🚀 Introducing the Universal API Testing Tool — Built to Catch What Manual Testing Misses

In today’s software-driven world, APIs are everywhere — powering everything from mobile apps to microservices. But with complexity comes risk. A single missed edge case in an API can crash systems, leak data, or block users. That’s a huge problem.

After years of working on high-scale automation and quality engineering projects, I decided to build something that tackles this challenge head-on:

👉 A Universal API Testing Tool powered by automation, combinatorial logic, and schema intelligence.

This tool is designed not just for test engineers — but for anyone who wants to bulletproof their APIs and catch critical bugs before they reach production.

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🔍 The Problem with Manual API Testing

Let’s face it: manual API testing, or even scripted testing with fixed payloads, leaves massive blind spots. Here’s what I’ve consistently seen across projects:

• 🔁 Happy path bias: Most tests cover only expected (ideal) scenarios.

• ❌ Boundary and edge cases are rarely tested thoroughly.

• 🧱 Schema mismatches account for over 60% of integration failures.

• 🔄 Complex, nested JSON responses break traditional test logic.

Even with the best intentions, manual testing only touches ~15% of real-world possibilities. The rest? They’re left to chance — and chance has a high failure rate in production.

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💡 Enter: The Universal API Testing Tool

This tool was created to turn a single API request + sample response into a powerful battery of intelligent, automated test cases. And it does this without relying on manually authored test scripts.

Let’s break down its four core pillars:

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🔁 1. Auto-Schema Derivation

Goal: Ensure every response conforms to an expected structure — even when you didn’t write the schema.

• Parses sample responses and infers schema rules dynamically

• Detects type mismatches, missing fields, and violations of constraints

• Supports deeply nested objects, arrays, and edge data structures

• Validates responses against actual usage, not just formal docs

🔧 Think of it like “JSON Schema meets runtime intelligence.”

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🧪 2. Combinatorial Test Generation

Goal: Generate hundreds of valid and invalid test cases automatically from a single endpoint.

• Creates diverse combinations of optional/required fields

• Performs boundary testing using real-world data types

• Generates edge case payloads with minimal human input

• Helps you shift-left testing without writing 100 test cases by hand

📈 This is where real coverage is achieved — not through effort, but through automation.

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📜 3. Real-Time JSON Logging

Goal: Provide debuggable, structured insights into each request/response pair.

• Captures and logs full payloads with status codes, headers, and durations

• Classifies errors by type: schema, performance, auth, timeout, etc.

• Fully CI/CD compatible — ready for pipeline integration

🧩 Imagine instantly knowing which combination failed, why it failed, and what payload triggered it.

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🔐 4. Advanced Security Testing

Goal: Scan APIs for common and high-risk vulnerabilities without writing separate security scripts.

• Built-in detection for:

  • XSS, SQL Injection, Command Injection

  • Path Traversal, Authentication Bypass

  • Regex-based scans for sensitive patterns (UUIDs, tokens, emails)

• Flags anomalies early during development or staging

🛡️ You don’t need a separate security audit to find the obvious vulnerabilities anymore.

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⚙️ How It Works (Under the Hood)

• Developed in Python, using robust schema libraries and custom validation logic

• Accepts a simple cURL command or Postman export as input

• Automatically generates:

  • Schema validators

  • Test payloads

  • Execution reports

• Debug mode shows complete request/response cycles for every test case

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📈 What You Can Expect

The tool is in developer preview stage — meaning results will vary based on use case — but here’s what early adopters and dev teams can expect:

• ⏱️ Save 70–80% of manual testing time

• 🐞 Catch 2–3x more bugs by testing combinations humans often miss

• ⚡ Reduce integration testing time from days to hours

• 🔒 Get built-in security scans with every API run — no extra work required

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🧰 Try It Yourself

🔗 GitHub Repository

👉 github.com/nsharmapunjab/frameworks_and_tools/tree/main/apitester

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💬 Your Turn: What’s Your Biggest API Testing Challenge?

I’m actively working on v2 of this tool — with plugin support, OpenAPI integration, and enhanced reporting. But I want to build what developers and testers actually need.

So tell me:

➡️ What’s the most frustrating part of API testing in your projects?

Drop a comment or DM me. I’d love to learn from your use cases.

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👋 Work With Me

Need help building test automation frameworks, prepping for QA interviews, or implementing CI/CD quality gates?

📞 Book a 1:1 consultation: 👉 topmate.io/nitin_sharma53

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Thanks for reading — and if you found this useful, share it with your dev or QA team. Let’s raise the bar for API quality, together.

#APITesting #AutomationEngineering #QualityAssurance #DevOps #OpenSource #TestAutomation #PythonTools #API #SDET #NitinSharmaTools

🚀 Mastering Kubernetes: 20+ Daily kubectl Commands Every Engineer Should Know

Whether you’re debugging a pod or scaling deployments, having the right commands at your fingertips can save hours of troubleshooting.

Here’s your quick-reference guide to essential kubectl commands used by DevOps, SREs, and Cloud Engineers every single day:

In below examples, assume "services" is the namespace given for your micro-services

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🔹 Context & Cluster Navigation

• kubectl config get-contexts – List all available contexts

• kubectx <env-name> – Switch between environments

🔹 Nodes & Namespaces

• kubectl get nodes – View all cluster nodes

• kubectl get namespaces – List all namespaces

🔹 Pods & Deployments

• kubectl -n services get pod <pod-name> – Get specific pod details

• kubectl -n services get pods – List all pods in a namespace

• kubectl -n services delete pod <pod-name> – Delete a specific pod

• kubectl -n services get pods -o wide | grep ½ – Filter for unhealthy pods

🔹 Detailed Views

• -o wide – Add node-level details

• -o yaml – See full YAML output

• kubectl describe pod <pod-name> -n services – Inspect pod specs

🔹 Inside the Pod

• kubectl exec -it <pod-name> -n services -- /bin/bash – SSH into a pod

• kubectl logs <pod-name> -c install -f -n services – View Init container logs

🔹 Scaling & Rollouts

• kubectl scale deploy <pod-name> --replicas=3 -n services – Manual scaling

• kubectl rollout restart deployment <pod-name> -n services – Restart a service

🔹 HPA & Canary Checks

• kubectl get hpa <pod-name> -n services – Horizontal Pod Autoscaler status

• kubectl describe canary <pod-name> -n services – Inspect a canary deployment

• kubectl get canary -n services – List all canary-enabled pods

🔹 Logs & Troubleshooting (with Stern)

• stern -n services <pod-name> -c <app-name> -t --since 1m – Tail recent logs

• stern -n services geolayers-api-primary -c geolayers-api -t --since 1m | grep '<text>' – Filter logs with grep

🔹 Consul & Vault Checks

• kubectl get pods -n configuration – View Consul/Vault status

• stern -n configuration <name> – Stream logs from Vault

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📘 Bonus: Official kubectl Cheat Sheet

💬 Which of these commands saved you recently — or is there a favorite one missing from the list?

Drop it in the comments and let’s build the ultimate K8s cheat sheet together. 👇

#Kubernetes #DevOps #CloudEngineering #SRE #kubectl #CheatSheet #K8sTips #PlatformEngineering #LinkedInLearning #Productivity

Generative AI: Transforming Software Testing

Generative AI (GenAI) is poised to fundamentally transform the software development lifecycle (SDLC), particularly in the realm of software testing. As applications grow increasingly complex and release cycles accelerate, traditional testing methods are proving inadequate. GenAI, a subset of artificial intelligence, offers a game-changing solution by dynamically generating test cases, identifying potential risks, and optimising testing processes with minimal human input. This shift promises significant benefits, including faster test execution, enhanced test coverage, reduced costs, and improved defect detection. While challenges related to data quality, integration, and skill gaps exist, the future of software testing is undeniably intertwined with the continued advancement and adoption of GenAI, leading towards autonomous and hyper-personalised testing experiences.

Main Themes and Key Ideas

1. The Critical Need for Generative AI in Modern Software Testing

Traditional testing methods are struggling to keep pace with the evolving landscape of software development.

• Increasing Application Complexity: Modern applications, built with "microservices, containerised deployments, and cloud-native architectures," overwhelm traditional tools. GenAI helps by "predicting failure points based on historical data" and "generating real-time test scenarios for distributed applications."
• Faster Release Cycles in Agile & DevOps: The demand for rapid updates in CI/CD environments necessitates accelerated testing. "According to the World Quality Report 2023, 63% of enterprises struggle with test automation scalability in Agile and DevOps workflows." GenAI "automates the creation of high-coverage test cases, accelerating testing cycles" and "reduces dependency on manual testing, ensuring faster deployments."
• Improved Test Coverage & Accuracy: Manual test scripts often miss "edge cases," leading to post-production defects. GenAI "analyzes real-world user behavior, ensuring comprehensive test coverage" and "automatically generates test scenarios for corner cases and security vulnerabilities."
• Reducing Manual Effort and Costs: "Manual testing and script maintenance are labor-intensive." GenAI "automatically generates test scripts without human intervention" and "adapts existing test cases to application changes, reducing maintenance overhead."

2. Core Capabilities and Benefits of Generative AI in Software Testing

GenAI leverages machine learning and AI to create new content based on existing data, leading to a paradigm shift in testing.

• Accelerated Test Execution: "Faster test cycles reduce time-to-market."
• Enhanced Test Coverage: "AI ensures comprehensive testing across all application components."
• Reduced Script Maintenance: "Self-healing capabilities minimise script updates."
• Cost Efficiency: "Lower resource allocation reduces testing costs."
• Better Defect Detection: "Predictive analytics identify defects before they impact users."

3. Key Applications of Generative AI in Software Testing

GenAI’s practical applications are diverse and address many pain points in current testing practices.

• Automated Test Case Generation: GenAI "analyzes application logic, past test results, and user behavior to create test cases," identifying "missing test scenarios" and ensuring "edge case testing."
• Self-Healing Test Automation: Addresses the significant pain point of script maintenance. GenAI "uses computer vision and NLP to detect UI changes" and "automatically updates automation scripts, preventing test failures." Examples include Mabl and Testim.
• Test Data Generation & Management: Essential for complex applications, GenAI "creates synthetic test data that mimics real-world user behavior" and "ensures compliance with data privacy regulations (e.g., GDPR, HIPAA)." Examples include Tonic AI and Datomize.
• Defect Prediction & Anomaly Detection: GenAI "analyzes past defect data to identify patterns and trends," "predicts high-risk areas," and "detects anomalies in logs and system behavior." Appvance IQ is cited for reducing "post-production defects by up to 40%."
• Optimising Regression Testing: GenAI "identifies the most relevant test cases for each code change" and "reduces test execution time by eliminating redundant tests." Applitools uses "AI-driven visual validation."
• Natural Language Processing (NLP) for Test Case Creation: Bridges the gap between manual and automated testing by "converting plain-English test cases into automation scripts," simplifying automation for non-coders.

4. Challenges in Implementing Generative AI

Despite the immense potential, several hurdles need to be addressed for successful adoption.

• Data Availability & Quality: GenAI requires "large, high-quality datasets," and "poor data quality can lead to biased or inaccurate test cases."
• Integration with Existing Tools: "Many enterprises rely on legacy systems that lack AI compatibility."
• Skill Gap & AI Adoption: QA teams require "AI/ML expertise," necessitating "upskilling programs."
• False Positives & Over-Testing: AI models "may generate excessive test cases or false defect alerts, requiring human oversight."

5. The Future of Generative AI in Software Testing

The article forecasts significant advancements leading to more autonomous and integrated testing.

• Autonomous Testing: Future frameworks will "not only design test cases but also execute and analyze them without human intervention." This includes "Self-healing test automation," "AI-driven exploratory testing," and "Autonomous defect triaging."
• AI-Augmented DevOps: The fusion of GenAI with DevOps will create "hyper-automated CI/CD pipelines" capable of "predicting failures and resolving them in real time." This encompasses "AI-powered code quality analysis," "Predictive defect detection," and "Intelligent rollback mechanisms."
• Hyper-Personalized Testing: GenAI will enable testing "tailored to specific user behaviors, preferences, and environments," including "Dynamic test scenario generation," "AI-driven accessibility testing," and "Continuous UX optimisation."

Conclusion

Generative AI is not merely an enhancement but a "necessity rather than an option" for organisations seeking to maintain software quality in a rapidly evolving digital landscape. By addressing the complexities of modern applications, accelerating release cycles, improving coverage, and reducing costs, GenAI will enable enterprises to deliver "faster, more reliable software." While challenges require strategic planning and investment, the trajectory of GenAI in software testing points towards an increasingly automated, intelligent, and efficient future.

Generative AI in Software Testing

Generative AI (GenAI) is poised to fundamentally transform the software development lifecycle (SDLC)—especially in software testing. As applications grow in complexity and release cycles shorten, traditional testing methods fall short. GenAI offers a game-changing solution: dynamically generating test cases, identifying risks, and optimizing testing with minimal human input.

Key benefits include:

• Faster test execution

• Enhanced coverage

• Cost reduction

• Improved defect detection

Despite challenges like data quality, integration, and skill gaps, the future of software testing is inseparably linked to GenAI, paving the way toward autonomous and hyper-personalized testing.

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🚀 Main Themes & Tools You Can Use

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1. The Critical Need for GenAI in Modern Software Testing

Why GenAI? Traditional testing can’t keep pace with:

• Complex modern architectures (microservices, containers, cloud-native)

  • GenAI predicts failure points using historical data and real-time scenarios.

  • 🛠️ Tool Example: Diffblue Cover — generates unit tests for Java code using AI.

• Agile & CI/CD Release Pressure

  • According to the World Quality Report 2023, 63% of enterprises face test automation scalability issues.

  • 🛠️ Tool Example: Testim by Tricentis — uses AI to accelerate test creation and maintenance.

• Missed Edge Cases

  • GenAI ensures coverage by analyzing user behavior and generating test cases automatically.

  • 🛠️ Tool Example: Functionize — AI-powered test creation based on user journeys.

• High Manual Effort

  • GenAI generates and updates test scripts autonomously.

  • 🛠️ Tool Example: Mabl — self-healing, low-code test automation platform.

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2. Core Capabilities and Benefits of GenAI in Testing

Capability	Impact
Accelerated Test Execution	Speeds up releases
Enhanced Test Coverage	Covers functional, UI, and edge cases
Reduced Script Maintenance	AI auto-updates outdated tests
Cost Efficiency	Fewer resources, less manual work
Improved Defect Detection	Finds bugs early via predictive analytics

🛠️ Tool Reference: Appvance IQ — uses AI to improve defect detection and test coverage.

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3. Key Applications of GenAI in Software Testing

✅ Automated Test Case Generation

• Analyzes code logic, results, and behavior to generate meaningful test cases.

• 🛠️ Tool: Testsigma — auto-generates and maintains tests using NLP and AI.

🔧 Self-Healing Test Automation

• Automatically adapts to UI or logic changes.

• 🛠️ Tools:

  • Testim

  • Mabl

🧪 Test Data Generation & Management

• Creates compliant synthetic data simulating real-world conditions.

• 🛠️ Tools:

  • Tonic.ai — privacy-safe synthetic test data

  • Datomize — dynamic data masking & synthesis

🔍 Defect Prediction & Anomaly Detection

• Identifies defect-prone areas before they affect production.

• 🛠️ Tool: Appvance IQ

🔁 Optimizing Regression Testing

• Prioritizes relevant tests for code changes.

• 🛠️ Tool: Applitools — AI-driven visual testing and regression optimization.

✍️ NLP for Test Case Creation

• Converts natural language into executable tests.

• 🛠️ Tool: TestRigor — plain English to automated test scripts.

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4. Challenges in Implementing GenAI

Challenge	Description
Data Availability & Quality	Poor data → inaccurate test generation
Tool Integration	Legacy tools may lack AI support
Skill Gap	Requires upskilling QA teams in AI/ML
False Positives	Over-testing may need human review

🛠️ Solution Suggestion: Use platforms like Katalon Studio that offer GenAI plugins with low-code/no-code workflows to reduce technical barriers.

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5. The Future of GenAI in Software Testing

🤖 Autonomous Testing

• Self-designing, executing, and analyzing test frameworks.

• 🛠️ Tool: Functionize

🔄 AI-Augmented DevOps

• Integrated CI/CD with AI-based code quality checks and rollback mechanisms.

• 🛠️ Tool: Harness Test Intelligence — AI-powered testing orchestration in pipelines.

🎯 Hyper-Personalized Testing

• Tailors tests to real user behavior and preferences.

• 🛠️ Tool: Testim Mobile — for AI-driven UX optimization and mobile test personalization.

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🧩 Conclusion

Generative AI isn’t just an enhancement — it’s becoming a necessity for QA teams aiming to keep pace in a high-velocity development environment.

By combining automation, intelligence, and adaptability, GenAI can enable faster releases, fewer bugs, and more robust software.

✅ Start exploring tools like Testim, Appvance IQ, Mabl, Functionize, and Applitools today to get a head start on the future of intelligent testing.

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💬 Let’s Discuss:

Have you implemented GenAI tools in your QA process? What has been your experience with tools like TestRigor, Tonic.ai, or Mabl?

👇 Drop your thoughts or tool recommendations in the comments.

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#GenAI #SoftwareTesting #Automation #AIinQA #TestAutomation #DevOps #SyntheticData #AItools #QualityEngineering

Kubernetes Architecture explained

In this session, we're gonna look at two types of nodes that kubernetes operates on one is master and another one is slave and we're gonna see what is the difference between those and which role each one of them has inside of the cluster and we're going to go through the basic concepts of how kubernetes does what it does and how the cluster is self-managed and self-healing etc.

Worker Nodes:

• 3 Node Processes
• Container Runtime
• Kubelet
• Kube Proxy
• Each node has multiple pods on it
• 3 processes must be installed on every Node
• Worker Nodes do the actual work
• First process that needs to run on every node is the container runtime because application pods have containers running inside, a container runtime needs to be installed on every node.
• Process that actually schedules those and containers underneath is kubelet which is a process of kubernetes itself unlike container runtime that has interface with both container runtime and machine (worker node itself) because at the end of the day kubelet is responsible for taking that configuration and actually running apod or starting a pod with a container inside and then assigning resources from that node to the container like CPU, RAM and storage resources.
• Usually kubernetes cluster is made up of multiple nodes which also must have container runtime and kubelet services installed and you can have hundreds of worker nodes which will run run other pods and containers and repplicas of the existing pods like my-app and database as an example and the way communication between them works is using services which is sort of loadbalancer which basically catches the requests directed to the pod or application like database for example and then forward it to respective pod and third process that is responsible for forwarding the requests from services to pods is actually kube proxy.
• Kube proxy must be installed on every node and kube proxy has actually intelligent forwarding logic inside that makes sure that the communication also works in a performant way with low overhead for example if an application my-app replica is making a request database instead of service just randomly forwarding the request to any replica, it will actually forward it to the replicat that is running on the same node as the pod that initiated the reuqest thus this way avoiding the network calls overhead of sending the request to another machine.
• To summarise 3 node processes must be installed on every node in order for kubernetes cluster to function properly.

Master Nodes + Master Processes:

• We discussed above regarding worker nodes and processes in details but another question comes in mind so, how do you interact with this cluster?
• How to:
• schedule pod?
• monitor?
• re-schedule/re-start pod?
• join a new node?

• Answer to above ask is all these managing processes are done Master Nodes
• There are 4 processes that run on every master node that control the cluster state and the worker nodes as well.
• API Server
• Scheduler
• Controller Manager
• etcd - the cluster brain
• API Server
• When you as a user want to deploy a new application in a kubernetes cluster, you interact with the APi Server using some client, it could be a UI like kubertest dashboard, could be commandline tool like kubelet etc. API server is like cluster gateway which gets the initial request which updates into the cluster or even the queries from the cluster and it also acts as a gatekeeper for authentication to make sure only authenticated and authorised requests get through to the cluster. 
• That means whenever you want to schedule new pods, deploy new application, create new server or any other component, you have to talk to the API server on the master node and it validates your reuqtest and if everything fine then it forward your request to other processes.
• Also if you want to query the status os your deployment or the cluster health etc, you make a request to the API server and it gives you the response which is good for security because you have one entry point into the cluster.

• Scheduler
• As mentioned API server sends request to scheduler and scheduler has intelligent way to decide in which node pod has to put based on how much resources new pod needs and how much resources availables in the given nodes etc.

• Controller Manager
• Another crucial component because what happens when pods die on any node, there must be a way to detect that nodes died and then reschedule those pods asap.
• Controller manager detects cluster state changes like crashing of pods for example when pods die, controller manager detects that and try to recover the cluster state asap and for that it makes a request to the scheduler to reschedule those dead pods in the same cycle what we discussed during scheduler part discussion.

• etcd
• etcd is the cluster brain
• key-value store
• Cluster changes get stored in the key value store
• why we call it as cluster brain because all of those mechianism with scheduler and controller manager works because of this data, for example
• How scheduler knows what resources are available on each worker node.
• How does controller manager know that a cluster state changed in some way for example pods died or that kubelet restarted new pods upon the request of a scheduler.
• or when you make a query request to API server about the cluster health or for example you application deployment state where as API server get all this information from so all of this information is stored in etcd cluster.
• Actual application data is not stored in this one, only cluster state data.

Happy Learning !! :) 

Kubernetes Basic Components

 Here is a quick view of few basic components of K8

Pod:

• Smallest unit of K8
• Abstraction over container
• Usually one application per Pod
• Each Pod gets his own IP address
• New IP address on re-creation

Services:

• An abstract way to load balance across the pods and expose an application deployed on a set of Pods.
• Permanent IP address

ConfigMap:

• External configuration of your application like DBs config etc

Secret:

• Used to store secret data like DBs username/pwd etc which can't be store in plain text in ConfigMap
• base64 encoded

Kubernetes Basics

 Official definition of Kubernetes

Open source container orchestration tool developed by Google and helps you manage containerized applications in different deployment environments like physical machines, virtual machines or cloud environment or even hybrid deployment environments.

The need for a container orchestration tool

• Trend from Monolith to Microservices
• Increased usage of containers
• Demand for a proper way of managing those hundreds of containers

What features do orchestration tools offer?

• High Availability or no downtime
• Scalability or high performance
• Disaster recovery - backup and restore

Kubernetes Basic Architecture

Made up with master node and couple of worker nodes where each node has kubelet process running on it. Kubelet is basically a kubertnetes process that makes it possible for cluster to talk to each other and actually executes some tasks on those nodes like running application processes. Each worker node has docker containers of different applications deployed on it. On worker nodes your applications are running.

Master node runs several K8 processes

• API Server which also a container and an entrypoint to K8 cluster.
• Controller Manager keeps track of whats happening in the cluster, may be if container died and needs a restart etc.
• Scheduler ensures Pods placement.
• etcd (key value storage) which basically holds at anytime current state of K8 cluster. It has all the configuration data inside and all the status data of each node and each container inside of that node and backup restore actually made of this etcd snapshot.

Kubernetes Basics Concepts

Pod is a smallest unit which you as a user will configure and interact with and basically a wrapper of container. In a worker node you gonna have multiple pods. Each pod is it's own self contained server with it's own IP address. Pods are recreated frequently and it gets new IP address on creation hence another component of K8 called service is used along with pod.