Throughout the past couple of years, I have helped a good number of companies, big and small, migrate their systems to the Google Cloud Platform (aka GCP). During the course of these migrations, there are always a few of those moments where people look at a specific Google Cloud feature and say, “now, that’s cool!”.

More often than not, it is because, coming from other platforms, they have gotten used to some features requiring multiple steps, or some operations being complicated, etc. And often they find out that in GCP you can do this specific operation in a couple of clicks, or by setting up a simple text-based configuration. Then you see that light bulb turning on in their head, and there you go… happy customer.

A few of these happen so often that I compiled them in a list to share with others who might also benefit from these “aha!” moments. You could say these are the five things I wish they told me when I started using Google Cloud.

Projects: Naturally Group Resources Together

A project is a namespace where resources live. Every resource you instantiate in GCP, from load balancers to Kubernetes clusters to virtual machines, belongs to a single project, and has no access (by default) to resources in other projects. User roles and authorisations can be defined per-project and trickle down to everything in it. This has two immediate benefits: you can group things that belong together in neat logical units, and things that don’t belong together are isolated from each other (and isolation is a Good Thing)

This is powerful and quite simple, but it often takes new users off-guard. I’ve had many clients call me and ask me “How can I make sure my developers cannot access the production machines? What’s the best way to create access policies? ”

The answer to this is actually super-simple:

• have a project for development where your developers have rights,
• have a project for production where they don’t.
• That’s it.

Every machine/other resource in the production project won’t be accessible to developers.

Of course there is a lot more to it, and you can refine roles and permissions to a much greater degree using Organizations, Folders, etc. Not to mention all the crazy things you can do with per-project billing. But at least you can say “hey, if it’s a machine in the staging environment then it can be found in the “staging” project”.

Global Virtual Networks Are *Truly* Global

Imagine you are using a Cloud provider and that you have servers in the US, and servers in Singapore, and that they need to communicate.

So you create a VPC (Virtual Private Cloud) network in the US data center, another one in the Singapore data center, and then you will connect them by setting up inter-region VPC peering or a VPN (Virtual Private Network) or a transit VPC or other routing magic.

Lots of work, right? And many moving parts, so lots of opportunities for things to break.

With GCP, however, what makes my clients go “aha!” is when they realize that in GCP a single VPC network covers the entire planet. Only subnets are attached to a geographic location, and virtual machines communicate between subnets on private IPs (good old RFC1918 addresses) — no extra routing needed.

So, to make your server communicate across continents on GCP, here are the steps:

• create a VPC network
• create a subnet in the US, put your US servers in it
• create a subnet in Singapore, put your Singapore servers in it

That’s all there is to it. Your VPC network spanning 2 continents is ready to use. Below is a screenshot of how it looks on my account, for a VPC network called ‘my-global-network’ with 2 subnets. The first column (“us-central1” and “asia-southeast1”) contains the name of the GCP regions (read: data centers). The second column is the subnet name that I picked when I created them.

A machine in the US (on the “us-central” subnet) with IP 10.0.0.5 can communicate directly with a machine in Singapore (on the “singapore”) subnet with IP 10.10.0.8.

Nothing else to set up.

And thanks to the way these networks work, the Google Cloud Load Balancer can present a single IP to the world, and forward traffic to the instances that are the closest to you geographically without having to setup a tedious DNS-based load balancing. But that’s worth an entire blog. I’ll save it for another day.

Firewalls with Tags and (Almost) No IP Addresses

There is no network security without a firewall so unsurprisingly GCP comes with one built-in.

Now, I don’t know about you, but nothing makes my brain hurt like a list of firewall rules displaying IP ranges and addresses and ‘Allow/Deny’ directives. It looks a bit like this:

If you imagine a normal network with a few dozen (hundred?) servers, you can quickly see how this can get out of control. You’d better have a solid printout of your network layout to refer to when you start adding and changing rules. And good luck debugging things!

Wouldn’t it be nice if, instead, you could just tell the firewall: “the HTTP traffic from outside can only reach the HTTP servers and the MySQL database is only reachable by the HTTP server(s) on the same network?”

Turns out it’s pretty simple on GCP by using a little thing called network tags. As the documentation says:

“Network tags are text attributes you can add to Compute Engine virtual machine (VM) instances. Tags allow you to make firewall rules and routes applicable to specific VM instances.”

So let’s see how it works. Firewall rules in GCP are defined in terms of source and target (the traffic flows from the source to the target). You can define filtering rules that apply to the source or the target, and in both cases you can use tags.

This is simpler shown with an example. The rule below states that on the default network, the traffic to the VMs with the tag mysql-server can come from the VMs with the tag http-appserver. Any other traffic is “Deny”-ed by default.

All you have to do is to tag your machines properly, and they will automatically be covered by the rule. You don’t need to enter their IP range.

That’s neat if you ask me. It makes it a lot simpler to grasp what’s happening.

Of course, there’s a TON more to firewalls in GCP. Tags also apply to routes and you can mix and match IP-based rules with tag-based rules. Not to mention that thing called service accounts, but I’ll leave those for another day.

The bottom line is that you can create most rules by just expressing a business need and not having to remember complicated network layouts. I have no hard stats, but I’m pretty sure this has saved me hours of work.

Console Access to VMs from the Browser

Easily access virtual machines (VMs) from the Google Cloud console was one of my first “aha!” moments when I started using GCP.

This is a screen capture of my Google Cloud console, with a virtual machine and its internal IP.

The last column has a header that says “Connect” and when you click on the word “SSH” a separate windows pops up. You wait for a few seconds, and… this is what you get. Your personal shell access — in a browser popup no less.

You are connected through ssh to the virtual machine of your choice. You did not have to download ssh keys and put them in the ~/.ssh directory, do the correct chmod command and run a long-winded ssh -i ~/.ssh/somekey me@<it-took-me-forever-to-copy-paste-the-address-here>

In addition, you have access to a few nifty features such as uploading and downloading files, changing the user etc. Just use the menu behind the cog icon at the top right.

In truth, you should not need to connect directly that often, but when you have to, this is a godsend.

Your Personal Jumphost from the Google Cloud Console

The Google Cloud console has a cool trick: you can actually connect to a virtual environment that is managed by the Google Cloud console itself. It serves a bit as a jump host. You can access most resources from the projects from it, and you can activate it directly from the top menu with, no particular setup on your side. It’s called the Cloud Shell.

This is how it looks at the top right of the console:

When you activate the Cloud Shell, the session opens at the bottom of the console. You get a command line prompt and it’s fully configured with the gcloud command line tool (the jack-of-all-trades of Google Cloud scripting).

You can do a great many things from there, and this even includes uploading and downloading files, editing code or deploying it, a web preview for your AppEngine application, and more.

So you can get access to a fully configured shell environment in your project from any laptop where you can connect with your credentials. On top of this, it persists between connections so you can fine-tune it to your needs and have these changes available the next time you re-connect.

This has saved me many times during my previous life as a traveling consultant!

Live migration

Did I say 5 “Aha!” moments ? Well, you’ve been patient reading all the way to here, so here’s one more for free.

Google Cloud has an amazing way to literally “teleport” a running virtual machine between physical hosts without stopping it. It’s called Live Migration. It allows Google to move your virtual machine away from a defective host, or a host that needs a patch or an upgrade, or for any other infrastructure related reason.

It’s all done in the background, and is totally transparent, so you never really see it happening. Unless you look VERY closely. I once did a demo to a client, where a machine was live migrated while he was simulating a solid network load — and we did not lose a single packet, with no noticeable degradation in latency.

And that’s a wrap!

So there you go. These are 5+1 things that made me go “Aha!” when I became more familiar with the Google Cloud Platform, and that still make my clients do the same.

There is a lot of depth to the platform, and my examples above only scratch the surface of our features. I encourage you to try it yourself. There is a generous free tier, and when you are ready to take the plunge and create that new company, please contact us at Google Cloud for Startups. We’ll get you up and running in no time.

Jerome is a Startup Architect at Google Cloud. Based in Singapore, he helps startups make the most of the Google Cloud Platform.

In the fragmented world of U.S. healthcare, patients often have to wait in line or on hold, navigate multiple patient portals, and fill out numerous request forms—all in pursuit of their own medical history. Healthcare technology startup PicnicHealth is on a mission to put control back with the patient, where it belongs.

PicnicHealth’s growth, from closing successful venture rounds to winning machine learning (ML) competitions, speaks to not only improvements and opportunities in healthcare, but also how startups are leveraging Google Workspace and Google Cloud services to accelerate momentum.

The company does the heavy lifting of collecting records and leverages human-in-the-loop ML to transcribe and validate them with an abstraction team of medical professionals. The records are then structured into a complete medical history that patients can access and share with providers to get better care.

But PicnicHealth helps to improve patient health on more than one front. It allows patients to contribute their data to de-identified medical research, building high-quality, anonymized datasets that researchers and life sciences companies can use to better understand disease progression and treatment in the real world.

Google Workspace has been part of PicnicHealth from day one, helping the founders collaborate and shape the company’s vision using cloud-synced documents and spreadsheets to collaborate and model predictions. “I’ve had a Gmail account since 2006, and in college 100% of people worked out of Google Docs. Workspace continues to be the best choice for online collaboration, and that’s why it’s still the default standard for startups,” said Troy Astorino, CoFounder & CTO of PicnicHealth. “When we started PicnicHealth, my co-founder Noga Leviner was in San Francisco and I was in Southern California, and of course we used Workspace.”

Today, Google Workspace continues to play a central role in the company’s collaboration. “We create design documents in Google Docs, primarily for engineering and product changes, and get really healthy, vibrant discussions through comments,” noted Astorino. “This practice has grown beyond engineering and is used for everything from how the company operates to communication norms.”

Google Workspace offers everything the team needs to collaborate, no matter where employees are. PicnicHealth’s team has spread from San Francisco to being distributed across the country and around the world. Instant collaboration is crucial.

“We work in a complex domain where people need a lot of information to make good decisions,” said Astorino. “Google Workspace allows us to operate in a mode of default transparency, where people can easily get the information they need even if it wasn’t intentionally or directly shared with them. Whether it’s working in Docs or scheduling in Calendar, we can operate much more effectively than we could otherwise.”

By any measure, PicnicHealth’s trajectory is one of record success. The startup is a 2014 alumni of Y Combinator, a program that helped launch household names like Airbnb, DoorDash, and Dropbox. Three years later, the team went on to win the $1 million grand prize at Google’s Machine Learning Competition. And the momentum has continued— PicnicHealth has recently announced a $60 million Series C round, bringing the amount raised to date to over $100 million. With the Series C, PicnicHealth is investing in expanding its reach to more patients across over 30 diseases.

As a healthcare startup, PicnicHealth faced a very particular set of challenges, especially when working with and accessing data. Data fragmentation and interoperability are only some of the challenges of realizing the value of big data in the cloud. The healthcare industry is notoriously difficult to navigate due to sensitive data protection laws and regulations like the Health Insurance Portability and Accountability Act (HIPAA).

PicnicHealth started in the cloud on Amazon Web Services (AWS). However, after migrating over to Kubernetes and facing an expanding list of requirements for HIPAA compliance, the company started to explore alternatives.

“We needed to be HIPAA compliant, which was going to be painful on AWS, and we wanted to get away from managing and operating our own Kubernetes clusters,” recalled Astorino. “We had heard good things about GKE (Google Kubernetes Engine). And particularly valuable for us, — many technical requirements you need for HIPAA compliance are configured by default on Google Cloud.”

PicnicHealth would have had to implement a lot of changes and get specialized instance types to get their existing configuration to work. So, they began experimenting with Google Cloud and discovered a much smoother experience.

“It was a lot easier to manage in terms of product setup and developer experience,” said Astorino. “There is a sane product hierarchy of resources you can access and use through Google Cloud and the relationships between them, from coordinated IAM (identity and access management) to using Google Groups for granting permissions. Overall, it’s cleaner.”

Astorino added that the move has also opened the doors to taking advantage of other services in the Google Cloud ecosystem like Cloud SQL, BigQuery, and Cloud Composer. PicnicHealth also uses Security Command Center because it easily integrates with everything but also helps meet various compliance frameworks’ requirements, providing visibility, near-real-time asset discovery, and security information and event management.

But most importantly, the integrated ecosystem has simplified the work needed for PicnicHealth to create a secure environment for employees to use when working with sensitive medical records while still providing all the tools they need. For example, abstractors not only use Google Workspace but also have Chromebooks because they are easy to manage and secure.

Altogether, Google Cloud helps form a technology stack that has enabled the startup to build a massive labeled dataset containing over 100 million labeled medical data concepts. In turn, it accelerates PicnicHealth’s ability to generate highly-performant AI models and feed other ML pipelines, which has been vital for processing and reviewing data at scale.

To learn more about how Google Workspace and Google Cloud help startups like PicnicHealth accelerate their journey, visit our startups solutions pages for Google Workspace and Google Cloud.

There are multiple reasons a growing number of database administrators, enterprise architects, application developers and other technology practitioners are moving to Google Cloud’s various database services.

Some are being driven by missing features in offering from other providers such as AWS. In Gartner’s Magic Quadrant for Operational Database Management Systems, the research and advisory firm points out that, “AWS’s surveyed reference customers scored its overall product capabilities one standard deviation (STD) below the mean. Their responses identified missing features such as multiregion writes and autosharding.”

Others are moving to database services on Google Cloud Platform driven by a few benefits. According to Gartner, “Reference customers repeatedly commented on Google’s ease of use and implementation, reliability and integration (with other services and other systems). Reference customers scored Google a full STD above the mean for satisfaction with GCP’s pricing; it received the second-highest satisfaction score of any vendor in this Magic Quadrant.

If you are looking to leverage the power of Google Cloud database offerings—but were unsure of which database services comes closest to the service you are currently using, here’s a handy map to find your way.