Putting data on cloud 9?

We are back again after a year of solitude. For those of you who are new to this series, then Hi! My name is Srinidhi, and I love writing. I also love learning tech. So, with the best of both worlds, we have this series. Over the past month, I have been fiddling with the Google Cloud platform and have had the opportunity to play games and do trivia. And so, like the full of ideas rani I am, I wanted to share what I learned, why it was vital for me to know it, and what I did. Unlike the conventional articles/YouTube videos that tell you steps on what to do, I will be more focused on what I learned rather than what I did. So, I hope I can make a startling statement for someone interested in the Cloud but who doesn’t know what the Cloud is.

So before we get into the pit, I want to quickly brief on what Cloud computing is and what do we do with it. Cloud computing means using the internet to access and store data or run programs instead of using your own computer or servers. Big companies like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer these services. People and businesses use the cloud to save money, avoid managing hardware, easily scale up or down, and access their data or apps from anywhere in the world. Now there are ways you try these for free with Azure, Google cloud facilitator programs, getting 300 points for free with GCP etc., if you enroll yourself.

Now how do we use this cloud? That is what the first game - TECH CARE deals with ( an application/use case scenario of cloud). It tells us how cloud can streamline healthcare data management. We worked with FHIR and HL7v2 data using the Healthcare API, de-identify medical images, and build a secure data lake on Cloud Storage. We also explored ways to cut cost and process large datasets with Cloud Dataproc. Now all these terms sound Greek Latin but I promise it will get easier. So lets break down this Game concepts into:

• What it is

• Why it's important

• Simple steps done in the lab

  TECH CARE is a lab where we modernize healthcare IT by:

  Making patient data more accessible, structured, and interoperable (FHIR, HL7v2)
  Anonymizing medical images for ethical research and compliance (DICOM de-ID)
  Creating secure, scalable storage systems (Data Lakes on GCS)
  Reducing costs intelligently (Storage tiers, Dataproc efficiency)
  Empowering real-time decisions with dashboards and AI (Looker, ML tools)

• Now why are we doing all this? Like Nani says in HIT 3- What is it’s purpose? (Cheesy! Ik sorry for the lame joke)

  Because healthcare is undergoing a digital transformation and data is at the center of it. From patient records and medical imaging to genomics and hospital operations, vast amounts of sensitive data are being generated every day.

  But with that comes major challenges:

  • • Data is fragmented across outdated systems

      • Patient privacy and data security are critical

      • Healthcare providers need fast, reliable insights to save lives

      • Storage and compute costs can spiral without smart planning

That’s where Google Cloud steps in with these labs.

1. Ingesting FHIR Data with the Healthcare API

What:
You use the FHIR (Fast Healthcare Interoperability Resources) standard to store and retrieve healthcare records using the Google Cloud Healthcare API.

• What is FHIR? A modern healthcare data standard that helps systems exchange patient info easily and securely.
  What us Google cloud Healthcare API? A tool that lets you store, manage, and work with healthcare data on Google Cloud.

  Why:
  FHIR is widely used in modern EHR (Electronic Health Record) systems. Knowing how to ingest FHIR data helps in building interoperable healthcare apps.

  Steps:

  • Enable the Healthcare API in your GCP project.

  • Create a FHIR store in a dataset.

  • Use a script (commands) to upload sample FHIR JSON files (e.g., patient or observation data).

  • Query data using REST APIs to fetch or search patient info.

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2. Ingesting HL7v2 Data with the Healthcare API

What:
Upload HL7v2 (a legacy healthcare messaging format) into Cloud Healthcare API and parse it.

• What is HL7v2? A messaging standard hospitals use to share patient information between systems.
  What is MLLP? A simple protocol used to send HL7v2 messages over a network.

  Why:
  Most hospitals still use HL7v2 systems. Mastering this lets you modernize existing systems by bridging old and new data formats.

  Steps:

  • Create a dataset and HL7v2 store.

  • Send sample HL7v2 messages using a script or MLLP (Minimal Lower Layer Protocol).

  • Observe how messages are parsed into structured data by the API.

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3. De-identify DICOM Data with the Healthcare API

What:
DICOM is a medical image format (like MRI, X-rays). This lab de-identifies patient metadata (names, dates) in DICOM files.
What is DICOM? A file format used for storing medical images like MRIs and X-rays.
What is HIPAA? A US law that protects patient health information and privacy.
What is a Job? A task or process you run on the cloud (like data processing or training a model).

Why:
To comply with HIPAA and other regulations, patient data must be anonymized before being shared or analyzed.

Steps:

• Upload DICOM images to a DICOM store.

• Use a de-identification template.

• Run a de-identification job via API to remove sensitive metadata.

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4. Create a Secure Data Lake on Cloud Storage (Challenge Lab)

What:
This challenge integrates what we have learned — building a secure cloud data lake for healthcare datasets.

• What is structured and unstructured data? Structured data is organized (like in tables), while unstructured data is raw (like images, text, or videos).
  What is GCS Bucket? A storage container in Google Cloud where you save files like documents, images, or data.
  What is IAM Policy? Rules in Google Cloud that decide who can access what resources and what actions they can take.

  Why:
  A data lake allows you to store structured/unstructured data securely and scalably for analytics, ML, or archiving.

  Steps:

  • Create a GCS bucket with encryption and IAM policies.

  • Store FHIR/HL7v2/DICOM files in structured folders.

  • Apply lifecycle and retention rules.

  • Set up logging to monitor access.

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5. Optimizing Cost with Google Cloud Storage

What:
You explore storage classes (Standard, Nearline, Coldline, Archive) to manage costs based on how often data is accessed.
What are storage classes? Options in Google Cloud Storage that control how often data is accessed and how much it costs.

Why:
Cost optimization is key in cloud projects. Proper storage class use can save thousands in long-term healthcare data storage.

Steps:

• Upload files to a GCS bucket.

• Change storage classes based on usage.

• Set up Object Lifecycle Management.

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6. Reinforcement Learning: Qwik Start

What:
A beginner lab that introduces reinforcement learning (RL) with prebuilt agents in a controlled environment.

Why:
RL is important for simulations and adaptive decision-making, e.g., in healthcare diagnostics or drug discovery.

Steps:

• Use AI Platform Notebooks.

• Load an RL agent.

• Train the agent in a Gym environment.

• View how it learns through trial-and-error.

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7. Introduction to Cloud Dataproc: Hadoop and Spark on GCP

What:
You run Hadoop and Spark workloads on Cloud Dataproc, Google’s managed big data service.

• What is Cloud Dataproc? A Google Cloud service to run big data tools like Hadoop and Spark easily and fast.
  What is Hadoop and Spark? Tools used to store and process large amounts of data quickly, often in parallel.

  Why:
  Healthcare generates huge data — think genomic data or patient monitoring logs. Spark processes large datasets fast.

  Steps:

  • Create a Dataproc cluster.

  • Upload data to GCS.

  • Run PySpark or Hadoop jobs to process it (e.g., filter, analyze).

  • Shutdown cluster to save cost.

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8. Sending and Scheduling Dashboards in Looker

What:
Learn how to use Looker (BI tool) to create visual dashboards and automate reports.

Why:
Healthcare decisions depend on insights. Automating dashboard delivery (e.g., weekly patient trends) enhances response time.

Steps:

• Create a dashboard with filters/metrics.

• Schedule it to email as PDF or CSV on set intervals.

• Set conditions (e.g., alert if readmission rate > 10%).

This was the very first lab I did. This follows The other games where it includes LEVEL 1,2,3. TRIVIA WEEK 1,2,3,4 with an additional cherry on top certification zone and base camp. Lets quickly skim over through them as well. For the purpose of this article being short and simple for the beginners I am going club labs and share.

LEVEL 1,2,3- Base camp, certification zones

The purpose of these labs are to learn how to use Google Cloud to build real-world tech solutions, especially in areas like:

• Healthcare (managing medical data securely)

• Big Data (analyzing massive datasets)

• AI/ML (adding intelligence to apps)

• Security (protecting systems and data)

• Automation & DevOps (faster, error-free deployments)

• APIs & Cloud Functions (connect apps and systems easily)

• Serverless & App Dev (build apps without worrying about infrastructure)

In short: These labs teach you how to use Google Cloud like a real engineer would in big tech companies.

LEVEL 1, 2, 3is a collection of hands-on labs and tools, where each one focuses on learning a specific part of Google Cloud:

🔹 Data & Analytics

• BigQuery Labs → Learn to analyze shared or massive data in seconds.

• Dataproc & Spark → Learn to process big datasets like in Netflix/YouTube.

🔹 Healthcare Tech

• FHIR/HL7v2/DICOM Labs → Work with real medical data formats used in hospitals.

• De-identification → Remove patient info securely (HIPAA compliance).

🔹 App Development

• App Engine / Cloud Run / Go Apps → Deploy your apps to the web easily.

• API Gateway / Gmail Add-ons / Chat Bots → Build and manage cloud-based integrations.

🔹 AI / NLP

• Natural Language API Labs → Analyze text, understand sentiment, extract info.

• Cloud DLP / Vision / Speech APIs → Work with AI for privacy, vision, and audio.

🔹 Security & Monitoring

• Security Command Center → Detect vulnerabilities and threats.

• Binary Authorization → Only allow trusted code to be deployed.

• Prometheus Monitoring → Watch your services and get alerts when things go wrong.

🔹 DevOps & Infrastructure

• Compute Engine / Load Balancers / VPC → Set up scalable servers and networks.

• Startup Scripts / Marketplace → Automate and use prebuilt solutions.

• IAM Policies → Control who can access what.

🔹 Smart Collaboration

• Smart Canvas / Google Docs Add-ons → Improve teamwork using smart tools.

With what we learnt, we put it into more use in Base camp and certification zone. These labs explore powerful Google Cloud tools across AI, data, app development, and system architecture. You'll learn to use APIs for translating text, converting speech to text, and classifying documents using Natural Language and Speech APIs. Labs like Visual Inspection AI teach how to detect defects in manufacturing, while BigQuery and Looker Studio help you analyze weather or business data visually. You’ll build apps without code using AppSheet, create chat apps, and manage Google Classroom. Infrastructure labs let you create virtual machines, configure cost controls, and build resilient systems with Cloud Run, Pub/Sub, and Eventarc. Others teach using the Google Cloud SDK, organizing data in BigLake, and working with hybrid data engines like SingleStore.

These labs simulate the real work you'd do as a cloud engineer, developer, or data specialist in top tech companies.

• They help you build, secure, analyze, and automate.

• You're learning how to make apps smarter, data safer, and systems faster — in the cloud.

What did I do in these labs? Whilst, I can’t explain you every thing I did, I will certainly indulge you in simple things I learnt to do.

1. Creating a Gmail Add-on

• Why: Develop a Gmail Add-on to modify email labels directly within the Gmail interface.

• Steps:

  • Use Google Apps Script to create the add-on.

  • Implement functionality to change email labels.

  • Deploy and test the add-on in Gmail.

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2. Using the Natural Language API from Google Docs

• Why: Integrate Google's Natural Language API to analyze text within Google Docs.

• Steps:

  • Enable the Natural Language API in Google Cloud.

  • Write a script to send document text to the API.

  • Display analysis results, such as sentiment and entity recognition, in the document.

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3. Google Chat Bot - Apps Script

• Why: Create a bot for Google Chat using Apps Script to automate responses and actions.

• Steps:

  • Set up a new Apps Script project.

  • Define bot behavior for different chat events.

  • Deploy the bot and add it to a Google Chat space.

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4. Consuming Customer Specific Datasets from Data Sharing Partners using BigQuery

• Why: Access and analyze datasets shared by partners through BigQuery.

• Steps:

  • Accept shared datasets in BigQuery.

  • Query and join shared data with existing datasets.

  • Perform analysis and visualize results.

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5. App Engine: Qwik Start - Go

• Why: Deploy a simple Go application using Google App Engine.

• Steps:

  • Write a basic Go web application.

  • Configure the app.yaml file for deployment.

  • Deploy the application to App Engine.

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6. Gating Deployments with Binary Authorization

• Why: Enhance security by ensuring only trusted container images are deployed.

• Steps:

  • Enable Binary Authorization in Google Cloud.

  • Create and sign container images.

  • Configure policies to allow only signed images to be deployed.

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7. Deploy Go Apps on Google Cloud Serverless Platforms

• Why: Deploy Go applications using serverless platforms like Cloud Functions and Cloud Run.

• Steps:

  • Write a Go application compatible with serverless environments.

  • Deploy the application using Cloud Functions or Cloud Run.

  • Test the deployed application endpoints.

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8. HTTP Google Cloud Run Functions in Go

• Why: Create and deploy HTTP-triggered functions written in Go using Cloud Run.

• Steps:

  • Develop a Go function to handle HTTP requests.

  • Containerize the function using Docker.

  • Deploy the container to Cloud Run and test the HTTP endpoint.

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9. Smart Canvas: Smart Chips, Templates, and Collaboration in Google Docs

• Why: Utilize Smart Canvas features to enhance collaboration in Google Docs.

• Steps:

  • Insert smart chips for people, files, and dates.

  • Use templates to structure documents.

  • Collaborate in real-time with team members.

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10. Detect and Investigate Threats with Security Command Center

• Why: Identify and analyze security threats within Google Cloud environments.

• Steps:

  • Enable Security Command Center.

  • Review and investigate security findings.

  • Implement recommendations to mitigate risks.

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11. Mitigate Threats and Vulnerabilities with Security Command Center: Challenge Lab

• Why: Apply knowledge to address security vulnerabilities in a simulated environment.

• Steps:

  • Identify vulnerabilities using Security Command Center.

  • Prioritize and remediate identified issues.

  • Validate that threats have been mitigated.

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12. Monitor Environments with Google Cloud Managed Service for Prometheus: Challenge Lab

• Why: Set up monitoring for applications using Prometheus in Google Cloud.

• Steps:

  • Deploy a sample application to a Kubernetes cluster.

  • Configure Managed Service for Prometheus to collect metrics.

  • Create dashboards to visualize application performance.

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13. APIs Explorer: Compute Engine

• Why: Explore and test Compute Engine APIs using the APIs Explorer tool.

• Steps:

  • Navigate to the APIs Explorer for Compute Engine.

  • Execute API calls to manage virtual machine instances.

  • Review responses and understand API functionalities.

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14. Compute Engine: Qwik Start - Windows

• Why: Launch a Windows Server instance on Google Compute Engine.

• Steps:

  • Create a new virtual machine with a Windows Server image.

  • Configure firewall rules to allow RDP access.

  • Connect to the instance using Remote Desktop Protocol.

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15. Provision Services with Google Cloud Marketplace

• Why: Deploy third-party applications quickly using Google Cloud Marketplace.

• Steps:

  • Browse the Marketplace for desired applications.

  • Configure deployment settings.

  • Launch the application and verify its functionality.

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16. Deploy a Compute Instance with a Remote Startup Script: Challenge Lab

• Why: Automate instance configuration using startup scripts during deployment.

• Steps:

  • Write a startup script to install and configure software.

  • Create a new Compute Engine instance and attach the script.

  • Verify that the script executed successfully upon instance startup.

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17. Monitor an Apache Web Server using Ops Agent

• Why: Set up monitoring for an Apache web server using Google's Ops Agent.

• Steps:

  • Install the Ops Agent on the server.

  • Configure the agent to collect Apache metrics and logs.

  • View collected data in Cloud Monitoring and Logging.

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18. VPC Networking Fundamentals

• Why: Understand and configure Virtual Private Cloud (VPC) networks in Google Cloud.

• Steps:

  • Create custom VPC networks and subnets.

  • Set up firewall rules to control traffic.

  • Deploy instances within the VPC and test connectivity.

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19. Set Up Network and Application Load Balancers

• Why: Distribute traffic across multiple instances using load balancers.

• Steps:

  • Deploy backend instances.

  • Configure a network or application load balancer.

  • Test load balancing by sending traffic to the frontend IP.

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20. The Basics of Google Cloud Compute: Challenge Lab

• Why: Apply foundational Compute Engine skills in a practical scenario.

• Steps:

  • Create and configure virtual machine instances.

  • Set up networking and firewall rules.

  • Deploy applications and verify their accessibility.

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21. Internal Load Balancer

• Why: Set up an internal load balancer to distribute traffic within a private network.

• Steps:

  • Deploy backend instances in a private network.

  • Configure an internal load balancer.

  • Test internal traffic distribution among instances.

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22. API Gateway: Qwik Start

• Why: Expose and manage APIs using Google's API Gateway.

• Steps:

  • Define an API configuration.

  • Deploy the API to the gateway.

  • Test API endpoints and monitor usage.

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23. Cloud Data Loss Prevention API: Qwik Start

• Why: Identify and protect sensitive data using the DLP API.

• Steps:

  • Enable the DLP API in Google Cloud.

  • Submit data for inspection.

  • Review findings and apply data masking or redaction.

If you're anything like me, your brain is probably both exhausted and excited right now! That’s the magic of tech. Over the coming weeks, I hope to share more articles covering skill badges and other learnings from the program. If you’re also enrolled in this journey, I’d love to hear your thoughts—feel free to drop a comment below. Until next time, tech talk tick tocks.