or modifying

data at most once every 90 days.

The fourth storage class is archive storage.

This is the lowest-cost option, used ideally for data archiving, online backup, and disaster

recovery.

It’s the best choice for data that you plan to access less than once a year, because it

has higher costs for data access and operations and a 365-day minimum storage duration.

Alternatively, there is structured data, which represents information stored in tables, rows,

and columns.

Structured data comes in two types: transactional workloads and analytical workloads.

Transactional workloads stem from online transaction processing systems, which are used when fast

data inserts and updates are required to build row-based records.

This is usually to maintain a system snapshot.

They require relatively standardized queries that impact only a few records.

Then there are analytical workloads, which stem from online analytical processing systems,

which are used when entire datasets need to be read.

They often require complex queries, for example, aggregations.

Once you’ve determined if the workloads are transactional or analytical, you then

need to identify whether the data will be accessed using SQL or not.

So, if your data is transactional and you need to access it using SQL, then two options

are Cloud SQL and Spanner.

Cloud SQL works best for local to regional scalability, while Spanner works best to scale

a database globally.

If the transactional data will be accessed without SQL, Firestore might be the best option.

Firestore is a transactional, NoSQL, document-oriented database.

If you have analytical workloads that require SQL commands, BigQuery is likely the best

option.

BigQuery, Google’s data warehouse solution, lets you analyze petabyte-scale datasets.

Alternatively, Bigtable provides a scalable NoSQL solution for analytical workloads.

It’s best for real-time, high-throughput applications that require only millisecond

latency.