Google Cloud Platform (GCP) - How do I choose among the Compute options? IaaS, PaaS, CaaS/KaaS?

Google has made their Cloud Platform (GCP) so that you can host your application any way your business requires. When we talk about the traditional Data Center, we tend to distinguish 3 types of "resources":
  • Compute
  • Storage
  • Networking and Security

In each of these 3 areas, GCP offers you plenty of options. Don't be naive though, you will need to know the options quite good in order to optimize your performance and costs. In this 3-part Blog Post I will go into each of these 3 in detail, and hopefully help you with your decision.

Let's start with the Compute options. There are 3 options to choose from. You can go for the Google App Engine, or a PaaS option, focus on the code and let Google handle everything else, use a GCP to simply deploy your VMs (or Instances as they call it) the way you like, or you can choose the Containers. My idea is to try and explain each of the options in a bit more details. If this is something you'd be interested in - keep reading.

What are IaaS and PaaS?

Let's start with a simple question: What are IaaS (Infrastructure as a Service), and PaaS (Platform as a Service) and how are they different from a traditional On-premise/Data Center model? Back to basics - what does our application need in order to run? Let's start from the bottom of the Application Stack:
  • Networking, to be reached, and to reach data it requires to operate. We need Switches, Routers, etc.
  • Storage, to store data. We need Disks, Storage Cabins, SAN switches.
  • Servers, to store the compute loads. Physical Servers, with RAM, CPUs etc.
  • Virtualization, to optimize the usage of the Physical Resources by using the VMs (Virtual Machines).
  • Operating System
  • Middleware
  • Runtime
  • Data
  • Applications

In the On Premise architecture, it is on us to (over-) provision and manage all these resources. Wouldn't it be great if someone would provision and manage some of the "basic" layers for us, so that we could focus on the part that actually matters to our business? THIS is what it's all about. I like how AWS defines this - in IaaS, the Cloud Provider takes care of all the heavy lifting, or as they call it - Undifferentiated Services, while you handle the services on top, that make your business different from your competitors.

Now, check out the following diagram, to see what exactly is managed by the Cloud Provider, and what is managed by You, in the case of IaaS, PaaS and SaaS.

*In some examples out there, in IaaS OS is partially managed by You. This pretty much depends on the model that Cloud Provider is offering.

What Compute options does GCP offer?

There are Compute options for hosting your applications in Google Cloud. You can use one of those, or Mix and Match:
  1. Google Cloud Functions (currently in Beta). It's a Serverless environment for building and connecting other cloud services. Very simple, very single purpose functions, written in JavaScript, executed in Node.js. Cloud Function executes a response to a TRIGGER event. Functions are not exactly a Compute option, but they do match this use case, so I'll just keep them here.
  2. Google App Engine (GAE), is the PaaS option, serverless and ops free. It's a flexible, zero ops, serverless platform for highly available apps. You would choose GAE if you ONLY want to focus on writing code. It can be used for the Web sites, Mobile apps, or gaming backend, and also for IoT. Google App Engine is a MANAGED SERVICE, meaning - you NEVER need to worry about the infrastructure, it's invisible to you. There are 2 available environments: Standard (predefined Runtime) and Flexible (configurable Runtime). We will get into these in more detail.
  3. Google Kubernetes Engine (GKE), is the CaaS/KaaS (Containers/Kubernetes as a Service) option, clusters of machines running Kubernetes and hosting containers. Containers are the auto-contained services, containing all the Libraries and Dependencies, so that you don't have to worry about the Operating System at all. GKE engine allows you to focus on the Applications, not on the OS. You should use it to increase velocity and improve operability by separating the application from the OS. Ideal for Hybrid applications.
  4. Google Compute Engine (GCE) is the IaaS option, fully controllable down to the OS. We're talking about Instances of VMs. You should use it if you have a very specific requirements from your operating system, or if you need to use the GPUs (yes, this is the only option that let's you add Graphical Processing Units for intensive compute tasks to your Compute resources).

There is also a fifth option called Firebase, and it's specific for Mobile, but I won't go into that right now. Instead, let's focus on each of the four options mentioned above. Each of these options can be used for any application, and it's on you to choose the one that best fits, each one has their Pros and Cons. Yes, you can mix them in the same application! Check out the following diagram, to get a clearer picture:

Google Cloud Launcher

Before we get into more detail about the Compute options, I'd like to cover the Cloud Launcher, one of my favorite tools in the GCP. Google Cloud Launcher can help you set up an easy app, such as WordPress or LAMP stack, in a few minutes. You can customize your application, because you will have full control of your instances. You will also know more or less how much everything will cost before you deploy it all. Remember this for now, because I will be mentioning the Launcher later.

Google Cloud Functions

Floating, serverless execution environments, for building and connecting the cloud environments. You would be writing simple, single-purpose functions. When an event that is being watched is fired - Cloud Function is triggered. You can run it in any standard Node.js runtime. This would be a perfect option for the coders that like to write their applications in functions.

Google Application Engine (GAE) - PaaS

PaaS option is a perfect option if you just want to focus on your code, and you trust Google to manage your entire infrastructure, including the Operating System. It tends to be very popular with SW, mobile and Web developers. If you prefer to pay per use, and not per allocation, you might prefer PaaS (No-Ops) to IaaS (DevOps) option. Also, there's no vendor lock-in, you can easily move your Apps to another platform because everything is built on the Open Source tools. App Engine is REAGIONAL, and Google makes sure that you have the HA using different (availability) zones within the region.

Can you use GAE in Multiple Regions? You cannot change the region. Your app will be served from the region you chose when creating the app. Anyone can use the app, but users closer to the selected region will have lower latency. More details:

App Engine supports ONLY HTTP/S.

GAE is super easy to use. You will basically need to create a new Folder, store your files in there, and execute the command "gcloud app deploy". That's it!

There are two environments, depending whether you can customize an OS:
  1. Standard (deployed in Containers), preconfigured with one of the several available runtimes (specific versions of Java 7, Python 2, Go, PHP). Each runtime includes the standard Libraries. Basically this is a container - Serverless. Your code is running in a Sandboxed environment.
  2. Flexible (deployed in VM Instances, based on GCE), that you can customize into a non standard environment, and you can use Java 8, Python 3.x, .NET, also supporting Node.js, Ruby, C#. This is not a container, it's a VM of a compute instance, and you are charged based on the usage of the VM instance (CPU, memory, disk usage) that's been provisioned for you. Unlike on GCE, the instances are automatically managed for you, meaning - regional placement, updates, patches and all (root SSH disabled by default, but can be enabled).

IMPORTANT: Scale up time is measured in seconds in Standard environment, and in minutes in the Flexible environment, simply because the containers are much faster then the VM instances.

Google Compute Engine (GCE) - IaaS

Google Compute Engine should be used when you need IaaS, for example, you need to tune your Load Balancing and Scaling. When you create a VM instance (each instance needs to belong to a Project, and a Project can have up to 5 VPC - Virtual Private Networks), you need to choose the Machine Type, a Zone, an Operating System (Linux and Windows Server are available, you get root access and SSH/RDP enabled). You can choose one of the following Machine Types, but have in mind that in order to later change it you need to stop the instance, change it, and then turn it back on:
  • Standard
  • High memory
  • High CPU
  • Shared core (small, non resource intensive)

Compute Engine instances are pay-per-allocation. When the instance is running, it is charged at an per-second rate whether it is being used or not. I'd also like to use this section to clarify a few important concepts related to GCE:
  • What is a PREEMPTABLE instance?
  • How does Google Maintenance affect your workloads?
  • How do I automate instance creation?
  • What Disks can I assign to my Instance?
  • Which VMs and Images are available for me, and can I qualify for discounts?

What's a Preemptable VM instance?

A type of VM instance that can be deleted with 30 second notification time, once the SOFT OFF signal is sent (best practice: you need a SHUT DOWN SCRIPT, able to shut the instance off and do all the clean-up in less then 30 seconds). It's much cheaper, of course, because it can be deleted AT ANY TIME (at least once every 24 hours). It can, for example, be used for the fault tolerant applications.

How does Google Maintenance affect your workloads?

Google can shut down your machine for maintenance. You can configure what to do in this case, migrate or terminate. This is your call, as it directly depends on the nature of your application, and whether they are Cloud Native (instances treated as a Cattle, rather then as Pets. Confused? Read my previous post for clarification).

Live Migration allows an instance to be up and running, even in the maintenance state, or during a HW or SW update, failed HW, network and power grid maintenance etc. The instance is moved to another host in the same zone. VM gets a notification that it needs to be evicted. A new VM is selected for migration, and the connection is AUTHENTICATED between the old and the new VM.

When a Live Migration is executed there are 3 stages:
  1. Pre-migration brownout: VM executing on the source, when most of the state is sent from source to target. The time depends on the memory that needs to be copied and similar.
  2. Blackout: a brief moment when none of the VMs are running.
  3. Post-migration brownout: a VM is running on the destination/Target Host, but the source VM is still not killed, ready to provide support if needed.

  • Preemptable instances cannot be live migrated.
  • Live migration cannot be used for the VMs with GPUs.
  • Instances with the local SSD can be live migrated.


How do I automate instance creation?

To AUTOMATE the instance creation, you can use the gcloud command line. One of the options is for example to assign a LABEL to instances you want to group (called Instance Group) in order to monitor or automate. You can get the exact script to, for example, create an instance, from the graphical interface, just look for the API and command line equivalents. Yes, this is awesome, you can literally get an API for any graphical interface action you take. Automation made easy, good job Google!

DevOps tools are also available (GCP equivalents for some), which is great if you have strong DevOps skills in the house:
  • Compute Engine Management using Puppet, Chef, Ansible.
  • Automated Image Builds with Jenkins, Packer and Kubernetes.
  • Distributed Load Testing with Kubernetes.
  • Continuous Delivery with Travis CI.
  • Managing Deployments using Spinnaker.

What Disks can I assign to my Instance?

You also have loads of Storage options for your instances. I won't go into the storage options here in detail, but to create a Disk for your VM instance you have 4 options:
  • Cloud Storage Bucket, as the cheapest option.
  • Standard persistent disks (64 TB).
  • SSD persistent disks (64 TB).
  • Local SSD (3 TB), actually attached to the instance, in the same Server.

Which VMs and Images are available for me, and how do I qualify for discounts?

Images help you instantiate new VMs with the OS already installed. There are Standard and Premium Images, depending whether you need some kind of license, like for RedHat Enterprise Linux or MS Windows. You should have in mind that you have 2 possibilities to get your image ready to launch:
  • Startup Script, that you need to write in order for it to download your dependencies, and prepare everything. It needs to always bring the VM in the same state, regardless how many times you execute it.
  • Baking is a more efficient way to create an image in order to faster provision an instance, much more efficient then a Startup script. You would start from the Premium image, and create a Custom instance (sort of a Template, if you will). Baking takes much shorter to provision an instance then a Startup disk. Everything is included into the "baking image". Version management and rollbacks are much easier, you can just rollback an image as a whole.

Check out this link about Google Cloud pricing for more details.

In the image lifecycle the possible statuses are: CURRENT, DEPRECATED (can still be used and launched), OBSOLETE (cannot be launched) and DELETED (cannot be used). This should give you some idea about how you would be managing your instance versions.

  • Snapshots can only be accessed within the same project.
  • All machines are charged for at least 1 minute. After that, a per-second payment is applied. The more you use the VM, the more discount you get.

Before we get to the possible discounts, you first need to choose your machine type correctly, to optimize the cost and the performance:
  1. Pre-defined
  2. Custom: You can specify the number of vCPUs and Memory. You would start with one of the pre-defined, and if you see that your CPU or memory are under-utilized, customize it.
  1. Shared-core is another option, meant for small, non resource intensive applications, that require BURSTING.
  1. High Memory Machines: more memory per vCPU, 6.5GB per Core.
  2. High CPU Machines: more vCPU per unit of memory, 1.8GB per Core

Google offers a few types of discount/price optimization, among others:
  1. Sustained use, when you use a VM for a long period of time
  2. Committed use, that you can purchase in 1 year or 3 year contract, and you get a good price.
  3. Rightsizing is a feature recommends which size of the VMs to run after analyzing your application behavior. This is a brand new feature, and it relies to the Stackdriver collected information from the past 8 days.

Google Containers/Kubernetes Engine (GKE) - CaaS/KaaS

If you have lots of dependencies, you would of course benefit most using the Containers. Container is a light weight standalone executable package that includes everything needed to run it: code, runtime, system tools, system libraries, settings. Containers de-couple the Application from the Operating System, and they can reliably run on different environments. Different containers run on a same Kernel, as presented in the picture below, taken from the Dockers web page:

Container vs VM

A VM contains an entire operating system packaged along with the application. A container ONLY runs an OS Kernel and nothing else, it contains the Application and the essential Libraries, Binary files etc., and it can easily be moved from one Physical or Virtual machine that has the Kubernetes engine, to another. Containers are much faster, as there is no OS to boot, and they are much smaller in size.

To be precise, using Containers/Dockers we can achieve:
  • Process isolation
  • Namespace isolation
  • Own Network Interface
  • Own Filesystem

Meanwhile, when we say a Micro service, that simply means that one container = one process.

What is Kubernetes?

Kubernetes is an open source Container Manager, originally created by Google for it´s internal use. Kubernetes automates Deployment, Scaling and Management. This means that using Kubernetes you can:
  • Rollout new features seamlessly
  • Auto scale your application
  • Run your application in the Hybrid environment, as long as you have the Kubernetes Engine in your VMs.

Why is Kubernetes so important here? Because Google Kubernetes Engine uses Kubernetes as a Container Management engine.

Let's first check out the important components of the Kubernetes architecture:
  • A Container Cluster has one supervising machine running Kubernetes (Master Endpoint, or Master Instance works like Hadoop Cluster Manager). Kubernetes Master manages the cluster, and it's your single point of management of the Kubernetes Cluster.
  • Master Instance will be in touch with a number of individual machines using a software called Kubelet, each running Docker.
  • Each individual machine running Kubelet is known as a Node Instance.
  • Pod is a smallest deployable unit, a group of 1 or more containers in a Node. Inside each Pod in every Node Instance, Containers are running. Pod has it's settings in a Template.
  • Replication Controller ensures that specific number of Pod replicas are running across Nodes.
  • Services are the abstraction layer that decouples the frontend clients to the backend pods. They define the LOGICAL set of pods across nodes and the way of accessing them. Load Balancing is one of the Services, creating an IP and a port as a connection point to our Pods.
  • Label is a METADATA with semantic meaning. It's used for selecting and grouping the objects.
  • scheduler is in charge of scheduling pods onto nodes. Basically it works like this: You create a pod,
scheduler notices that the new pod you created doesn’t have a node assigned to it, and assigns a node to the pod. It’s not responsible for actually running the pod – that’s the kubelet’s job. So it basically just needs to make sure every pod has a node assigned to it.
  • kubectl is a CLI tool for Kubernetes.

Google Cloud Engine includes the following components, most clarified in the Kubernetes architecture:
  • Container Cluster, includes a Kubernetes Master and Compute Engine instances where Kubernetes are running, managing all the components with Kubernetes Master.
  • Kubernetes Master, as a single point of management of the cluster.
  • Pods, as groups of containers.
  • Nodes, as individual Compute Instances.
  • Replication Controller, ensuring the defined number of Pods are always available.
  • Services, decoupling a frontend client from the backend Pods, providing a Load Balancer with a single URL to access your Backend.
  • Container Registry is the image repository, so that you can deploy container images

Why GKE, and not Kubernetes on GCE?

This all depends on what exactly are your needs. You can use CaaS by Google (GKE), which is easier out of the box, and Google would manage the entire "Undifferentiated" application stack, up to Containers. You can also build your own Container management on top of Googles IaaS (GCE), for example if you need GPUs, or you have some specific OS needs, or maybe a non-Kubernetes container solution, or if you are migrating your existing on premise Container solution.

Before you make a decision to, for example, run Kubernetes directly without something like GKE on top of it, I strongly recommend you to investigate the following GitHub link, on implementing Kubernetes without the pre-defined scripts:

If you use containers, the best way would be to use DevOps methodology, and Jenkins for CI/CD. You can use Stackdriver for logging and monitoring.

Storage options for GKE are the same like with the GCE, but Container disks are ephemeral (lasting for a very short time), so if you do want your data not ephemeral, you need to use an abstraction called gcePersistentDisk.

When would you use GKE instead of GAE?

GAE only supports HTTP/HTTPS, so if you need to use any other protocol - you would go for CaaS rather then App Engine.  Also, if you are using a Multicloud environment, GAE only works on GCP. App Engine doesn't use Kubernetes, so if you want to use Kubernetes - you would also rather go for GKE.

Interesting fact: Pokemon GO was deployed on GKE (50x more users connected then expected), while Super Mario Run (launched at 150 countries at the same time) was deployed on the GAE.

Need some help choosing?

If it's still not clear which is the best option for you, Google also made a complete Decision Tree.

Big Data for Infrastructure Engineers: What is Hadoop?

Before we start, let's make sure we know what we're talking about here. A few concepts need to be clarified:
  • OLTP vs OLAP
  • Big Data
  • Machine Learning


We can divide IT systems into transactional (OLTP) and analytical (OLAP). In general we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it. Taking this into the Data Bases world, we have:
OLTP - On Line Transactional Processing, normally handled by Relational Data Bases
OLAP - On Line Analytical Processing (Business Analytics and advanced data processing), requires a Big Data technology, like Hive. Business Intelligence (OLAP) refers to the generation of reports which may or may not involve sophisticated tools like Cognos or Business Objects.

Big Data

Big Data tends to refer to the extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Machine Learning

Machine Learning, or as I would call it BI 2.0, does exactly as its name suggests. Enabling the Machine to learn from the data. Any forward looking activity, i.e. whenever the term “Predictive” kicks in, you can expect Machine Leaning to be there.  At the same time, it can also be used for Business intelligence. Example: predict orders next week, identify a fraudulent insurance claim, power a chat-bots to provide L1 support to customers and so on. Machine learning has wider applications. It can be leveraged to power businesses as well. And since business generates lot of data, it makes a easy ground for machine learning. The idea is always the same:

How does the Machine Learning work? Simply get a bunch of Data, proceed to the "Training" analyzing the data, and get the conclusions (Model) in order to later be able to make Predictions, applicable to the data that hasn't been used for training.


Have in mind that Model is a "living" thing, as the new data is continuously being brought in, the Model is being continuously improved.

What is Hadoop?

Relational Data Bases or RDBMS (Relational Data Base Management System) are not optimized for the needs of Big Data and Machine Learning. We need a File System, that permits us to scale horizontally, and allows us to perform BA. Enter - Hadoop. Hadoop is based on the Distributed Computing principles, meaning - lots of cheap hardware, and prepared for horizontal scaling, unlike Monolithic Computing, where you'd rely on a single Super Computer. To get the naming right, remember that Hadoop Clusters are composed of Nodes, that run in Server Farms.

Simply put, Hadoop = HDFS + MapReduce (+ YARN). Let's now demystify this… We have 3 components:
  • HDFS, or the Hadoop File System
  • MapReduce, for data representation, using Java
  • YARN, in charge of Replication and Clustering

HDFS: Lots of cheap hardware where the Distributed Computing is "stored". Google File System was created to solve the Distributed Storage problem, and Apache developed the Open Source version of this called HDFS. It's not optimized for low latency, but it has an "insane" throughput.

In HDFS we will have a bunch of cheap servers, with the Hard Drives. Each of these will be known as a Node, and there will be one Master Node, called the Name Node, containing the Metadata for all the other Nodes, known as the Data Nodes. Name Node knows where the stuff is, but the Data Nodes contain the Data.

HDFS works as a Block Storage, because the large files are separated in many same sized (128MB) Blocks, and stored in different Data Nodes. Each node contains a partition of a split of data. Name node has the "Table of Contents" where different Block locations are documented. For example, Block 7 is in DN4, as in the diagram below.

How is HA handled? Using the replication factor, meaning that every Block is stored in more then one Data Note. Name node needs to keep track of these. A Replication Strategy handles that the Replicas are stored in an optimal way, to optimize Bandwidth.

Since I'm currently preparing for the Google Cloud Architect exam, I've been investigating how Hadoop as a Managed Service is handled on the GCP (Google Cloud Platform). Dataproc is Google's managed Hadoop, which let's you not worry about the Replication or Name Nodes. Google Cloud Storage is used on the GCP instead of the HDFS, because if you followed the model with the Name Node, such as Hadoop, the Instance (VM) with the Name Node would be spending an insane amounts of resources. In Google Cloud Storage this is optimized without the Name Node (not going into details here).

YARN (Yet Another Resource Negotiator): a Resource allocator that lets us do Replication and Fault Tolerance. YARN coordinates the cluster, using two components:
  • Resource Manager, on a master node
  • Node Manager, running on all other nodes. This is actually a container, isolated from everything else on the Node.

MapReduce: Abstraction that allows any programmer to present the data in the form of Map and Reduce jobs, and enable the Distributed Computing. The role of MapReduce is to handle a huge amounts of data. It takes advantage of parallelism. Every step is done in two functions.
  1. Map operations: Express what the body needs to accomplish. Runs in parallel on many of the machines in the cluster.
  2. Reduce operations: Distribute all the results of Map operation, and create a final output, storing it into all the Data nodes, and their execution will happen in parallel.

This is all (map and reduce operations) written in JAVA, and Business Analysts don’t do JAVA. This is why an SQL interface, provided by Hive (or by BigQuery, in GCP) is so important and popular.

How does Hadoop work?
  1. User defines the Map and Reduce tasks using the MapReduce API
  2. A Job is Triggered, MapReduce communicates it to YARN
  3. YARN decides the Resource allocation model, and communicates it to HDFS.

Hadoop Ecosystem

Hive (along with Spark), which is basically the same as Google BigQuery on GCP, provides an SQL interface to Hadoop. BigQuery uses a columnar format called Capacitor. Hive is great for High Latency applications (BigQuery doesn't have as high latency as Hive, it can even be used for almost real-time applications).

Hive runs on top of Hadoop, and stores it's data in HDFS. HiveQL is an SQL type language, familiar to analysts and engineers. SQL is optimized for Relational DBs, and HiveQL for Hive. Hive will TRANSLATE the queries written in SQL in HiveQL into MapReduce. A Hive user sees data as if it were stored in Tables.

Comparing to Relational DB, Hive is meant to be used for LARGE datasets (Giga or petabytes), Read operations to analyze the historical behavior, with Parallel computations (need more space - add more servers, in accordance with Horizontal Scaling philosophy) enabled by MapReduce (relational DB runs against one really powerful server), and remember that Hive is designed for the High Latency use, mostly for Read operations. Relational DB was designed for Low Latency, quick SQL consults, Read and Write operations.

Hive uses a so called Bucketing segmentation. Partitioning is designed for a non equal data segments. Bucketing is designed to evenly distribute data. Since in HDFS the Blocks are 128MB each, which is why this concepts fits the Bucketing perfectly.

Hbase, which maps directly to Google BigTable provides a management system on top of Hadoop. It integrates with the Application much like a traditional database. Hbase and Bigtable are columnar data bases, and they are designed for the low latency use.

Pig - A data manipulation language. It transforms the unstructured data into a structured format. You can query this structured data using Hive. Included in Google DataFlow.

Spark - A distributed computing engine used along with Hadoop. Spark acts as an interactive Shell to quickly process Datasheets. It completely abstracts away the MapReduce complexity in data transformation. You can use Spark if you want to use Scala or Python to operate HDFS and YARN. Spark has a bunch of built in Data Libraries used for Machine Learning, stream processing, graph processing etc. Included in Google DataFlow.

Kafka - Stream processing for unbounded datasets. Kafka takes streaming data from sources and distributes to sinks. Google Cloud used a Google Pub/Sub instead of Kafka.

Oozie - a workflow scheduling tool on Hadoop.

I hope this helps understand the basics of Hadoop ecosystem and Big Data. Stay tuned for more posts on how GCP is handling Big Data.

Google Cloud Architect, why and how to prepare for the exam

Why Public Cloud?

In the last many years I've been strongly focusing on the Cloud. I have to admit though that due to various conversations with my customers, Public and Hybrid models have been getting more and more attention, and I can see how when I hear the problems of their businesses - Public or Hybrid Cloud is the answer! So, why aren't they all moving their services and applications to the cloud yet? 3 reasons actually:

  1. Their applications are not Cloud Native. No, you can't just migrate your VMs from VMware to the Public Cloud and say that you're running a Cloud Architecture, you need to kill your pets and grow your cattle (google "Pets vs Cattle" if this sounds like a complete nonsense, and then check out the diagram below).
  2. They have prejudice about Security and Speed of the Cloud. This is partly true... but Public Clouds are continuously improving their infrastructure. Google has invested a LOT of money in Security and High Speed Networking (global mesh redundant FO network). Most companies cant match this.
  3. They're a bit scared of the horror stories of insanely large bills that some of the pioneers got when they moved to cloud. This just means that you need to be well informed what and how you want to use, you need to plan your application migrations, and have a managed service capable of optimising your costs, performance and price wise.

Why Google Cloud Platform (GCP)?

With all this in mind, a few months ago I started my path to get certified in Google Cloud Platform as a Cloud Architect. Yes, I know that AWS and Azure are the current market leaders, but let me tell you why my money is on GCP:
  • Google's Cloud is the clear winner when it comes to compute and storage costs.
  • GCP provides a better approach to discounted long-term usage: Instead of requiring users to reserve instances for long periods of time as AWS does, GCP will automatically provide discounts the longer you use the instance - no reservations required ahead of time. 
  • With 5 years head-start, AWS offers a lot more cloud products and options. Even though GCP doesn't offer as many services (at the moment), I have a strong feeling that it's a sleeping giant, and I want to be there when it wakes up!
  • Google is also pioneering efficiency, 100% carbon neutral since 2007, 50% less energy use then the typical DC.
  • Last, but not the least, Azure is tied to the MS customers, AWS has a somewhat closed system of it's own (yes, I know they started using Kubernetes, after a long period of resisting the market),  and GCP gives the feeling of a most opened platform, which is extremely important when it comes to the Hybrid IT.

How did I prepare for the exam?

CURRENT STATUS: PASSED!!! Find the details here.

Step 1: Get skilled

I passed quite a few difficult exams during my professional career, including Cisco's CCIE and VMware VCIX, so I kinda know what I should be expecting. Google Cloud Architect is a relatively new exam, so there is not enough feedback on the courses that I've found online. I did a few GCP courses, just to get into the "rumbo", and let me tell you what I've found:

Blueprint: First I went to Google's official page, and scheduled, read a blueprint, all the documentation, and got an idea how the exam is. You can read it all here.

Then I simply went ahead and scheduled an exam 3 months ahead of the starting date, to give my self motivation to work harder on it.

GCP Account: I opened an account at GCP, and started playing around. Google gives you 300$ to just "enjoy the experience" for 12 months. Don't be lazy, I was amazed with how smoothly it works. There are projects in BETA, some are pretty mature, check out the Compute, Container and App Engine, see what the Google Launcher can do (use this one!).

Udemy: Google's official documentation is complete, but insufficient. I went to Udemy, and purchased the 26 hours and 60 Demos "GCP: Complete Google Data Engineer and Cloud Architect Guide", that you can find here.  This cost me around 20 euros. The Data Engineer part was also important for me, as GCP relies heavily on Data Bases, and I must admit - having spent most of my career in a Data Center with the infrastructure, I'm not really a Data Base expert, so I had a lot to learn. This course was Ok, not too long, and even though it's not the best quality - I'd recommend it as a starting point.

Google Cloud Next '17: Go to the YouTube channel, and just watch all the videos from Google Cloud Next that sound interesting. There's a bunch of great material there.

Linux Academy: These guys have a really high quality courses, and I strongly recommend you to purchase 1 or 2 months of subscription (49$ a month), depending on how much time you can dedicate to studying weekly, and get all 3 courses done. I did it, and I think once I did, I really got the feel of what GCP is all about:
  • Google Cloud Platform Architect, Part 1
  • Google Cloud Platform Architect, Part 2
  • Google Cloud Platform Architect, Part 3

Read: There are so many Blogs, Videos and Materials out there about GCP. Google it, read it, have your opinion, read the comments, that way you'll get the feeling of how the platform has been evolving.

Reflect: If you check out the below diagram, taken from one of the better blogs out there, and you're 100% clear about all the flows, you're good to procede with the Step 2.

Step 2: Tell people you're preparing for the Exam

You can Tweet, Blog or just comment it to few of your colleagues. Having in mind that they'll ask you how the exam went will make you take it more seriously, because in the world of preparing for the exams, the excuses are the low hanging fruits.

Step 3: Build something

Even though the exam is theoretical, you will be given actual use cases, so you do need a hands-on experience. I went and started building a Wordpress project for my newest personal site - a Mat's Cloud Wiki-like Hybrid Cloud knowledge base. I'll publish it as soon as I get something nice.

Step 4: Practice

Do a MOCK lab, do bunch of practice labs that you can find online (for example, check this out), be sure you understand all the technologies.

Step 5: Pass the exam!

Why is Hybrid Cloud so important? Cisco Cloud Blog

As you probably know, CiscoLive 2018 was celebrated in Barcelona in January 2018, and I had the pleasure of speaking at 3 sessions, two about ACI Anywhere and Hybrid Cloud and one about IoT. My focus was on how Cisco products can help you with these, products such as Cisco ACI (and ACI Anywhere), Cloud Center, Intersight, Tetration etc. The session recordings are not public yet, but as soon as they are I will be publishing them here.

In the meantime I'm glad to announce that I've been officially registered as Blogger on Cisco Blog, and I'm giving you the link to my post on the topic of why Hybrid Cloud is so important:

I hope you like it.

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