Google Cloud Data Analytics Certificate Course

Key Takeaways

Unlock the power of data with the Google Cloud Data Analytics course. In this comprehensive program developed by Google Cloud, you will learn the skills to analyze, visualize, and communicate insights from complex data sets, transforming raw numbers into actionable strategies. Through expert instruction and hands-on challenges, you'll build a portfolio of industry relevant projects that demonstrate your expertise. This complete course can be watched alongside the Google Cloud Skills Boost platform where you will find interactive labs and hands-on practice environments. You get 35 credits free per month for labs, but for faster completion, you can pay for unlimited access. At the end, you can earn a certificate. So, let's begin your journey into cloud data analytics with the foundational concepts you'll need to start or advance your career in this field. And thanks to Google for providing a grant to make this course [Music] possible. There's something happening right now everywhere in the world that's changing all of our lives in every way. It's cloud computing. How can cloud computing make that much change? By connecting us people with data quickly, easily, and anywhere at any time. It affects how we communicate, work, shop, plan, and even how we relax and have fun. The cloud is changing people's lives, and it's also completely reshaping and improving business. These days, data is the cornerstone of all kinds of organizations. They depend on non-stop information about sales transactions, consumer feedback, inventory, and purchase orders, customer service interactions, market research statistics, and so much more. Uninterrupted access to business data is a must for organizations which creates another must. People who can assess that data and put it to work. And this is why the demand for cloud data analytics professionals keeps growing and growing. We need people like you to help organizations understand their customers, collaborate with partners, strategize for the future, mitigate risk, and become more flexible and resilient. The content in this program will equip you with the knowledge and skills required of entry-level roles in the field of cloud data analytics. Hi, I'm Joey. Here at Google, I am an analytics manager. This means that I lead a team of business analysts whose job is to provide databacked insights to inform key business decisions. I'm so happy to welcome you to the program. I'm your course one instructor and I'll be by your side the whole way through this course. I grew up in a single parent household in the Los Angeles area with strong roots in my Mexican-American heritage. Living in a big, diverse, complicated city like LA, while challenging at times, definitely instilled in me a passion for connecting with diverse groups and helped me build interpersonal skills that have been super valuable in life and in my career. My career path wasn't linear or planned, but through an internship and an early career rotational program, I discovered a passion for data analytics and specifically using data to help people make better decisions or gain knowledge they wouldn't otherwise have. As an analyst, I want to show folks that data is for everyone and make technical work less intimidating and more approachable to all. The program is divided into courses based on different cloud data analytics processes. The course topics include an introduction to cloud computing in data analytics, cloud storage and data management, data processing and analysis in the cloud, and visualization of data in the cloud. I encourage you to complete the courses in order as each topic builds on what you've learned before. The final course is the capstone. It's a great opportunity to demonstrate the knowledge and skills you gained throughout your academic journey. We've got videos and readings to teach you cloud data analytics concepts and skills. Then interactive activities and labs will let you practice those concepts and skills. You can take the labs more than once. So if you hit some trouble spots, just keep at it. You'll also have quizzes to confirm your understanding and glosseries to help you prepare to do your very best. And career resources, including resume and interview prep, will help you prepare to apply for jobs and impress hiring managers. You'll hear from Googlers like me working in cloud computing. We'll give you an inside perspective at what it's like in our industry and share personal stories of how we got into the field. Some of these Googlers are going to join me in guiding you through the courses. Let's take a second to meet them now. Hello, I'm Eric and I'm a product analyst at Google. In course 2, you'll explore how data is structured and organized. You'll gain experience with data lakehouse architecture and cloud components like BigQuery, Google Cloud Storage, and data proc to efficiently store and analyze and process large data sets. Next, you'll meet my colleague, Alex. Hey there, I'm Alex and I'm a data analytics customer engineer. I'm really looking forward to spending time with you as you'll learn all about the data journey from collection to insights. You'll explore data transformation and practice strategies to transform real data sets to solve business needs. Hi, I'm CJ and I work in data analytics here at Google. I'll guide you through the key stages of visualizing data in the cloud. Storytelling, planning, exploring data, designing visualizations, and collaborating with data. You'll use Looker to create data visualizations and build dashboards. I'm Christine, your course 5 instructor. Together, we'll put everything you learned from across courses one through four into action in a capstone project, and you'll create impressive work examples to share with future employers. All of us are thrilled to introduce you to the fascinating and rewarding field of cloud data analytics. So, let's get you started on your cloud journey. Not too long ago, when a company stored data or ran programs, it needed a huge room filled with a bunch of gigantic, noisy computers humming away right there in the office. But in the 1960s, a group of engineers asks, "What if we share computing power among many users, so not everyone needs their own computer?" Fast forward a few decades and here we are with remote data centers ready to store our data, run our apps, help us with analysis, and so much more. In this course, you'll start your journey into the world of cloud computing, and gain the fundamental knowledge you need to be successful in the field. Whether you're a cloud newcomer or seeking to level up your cloud skills, you've come to the right place. This course will provide you with the solid foundation in key concepts, skills, and tools used for data analysis with Google Cloud. I started my career as a philosophy graduate with no professional experience, unsure of how my education would translate into a job. But after being exposed to a few different roles at Google, I found a passion for data analysis and engineering where a lot of entry-level tasks were like mini logic puzzles that I was paid to solve. I looked forward to the technical challenges that the role offered which provided the building blocks for my current career path. I first learned to write SQL in my role as an HR analyst at Google. One of my first responsibilities was as a primary responder on our team's ticket queue. Each day we'd receive requests from internal business partners to produce data reports, usually in the form of big spreadsheets with custom logic based on real business questions and problems. I really enjoyed the process of fulfilling these requests. Starting with transforming the business request into an analytics problem, using SQL to mold the data into an answer and providing a data set that was understandable and approachable to our non-technical users. It felt great to offer a service to our users and give them information that they couldn't otherwise obtain. It was fun. As more organizations adopt cloud-based solutions, there's a growing need for skilled cloud professionals to help them make the transformation. To get you on your way, I'll introduce you to the program, let you know what to expect moving forward, and share some great tips for successfully completing the certificate. You'll learn how to define cloud computing, identify its components, and differentiate between cloud and traditional computing. You'll explore cloud data analysis compared to on premises physical data analysis and you'll learn about the impact of cloud data analytics on all kinds of businesses with a special focus on the Google cloud architecture framework. Then you'll discover the inner workings of data management and the data life cycle and the cloud data analyst role in keeping both running smoothly. You'll also explore how cloud professionals collaborate to create some really cool business projects together. Finally, you'll discover key tools in the cloud data analyst toolbox and learn about the importance of process management in cloud computing. As you progress, you'll be introduced to Google cloud-based tools, including BigQuery and data proc. And after completing this course, you'll know about cloud data tools and be able to understand and communicate cloud benefits, share timely insights, and so much more. I'm so excited to be part of your cloud data analytics exploration, and I'm here to guide you every step of the way. Let's keep the momentum going and head on over to the next topic. Hey there. Coming up, we have many exciting things to discover about the cloud. Here's a quick breakdown. First, we'll check out the basics of cloud data analytics. You'll learn about its history and explore the many benefits of cloud computing. After that, you'll consider the differences between cloud computing and traditional computing. This includes their network infrastructures, defining characteristics and advantages and limitations. Next, you'll tap into the program resources so you can make a plan to be successful and career ready. To wrap up, peruse the glossery with key terms and definitions. meet you again soon. My name is Ben and I'm the senior vice president of learning and sustainability at Google. I'd always been interested in learning because for me my mom my mother was a school teacher and I felt that um learning is really what enables people to reach a different point than they otherwise would. uh I know it enabled me to go to a place I could not have dreamt of being were it not for the education I got and I think that's incredibly uh important for people to have access to that kind of opportunity and growing up in India I did have access to a good school I did not come from a wealthy family but I had access to a good school and I saw the difference it made in my life and I think today with the help of technology we can hopefully bring more of that opportunity to more people in the world the cloud is really important important because it's a trajectory of where computation is going. If you think about all of the major uh products that you use, almost all of them are now based in some uh online cloud uh data center and they have access to all these amazing computing resources and they enable you to these these services to really provide amazing things for their users. So studying cloud technologies enables you to participate in that whole economy of jobs and of opportunities that consist of building these powerful facilities in the cloud that are being used by people around the world. One of the really interesting ways in which education is evolving is allowing people to build and learn individual skills through various skilling courses. Many aspects of education are not available to everybody everywhere unfortunately but it's possible to build the basic skills that one could get from that one needs from an education more peace wheel today and I think the approach of skilling can allow people to build up the pieces of the education that they really need in the way that they have access to in a way that they have time for in a way that they have the resources for. The initial parts of learning anything are learning the basics and the fundamentals. Whether it is a sport or a or or or a physical skill like carpentry or whatever, the first steps are learning the basics. So, persevere with it and it'll get really interesting. I've been working with computers for what is it now over 30 35 years and it is still fascinating every day. Hello cloud enthusiasts. Get ready to learn exactly what cloud computing is all about, including how it works, the components of a cloud infrastructure, and different cloud service models. So, first up, what exactly is cloud computing? Cloud computing is the practice of using ondemand computing resources as services hosted over the internet. Over the internet is what makes up the cloud part. It eliminates the need for organizations to find, set up, or manage resources themselves, and they only pay for what they use. Cloud computing uses a network to connect users to a cloud platform. This is a virtual space where they can access and borrow computing services. A primary computer handles all communication between devices and servers to share information. And there are privacy and security measures to keep everything safe. Here's another way to think about it. Picture cloud computing like a shared kitchen in a rental apartment owned by a property management company or in the case of the cloud a third party service host. The kitchen has many appliances and cooking tools just as the cloud platform has servers, storage, hardware and software. So when someone in the apartment gets hungry, they just go ahead and cook a meal in the welle equipped kitchen. They don't each have to buy their own wooden spoons or toaster oven. Likewise, the cloud enables organizations to access computing resources on demand without spending time and money buying and maintaining their own storage, hardware, and software. It's the unique infrastructure of a cloud computing model that makes all of this possible. This infrastructure has four main components: hardware, storage, network, and virtualization. Let's check out hardware first. Types of hardware include servers, processors, and memory, network switches, routers, and cables, firewalls, and load balancers, cooling systems, and power supplies. These are the physical items needed to keep things running. Now, data storage in a cloud computing infrastructure can occur in three main ways. File, object, or block. File storage keeps data in one place and organizes it in a simple, easy to understand way through a hierarchy of files and folders. This is the oldest and most widely used data storage system, but it's a bit cumbersome and can only accomplish so much. Next, object storage holds unstructured data along with its metadata. Metadata is just data about data. For example, a picture taken with a smartphone might contain information about the location, the date, and the type of device that captured the image. This is really useful for understanding the photo, just as metadata explains what its own data is all about. Lastly, block storage divides large volumes of data into smaller pieces, optimally organized with unique labels. An advantage of block storage is that the data is easily accessible but it can be expensive and has limited capability to handle metadata. All right. Now we have the network. After all cloud computing infrastructure needs a way to connect its back-end resources and this connection is made possible through a network of the physical hardware. Through this network, users tap into cloud resources using some of the hardware mentioned earlier, including routers and firewalls. Basically, the physical network setup is what enables the virtual one to operate. Finally, there is virtualization, which is a technology that creates a virtual version of physical infrastructure like servers, storage, and networks. This is what lets the service work without a connection. Here at Google, we have many data centers. A data center is a physical building that contains servers, computer systems, and associated components. These facilities provide a centralized location for vast amounts of data. And skilled cloud analysts access this valuable information right through the cloud. For all sorts of business tasks and projects, cloud analysts select and extract relevant data, then prepare it for processing and examination. They know how to expertly analyze, visualize, and share data discoveries to uncover valuable insights and make smart business decisions. So, it's really important to know that there are three primary models to choose from. Each offers a different level of flexibility and control. These models are infrastructure as a service or IAS, platform as a service Pas and software as a service sA. First, IAS is a cloud computing model that offers ondemand access to information technology or IT infrastructure services including hardware, storage, network, and virtualization tools. With IAS, a service provider hosts, maintains, and updates the infrastructure. Your organization would manage everything else, your operating system, your data, and your applications. An AAS model provides the highest level of control over your IT resources, and works a lot like traditional on premises IT. An example of an IAS model is cloud storage, like emails you've sorted into an online folder. Here's another example. When someone leases a car, it's like they're borrowing it for a while, having fun driving it around, but they have to give it back when the lease agreement is up. Well, IAS is kind of like that. A user picks the infrastructure they want, uses it for the contracted period, but they do not own it. Next, PAS provides hardware and software tools to create an environment for the development of cloud applications, simplifying the application development process. PAS is all about helping users build apps. So, your organization would enjoy being able to fully focus on app development without the burden of managing and maintaining the underlying infrastructure. Your developers would create, test, troubleshoot, launch, host, and maintain your app all on the platform. PAS is like hopping into a taxi and telling the driver where to take you. You're not behind the wheel, but you trust the driver to get you to where you need to be. Lastly, SAS provides users with the licensed subscription to a complete software package. This includes the infrastructure, maintenance and updates, and the application itself. Other users also have access to use the same services. Using SAS, you just connect to the app through the internet. Think of SAS like riding the bus. You pick your stop from routes that are set already and you share the bus ride with other people. Remember that these examples are meant to demonstrate the level of individual customization in IAS, PAS, and SAS. They do not refer to any actual hardware or software details. Woo! We've covered a lot about cloud computing, infrastructure, and service models. I think we've earned ourselves a study snack. I'm going to go cook something up in my cloud kitchen. Catch you later. I love today's module because I get to do one of my most favorite things, nerd out about the cloud. I'm a huge fan, but I also know that as a cloud data professional, my enthusiasm level may come on a little strong for folks who don't have a cloud computing background. That's why it's important to really understand the cloud and its many advantages, so that you can explain it clearly to others in a way that is easy to understand and hopefully exciting. Let's first learn about accessibility. One of the big advantages of a cloud computing model is that organizations can access and manage data, software, storage, and cloud infrastructure from any location at any time through the internet. They don't need to be physically present where the hardware and software are installed. And they don't need their cloud service provider assistance when they need more data. Next is scalability, which means to easily expand or upgrade computing resources to meet changing needs. Scalability eliminates physical computing limitations. Now, the benefit of cost savings is pretty straightforward. Organizations only pay for the computing resources used. In a cloud computing model, organizations get what's called a measured service. Similar to household utilities like electricity and water, users are charged only for what they use based on the number of transactions, the storage volume, and the amount of data transferred. This helps make all kinds of business initiatives more profitable and sustainable. The advantage of security is also pretty straightforward. With cloud computing, an organization's systems, data, and computing resources are protected from theft, damage, loss, and unauthorized use. Cloud computing security is generally recognized as stronger than the security of a traditional network infrastructure. This is because data is located in data centers that few people have access to. Plus, the information stored on cloud servers is encrypted, meaning it's not something that's easily broken into. Okay, moving on to efficiency. There's a lot that's efficient about cloud computing, but one of the main advantages is that organizations can provide immediate access to new and upgraded applications. There's no time wasted worrying about the state of network infrastructure or going through a costly or timeconsuming implementation process. There are tons of amazing things about the cloud and now we've come to freeing resources so users can focus on more value added tasks in the cloud field. We refer to this as managed services. A managed service involves a third-party provider taking care of the ongoing maintenance, management, and support of an organization's cloud infrastructure and applications. This in turn gives users lots more time to focus on other work. It's like a mechanic who automatically comes to you for annual inspections and services rather than you spending many hours in a mechanic shop waiting for services. That's because all of the ongoing maintenance and management from the cloud happens automatically in the background. A user doesn't have to initiate it. Because cloud computing is super versatile, it offers a wide range of common uses, including disaster recovery, data storage, and largecale data analysis that provides users with significant benefits. Let's start with disaster recovery. Using cloud computing in disaster recovery means having access to more data centers to ensure that data and information is safe and secure during an emergency. The next benefit is data storage. Data storage helps streamline data centers by storing large volumes of data which enables easier access to the data, analysis of the data and backup of the data. Then we have largecale data analysis. Large-scale analysis offers easy and quick access to multiple data sources and intuitive user interfaces to query and explore the data. This speeds up the overall process of discovering datadriven insights. Isn't cloud computing amazing? Users can say goodbye to the limitations of traditional data storage and computing methods and enjoy the world of advantages that it offers. and data analysts can help users seize these advantages with expert cloud data analysis skills. When cloud computing was first introduced, many people resisted the idea of losing physical control over important files, cherished photos, and all sorts of other data. People were used to keeping these things close by, under their own roofs. So, let's use those cherished photos as an example. Putting a photo in an album that you keep on your bookshelf does offer control, convenience, and security to a certain extent. Control can be limited by resources. You need money, materials, and time to print out a photo or purchase a frame or album. You can also only fit so many physical items in your space. As far as security goes, well, that physical moment could be damaged. Now, let's think about how we can enjoy that photo if it's in the cloud. You can view and share it anytime, anywhere. And if you still want a physical copy, you can make one and feel comfortable knowing that you have backup just in case. Choosing between traditional and cloud computing is also a trade-off. Both have advantages and limitations, and both can have a place in business depending on what the priorities are. In this video, you'll learn about traditional computing, how it works, and its defining characteristics. You'll then compare traditional and cloud computing which will be valuable to know in the role as a cloud data analyst. So what's traditional computing? Traditional computing is a computing model that enables data storage access and management through the use of physical hardware and software within a network infrastructure typically located on premises. Here's how that all comes together. First, hardware is set up in a dedicated space or room by IT professionals. Next, the required software, operating systems, applications, and security tools are purchased and installed. Once the hardware and software operational, IT personnel are responsible for maintaining and managing the entire system. This infrastructure gives an organization sole control and access to its data and equipment. So with traditional computing, everything you need is located in one location on premises and can't be accessed anywhere else. This defining characteristic offers four key advantages: control, security, compliance, and no reliance on the internet. Let's explore each of these. First, with traditional computing, organizations have full control over their hardware, software, and data. They can customize their localized network infrastructure to meet their specific needs. And because of this control, users usually feel more confident about the second advantage, security, if properly maintained. This is because they have sole access to their systems and sensitive information. Third, traditional computing might be the only viable option if a business is in an industry that requires data to be stored on premises. This is an example of compliance which means that a company must follow certain regulations, rules, and laws. In this case, ones that deal with data security. Lastly, traditional computing does not rely on an internet connection when users want to access the network or the data it contains. So, important information can be accessed even if internet service goes down. But just as with our photo album example, there are some downsides. First, with the traditional computing system, data access is limited to the device and location where the hardware and software are installed. Also, scaling up in a traditional computing model is challenging. Software limitations, the time needed to purchase and set up hardware, and the physical space required make it difficult to scale and expensive. Besides scaling up expenses, traditional computing involves buying hardware and software plus ongoing maintenance of network infrastructure. Lastly, traditional computing can be inefficient as each user software must be purchased rather than shared. And again, the software is not automatically updated. These are just some of the reasons why many organizations are moving to the cloud for their computing needs. The cloud is more accessible, scalable, and offers tons of savings. It's also super secure, efficient, and frees up staff to work on more projects. It's picture perfect. Get it? Thanks for joining me as we venture into the wide world of cloud data warehousing. There's so much data out there, it's truly dizzying. So, it's no surprise that businesses have struggled to figure out where to keep it all. The fact is, traditional databases struggled to keep up with the evolving demands of data analytics. Luckily, cloud data warehouses are emerging to fill the need. How do they do it? Well, that's what we're going to learn about in this video. First, a cloud data warehouse is a large-scale data storage solution hosted on remote servers by a cloud service provider. To understand this better, picture it like a huge warehouse where large amounts of different types of containers from various places are stored. The cloud data warehouse can collect, store, integrate, and analyze data. There are many advantages to this structure. Cloud data warehouses are typically fully managed by the cloud provider. This means that the cloud provider takes care of various operational tasks and maintenance, allowing users to focus on utilizing the data and gathering insights rather than handling the underlying infrastructure. This saves time, money, and resources. Cloud data warehouses also have more uptime compared to on premises data warehouses. Uptime is the amount of time a machine is operational. And of course, only working computers have the ability to scale and support increased demands for data. Next, cloud warehouses can integrate separated data by gathering data from various structured sources within an organization like sales systems, email lists, and websites and pulling it all into one place. This integrated data then can be analyzed for some pretty exciting and useful business insights. Another big advantage is that cloud data warehouses provide real-time analytics, ensuring users have quick access to the latest information. And in business, being fast is usually the key to outperforming the competition. Cloud data warehouses also offer some really cool artificial intelligence or AI and machine learning or ML capabilities. And when you apply AI and ML to your data analysis, this really powers up the possibilities. My team worked on a recent project where we built a predictive model to help Google anticipate demand for office amenities such as its cafes and help save money and reduce waste. Using ML tools, we were able to test over 30 factors across months and months of data and build a model that could forecast demand with enough accuracy and time to allow on the ground teams to adjust accordingly. Pretty cool, right? Last but not least, cloud data warehouses enable custom reporting and analysis. This means that users can analyze and generate reports specifically from historical data because it is stored on a separate server from data related to current business transactions and day-to-day operations. As you've probably figured out, the types and amounts of data that companies need to organize are only growing, which means so is the demand for data storage. Luckily, cloud data warehouses are up for the challenge with the added benefits of management and analysis to make it even easier to use the data you have. Okay, data enthusiasts, now that we know what cloud-based data warehouses are, we should probably figure out which one suits our needs. And I've got a great one to introduce to you. Meet Big Query, Google's powerhouse of storage and analysis. An organization's data is vital to its business success and data warehousing helps make the most of that data by providing quick and easy access to information which leads to ideas, insights and best of all datadriven decisionmaking. BigQuery is a data warehouse on Google Cloud that helps users store and analyze data right within BigQuery. They can query data, filter large data sets, aggregate results, and perform some really complex operations. BigQuery works with SQL or structured query language. This is a computer programming language used to communicate with the database. It allows users to search through massive amounts of data and find information they are searching for incredibly quickly using Google infrastructure. As a cloud data analyst, BigQuery's integrated SQL interface and machine learning capabilities will help you discover, implement, and manage data tools to inform critical business decisions. The output of your work in BigQuery can integrate with typical business intelligence tools or spreadsheets. But there's a lot more to explore. Another feature is BigQuery's ability to easily migrate existing data warehouses from other cloud service providers. This is a huge timesaver. One of my favorite things about BigQuery is its dry run parameter. This lets you check your query thought process and plan before actually running it. And BigQuery would tell you the number of bytes the query will run so you can estimate the cost before actually querying the database. It's like a practice swinging golf to help you make sure your ball goes in the hole. You can also use BigQuery to store, explore, and run queries on data gathered from servers, sensors, and other devices. And scheduled queries can be used to automatically refresh data and keep tables up to date. Data can be updated hourly, daily, or weekly, so you'll deliver the most dynamic, timely metrics to your stakeholders. On my team, we use BigQuery almost daily to query, transform, and report on data. Using BigQuery, we're able to tap into a multitude of data sources, which allow us to support our users with the most relevant insights. We use SQL to join data sets and transform the data, creating tables and charts that provide answers. And when we land on an answer that can be useful in the future, we scale it, building self-service reports and dashboards that allow users to retrieve the same answer over and over again in a timely manner. For my team, BigQuery helps us create a bridge between the data that exists and the problems folks are trying to solve or questions they're trying to answer. With its smooth integration with other tools, user-friendly interface, and the use of SQL for effective programming, BigQuery makes the discovery of valuable information within complex data sets simple and productive. It's an essential part of the cloud data analyst toolbox. There's tons to explore, so have some big fun getting to know BigQuery. This will be an invaluable tool for your cloud career. It's been a blast introducing you to the field of cloud data analytics. You've learned that cloud computing is an advanced and powerful computing model that resolves many limitations of traditional computing. It also addresses evolving data computing needs of people and businesses all across the globe. Cloud computing provides ondemand availability of computing resources as services over the internet which offers accessibility, scalability, cost-savings, security, and efficiency. And it frees up your time and resources so you and your colleagues can focus on the kinds of tasks that bring more value to your team and organization. You began this course with an introduction to cloud computing. You then learned about its history, current defining characteristics, and the advantages of using a cloud computing model. Next, you examined the differences between cloud computing and traditional computing, like a physical network infrastructure in traditional computing versus a cloud network infrastructure in cloud computing. You've got this. And remember to celebrate your hard work in a favorite way. A yummy snack, a comfort show, or a touchdown celebration. Hi there. Welcome to the next section where you'll continue learning about data analytics in the cloud. In this section, you'll explore migrating data to the cloud from on premises systems. And together we'll get into the differences between on premises, hybrid, and cloud data system architectures. You'll also learn a lot about the Google Cloud architecture framework throughout. You'll witness cloud's impact on data analytics and many other industries. and you'll check out strategies for cloud cost optimization and its benefits for users. You'll also explore the cost of storage, running queries, and resource provisioning along with the different billing models. This information will help guide your future employer towards the most cost-effective cloud solution for their particular needs. Meet you in the next video. Anyone who's been through a move knows that it's a lot of work. There's emptying shelves, sorting items for packing or donation, carefully boxing everything up, loading the boxes into a moving van, and then you have to unpack and get everything organized in its proper place once you get to the new place. An organization migrating its physical computing infrastructure to the cloud also requires careful planning and a great amount of effort to ensure a successful move. Fortunately, third-party cloud service providers like Google Cloud can help make everything easier. In this video, we'll discuss the process of migrating an on premises computing network infrastructure to a cloud platform. You'll learn the steps to follow cloud data migration strategies and important factors to consider during migration. All right, the first step is to think about some key factors. These include choosing the right cloud environment for your organization. Then think about how much data will be transferred to the cloud. This is important because large amounts of data can take a long time to move which can delay operations. Next, consider how much downtime your organization can deal with during migration. Obviously, no business wants to shut down their systems any longer than necessary. So, this decision should be agreed on by all stakeholders. The next step is to choose your migration strategy. Options include rehosting, also called lift and shift, replplatforming, repurchasing, refactoring, or retiring. Let's break those down. Rehosting is a cloud migration strategy that involves moving an entire on premises system to the cloud without changing anything else about the system. An exact copy of the current setup is created in the cloud which helps the organization quickly achieve a return on investment as they use the enhanced efficiency of their operations, the robust and reliable nature of the cloud infrastructure and the innovative technologies that are built into cloud-based solutions. Replatforming is a cloud migration strategy that involves making small changes to the on premises system once it's migrated to the cloud. So the main structure of the systems applications remain the same, but a few things are improved for better performance. Repurchasing is a cloud migration strategy that involves moving applications to a new cloud-based service platform. Usually a software as service platform. The cloud service will be an all-new experience. So this requires some team member training. Refactoring is a cloud migration strategy that involves building all new applications from scratch and discarding old applications. This is ideal when organizations need new features like serverless computing that their current systems don't have. Retiring occurs when applications that are no longer useful are turned off. The next step in the migration process is choosing your migration partner. As a cloud professional, you'll want to help your organization find a cloud service provider that offers the right infrastructure for your particular business, that offers valuable services and tools, and that invests in development to keep things fresh. It's also important to examine the provider's customer support and service level agreement, or SLA, so you get reliable and prompt support. Obviously, I'm a big fan of what we do here at Google, especially how we help our partners prepare for cloud migration success. We've got something called the Google Cloud Adoption Framework, which helps users assess their organization's readiness to adopt cloud technologies. This framework acts like a map from current capabilities to their ideal cloud destination. The Google Cloud adoption framework evaluates four themes. First, learn refers to the quality and scale of an organization's learning programs. Lead describes the level of support from leadership given to an organization's IT department when migrating to Google Cloud. Scale is the extent to which an organization uses cloud-based services and how much automation they need to manage their system. And secure ensures an organization's ability to protect their cloud environment from unauthorized and inappropriate access. Google Cloud provides a migration path that also has four phases. Assess, plan, deploy, and optimize. In the assess phase, users perform a thorough review of their existing network infrastructure. Then in the plan phase, users set up a basic cloud infrastructure on Google Cloud where their workloads will exist. When it's time to deploy, the workloads are actually moved to Google Cloud. Lastly, the optimize phase is when organizations begin using cloud-based technologies and features. And this is where they start to really enjoy the improved accessibility, scalability, cost savings, security, and efficiency. Like any move, cloud migration requires careful planning. As a cloud data analytics professional, the ability to help your organization follow the steps in this video will help ensure everything gets to where it needs to be so you can enjoy your beautiful new place in the cloud. Welcome to this intro to cloud deployment models. We're going to explore how to advise any organization in choosing the right environment for their unique business needs. There are three primary models. Public clouds, private clouds, and hybrid clouds. As a cloud data analytics professional, it will be important to understand how each works. Then you can help your organization select a model that's flexible, adaptable, and helps users quickly respond and adjust to changing conditions. First up, a public cloud is a cloud model that delivers computing, storage, and network resources through the internet. In this model, these resources are shared among multiple users and organizations, granting them ondemand access and utilization. Public cloud services are overseen and maintained by third-party cloud service providers who not only manage the infrastructure but also operate their own data centers. Next, a private cloud is a cloud model that dedicates all cloud resources to a single user or organization and is created, manage, and own within on premises data centers. Finally, hybrid clouds are a combination of the public and private models. They enable organizations to enjoy both cloud services and the control features of on premises cloud models. Think of these three cloud models as different ways to heat a building. Public clouds are like an electricity company that delivers the power you use to generate heat. You can choose to turn it up when you're chilly or turn it off when it's warm outside. You only pay for the power used. And as a customer, you don't need to worry about the maintenance of the power lines and generators. Private clouds are like having your own solar panels to generate power and heat. You need to buy the panels, have a place to install them, and you're responsible for their proper care and maintenance. And hybrid clouds are like using the services of an electricity company but also owning and using solar panels. This gives you more options over how heat is delivered when the temperatures drop. You can choose when the power company is right to use and when solar panels are the better option. Okay, now let's discuss the advantages and disadvantages of each model. With public clouds, you pick and choose the resources you need and pay only for what you use. Public clouds can easily scale up or down based on demand and there are no maintenance worries because the cloud service provider handles all of that for you. Another key advantage is reliability. Public clouds have vast networks of servers and can quickly redirect resources in an emergency. Speed and ease of deployment are also benefits of a public cloud model. Adoption occurs faster and more simply because the cloud infrastructure is already in place. Lastly, public clouds offer new services and frequent updates that enable users to benefit from the latest innovations like artificial intelligence and machine learning or AI and ML. Now, private clouds come with higher maintenance and management costs but offer a few critical advantages. The first is that as the name suggests, they offer private and secure networks if protected properly. Without proper security measures put in place, it can be vulnerable to hackers. Second, private clouds help with any required regulations and compliance because you control where your data is stored and where computing takes place. Private clouds also provide consistent performance because hardware isn't shared among other organizations. Going hybrid can be a bit tricky as blending public and private models adds complexity. But there are some key benefits. A hybrid cloud model allows you to add a public cloud provider to your existing on premises infrastructure, which increases cloud computing power without adding data center expenses. A hybrid cloud model also gives you access to the latest innovations like AI and ML, which can really be business game changers because you choose where your applications sit and where computing happens. There are key security and compliance advantages. Likewise, hybrid computing occurs closer to the actual users, so it enables faster performance. There's also greater flexibility because you can choose whichever cloud environment is best for the specific task at hand. Whether your organization is best suited to a public, private, or hybrid cloud model, your guidance will help choose a great option. All three offer some really cool ways to advance any organization's computing power. Keeping track of business data used to be incredibly labor intensive. From handwritten ledgers to complex filing systems to typing facts and figures into a spreadsheet, collecting, cleaning, organizing, and storing information was a huge and resource-heavy task. But cloud data analytics has automated and enhanced these processes, making data management much more efficient and less prone to human error. In this video, we'll dive into how the cloud enables data from a variety of sources to be smoothly integrated, creating a single source that users can access and analyze in real time. First, cloud data can be managed with data integration or data ingestion. Data integration combines data from different sources into a single usable data source. This integration can happen through the ETL process or extract, transform and load or the ELT process, extract, load and transform. ETL and ELT are cloud-based approaches that use the power of cloud data warehouses like Google BigQuery to transform data. ETL transforms the data before it's loaded into the warehouse and ELT transforms it after. But either way, it's ready for further processing or analysis. Data ingestion obtains, imports, and processes data for later use or storage. The information is obtained from various sources and processed through stream or batch ingestion. Stream ingestion involves real time continuous processing of data as soon as it is collected from various sources. Batch ingestion processes data in predefined intervals or larger chunks. Those are just a few of the ways cloud data analytics has transformed how organizations access their data. There are also web interfaces, application programming interfaces or APIs, SQL, other ingestion tools like PubSub and business intelligence solutions like Looker and Jupiter notebooks. All of these help users access data that's stored in the cloud anywhere, anytime. And while we're discussing data in the cloud, cloud data analytics also makes it possible to store different types of data like files, objects, or blocks. File data is information that's stored in a file on your computer or another storage device. Object data is a piece of information with a unique identifier which you can find no matter where it's stored. And block data is a piece of information that has been cut from a larger piece of information and given its own file path. So many data analysis activities have greatly benefited from cloud data analytics processes. Big data analysis, the ability for visualization of multiple data sources asynchronously, AI and ML, custom report analysis, data mining, data science, the list goes on and on. In today's world, innovation is the driving force for many companies. And there's no doubt that data fuels these innovations. It's really amazing how much cloud analytics has advanced the field of data analytics, making powerful analytical tools and processes available to organizations of all kinds. At the same time, it enhances the analytics process, making it easier, faster, and more cost-effective for users to discover valuable insights from data. Hello, Future Cloud Pro. Thanks for being with me for this rundown on the key features affecting cloud costs. Resource provisioning, storage, and running queries. Let's start with an example. Say you're headed to the market, so you create a shopping list. You think about what you'll need in the coming week, then write down exactly those items. The list helps ensure you don't overspend on items you don't need or that might go bad later. Well, managing the cost of cloud data analytics is kind of like that. The key is to be a super savvy shopper, knowing exactly what resources you need and how much. This saves money and prevents waste. The first method cloud professionals use to achieve these goals is resource provisioning. This is the process of a user selecting appropriate software and hardware resources and the cloud service provider setting them up and managing them while in use. The resource provisioning process occurs through one of three delivery models. Advanced provisioning, dynamic provisioning, and self-provisioning. Each delivery model is different based on the types of resources an organization buys, how and when it receives these resources, and how it pays for them. In advanced