Nicolas Mosquera

Mechatronics engineer |
Software developer | AWS | Python | Typescript | React 💻

[Neo Kim @systemdesignone]

If you want to get ahead of 99% of software engineers, then learn these 20 API concepts:

1 Endpoint
2 HTTP Methods
3 Request-Response
4 Status Codes
5 Authentication
6 Authorization
7 Access Tokens
8 OAuth 2.0
9 Rate Limiting
10 Throttling
11 Pagination
12 Caching
13 Idempotency
14 Webhooks
15 API Versioning
16 OpenAPI
17 REST vs GraphQL
18 API Gateway
19 Microservices
20 Error Handling

What else should make this list?

===

💾 Save & RT to help others ace API design.
👤 Follow @systemdesignone + turn on notifications.

[Akhilesh Mishra @livingdevops]

https://t.co/QWt3k9rSqJ

[Dhanian 🗯️ @e_opore]

Docker CI/CD pipeline flowchart

[Dhanian 🗯️ @e_opore]

AWS Roadmap

Grab AWS ebook: https://t.co/okWeD9ZHFa

[Dhanian 🗯️ @e_opore]

Day 29 of Learning Backend Engineering.

Working with JSON Web Tokens – Refresh Tokens.

Grab the Backend Engineering Ebook: https://t.co/qQcR0x4fAo

[yourclouddude @yourclouddude]

10 AWS PROJECTS that can make your RESUME stronger:

[Python Developer @PythonDvz]

n8n Process - Step by Step

[Dhanian 🗯️ @e_opore]

How Kubernetes orchestrates pods
(clearly explained in under 1 min):

Kubernetes is a container orchestration platform that automatically deploys, manages, scales, and heals containerized applications.

Instead of manually managing containers, Kubernetes handles everything for you.

Here’s a simple mental model to understand it:

𝟭) 𝗬𝗼𝘂 𝗱𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗱𝗲𝘀𝗶𝗿𝗲𝗱 𝘀𝘁𝗮𝘁𝗲
↳ You create a Deployment manifest
↳ Specify how many pod replicas should run

𝟮) 𝗧𝗵𝗲 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲𝗿 𝗽𝗹𝗮𝗰𝗲𝘀 𝗽𝗼𝗱𝘀
↳ Kubernetes chooses the best worker node
↳ Pods are distributed across available resources

𝟯) 𝗣𝗼𝗱𝘀 𝗮𝗿𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱
↳ The kubelet on each node starts containers
↳ Containers run inside pods

𝟰) 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗲𝘅𝗽𝗼𝘀𝗲 𝗽𝗼𝗱𝘀
↳ Pods can be reached through Services
↳ Traffic is load-balanced across replicas

𝟱) 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝘀 𝘁𝗵𝗲𝗺
↳ Health checks detect failures
↳ Crashed pods are automatically recreated

𝟲) 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝘀 𝗮𝘂𝘁𝗼����𝗮𝘁𝗲𝗱
↳ More traffic → more pod replicas
↳ Less traffic → fewer replicas

𝟳) ��𝗽𝗱𝗮𝘁𝗲𝘀 𝗮𝗿𝗲 𝗿𝗼𝗹𝗹𝗲𝗱 𝗼𝘂𝘁 𝘀𝗮𝗳𝗲𝗹𝘆
↳ New pods are created gradually
↳ Old pods are removed with minimal downtime

That's the core idea behind Kubernetes orchestration.

You tell Kubernetes what you want.
Kubernetes continuously works to make reality match that desired state.

If you want hands-on Kubernetes projects, deployments, scaling strategies, and production-grade examples, check out this ebook:
https://t.co/vyboCdLTx0

What else would you add?

——

♻️ Repost to help others learn DevOps.
🙏 Remember to bookmark.
➕ Follow me ( @e_opore ) to improve at software engineering.

[Suhani @tiwarisuhani_11]

Todays Concept
API Gateway

[JR | Data Engineer @Mbadiwejesse_]

Types of Databases

[Akhilesh Mishra @livingdevops]

Everyone is sharing DevOps roadmaps.
Most are just lists of tools.

But DevOps isn't about learning 20 tools at once. It's about learning the right things in the right order.

If I were starting in DevOps today, knowing what companies actually hire for, this is the roadmap I'd follow from Day 1 to landing a DevOps role.

I put together the complete roadmap here 👇

Full roadmap 👇
https://t.co/GDW4YzUJY7

[Brij Pandey @LearnWithBrij]

MASTER SYSTEM DESIGN

SYSTEM DESIGN MASTER TREE
│
├── 1. Fundamentals
│ ├── What is System Design
│ ├── Functional Requirements
│ ├── Non-Functional Requirements
│ │ ├── Scalability
│ │ ├── Reliability
│ │ ├── Availability
│ │ ├── Consistency
│ │ └── Performance
│ └── Trade-offs
│
├── 2. Architecture Basics
│ ├── Monolithic Architecture
│ ├── Microservices Architecture
│ ├── Service-Oriented Architecture (SOA)
│ ├── Layered Architecture
│ └── Event-Driven Architecture
│
├── 3. Networking Basics
│ ├── HTTP / HTTPS
│ ├── TCP / IP
│ ├── DNS
│ ├── Load Balancing Concepts
│ └── Latency vs Throughput
│
├── 4. Load Balancing
│ ├── Types (L4 / L7)
│ ├── Algorithms
│ │ ├── Round Robin
│ │ ├��─ Least Connections
│ │ └── IP Hash
│ ├── Reverse Proxy
│ └── Failover Strategies
│
├── 5. Databases
│ ├── SQL vs NoSQL
│ ├── Relational Databases
│ ├── NoSQL Types
│ │ ├── Key-Value
│ │ ├── Document
│ │ ├── Column
│ │ └── Graph
│ ├── CAP Theorem
│ └── ACID vs BASE
│
├── 6. Caching
│ ├── Why Caching
│ ├── Cache Layers
│ │ ├── Client-side
│ │ ├── CDN
│ │ ├── Server-side
│ ├── Cache Strategies
│ │ ├── Write-through
│ │ ├── Write-back
│ │ └── Cache-aside
│ └── Cache Invalidation
│
├── 7. Data Partitioning
│ ├── Vertical Partitioning
│ ├── Horizontal Partitioning (Sharding)
│ ├── Consistent Hashing
│ └── Rebalancing
│
├── 8. Replication
│ ├── Master-Slave
│ ├── Master-Master
│ ├── Synchronous Replication
│ └── Asynchronous Replication
│
├── 9. Consistency Models
│ ├── Strong Consistency
│ ├── Eventual Consistency
│ ├── Causal Consistency
│ └── Read-after-Write Consistency
│
├── 10. Messaging Systems
│ ├── Message Queues
│ ├── Pub/Sub Systems
│ ├── Kafka / RabbitMQ
│ ├── Event Streaming
│ └── Exactly-once / At-least-once Delivery
│
├── 11. API Design
│ ├── REST
│ ├── GraphQL
│ ├── gRPC
│ ├── Rate Limiting
│ └── API Gateway
│
├── 12. Storage Systems
│ ├── File Storage
│ ├── Object Storage (S3)
│ ├── Block Storage
│ └── CDN (Content Delivery Network)
│
├── 13. Security
│ ├── Authentication
│ ├── Authorization
│ ├── Encryption (SSL/TLS)
│ ├── Firewalls
│ └── DDoS Protection
│
├── 14. Scalability
│ ├── Vertical Scaling
│ ├── Horizontal Scaling
│ ├── Auto Scaling
│ └── Stateless Services
│
├── 15. Fault Tolerance
│ ├── Redundancy
│ ├── Circuit Breaker
│ ├── Retries & Backoff
│ └── Graceful Degradation
│
├── 16. Observability
│ ├── Logging
│ ├── Monitoring
│ ├── Metrics
│ ├── Alerting
│ └── Distributed Tracing
│
├── 17. Deployment & DevOps
│ ├── CI/CD
│ ├── Docker
│ ├── Kubernetes
│ ├── Blue-Green Deployment
│ └── Canary Releases
│
��── 18. Real-World System Design
├── URL Shortener (like Bitly)
├── Chat System (like WhatsApp)
├── Video Streaming (like YouTube)
├── Ride Sharing (like Uber)
└── Social Media Feed (like Twitter/X)

[Dhanian 🗯️ @e_opore]

Backend Engineering Career Mastery Roadmap:From Beginner to Expert.

Grab backend engineering ebook: https://t.co/qQcR0x4fAo

[Dhanian 🗯️ @e_opore]

Day 14/100 of Learning System Design.

Microservices Architecture – Pros, Cons, and Challenges.

Grab the System Design Ebook: https://t.co/1pR2xx1I3p

[Arun Chauhan @imarun_chauhan]

𝟯𝟬 𝗕𝗹𝗼𝗴𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝟯𝟬 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:

1. APIs: https://t.co/7H7a1hbeTT
2. API Gateways: https://t.co/7D0lFhOkL5
3. JWTs: https://t.co/zZHHBM9wBb
4. Webhooks: https://t.co/dmNCRFSqj1
5. REST vs GraphQL: https://t.co/iy4pgOGgFc
6. Load Balancing: https://t.co/dZM2ljxWcJ
7. Proxy vs Reverse Proxy: https://t.co/AoKQi84yk0
8. Scalability: https://t.co/ZY345ro6Kt
9. Availability: https://t.co/sGMTFelPVq
10. SPOF: https://t.co/FEKM4xayDi
11. CAP Theorem: https://t.co/wihSO7yRyn
12. SQL vs NoSQL: https://t.co/QtMfhVK8N7
13. ACID Transactions: https://t.co/VZbabQ3XZ3
14. Database Indexes: https://t.co/2W2zz8ACnp
15. Database Sharding: https://t.co/B3c343tHdq
16. Consistent Hashing: https://t.co/uxnWwwQCXR
17. CDC: https://t.co/CkVR7LieSv
18. Caching: https://t.co/smvgorjEYo
19. Caching Strategies: https://t.co/ZU2Jfe8CPY
20. Cache Eviction Policies: https://t.co/TATblyQDLN
21. CDN: https://t.co/hZuGvCKZVW
22. Rate Limiting Algorithms: https://t.co/eajEzctOum
23. Message Queues: https://t.co/Pkxsxtbfev
24. Bloom Filters: https://t.co/a2gXDaLXiO
25. Idempotency: https://t.co/afRE4i5dOX
26. Concurrency vs Parallelism: https://t.co/xZs7ofHcag
27. Long Polling vs WebSockets: https://t.co/Sq04tipuMk
28. Stateful vs. Stateless Architecture: https://t.co/iIVOOm7OdG
29. Batch vs Stream Processing: https://t.co/Awyb4eK31s
30. Geohashing: https://t.co/lQPefhiWoI

🔁 Repost to help a friend.
📌 Save this post for later.

Follow @imarun_chauhan for more resources daily 🚀

[Dhanian 🗯️ @e_opore]

Backend Engineering Mastery Guide

If it is handling requests → APIs
If it is designing APIs → REST / GraphQL
If it is server-side development → Node.js / Java / Go
If it is database management → PostgreSQL / MySQL
If it is NoSQL data → MongoDB
If it is authentication → JWT / OAuth
If it is authorization → RBAC / Permissions
If it is caching → Redis
If it is background jobs → Queues & Workers
If it is messaging systems → Kafka / RabbitMQ
If it is file storage → S3
If it is scalability → Load Balancers
If it is performance → Query Optimization
If it is monitoring → Prometheus / Grafana
If it is logging → ELK Stack
If it is containerization → Docker
If it is orchestration → Kubernetes
If it is deployment → CI/CD Pipelines
If it is system reliability → Fault Tolerance + Resilience
If it is mastering backend engineering → build → scale → monitor → improve → repeat

Grab the Backend Engineering ebook: https://t.co/t9mqUuRbjx

[InfoQ @InfoQ]

Shopify has unveiled GraphQL Cardinal, a new execution engine replacing depth-first traversal with breadth-first execution.

⚡ Up to 15× faster field execution
⚡ 6× lower garbage collection overhead
⚡ More than 4 seconds improvement in P50 latency

🔗 https://t.co/rMoz5JgVyG

[React Router @ReactRouter]

We just published 7 CVEs identifying security vulnerabilities in React Router and Remix v2

We recommend updating to the latest appropriate versions:

- React Router v7 -- 7.16.0
- React Router v6 -- 6.30.4
- Remix v2 -- 2.17.5

Details, links, and package ranges are listed below

[Akhilesh Mishra @livingdevops]

"We use Prometheus for monitoring."

I hear this in almost every interview. Then I ask one question and the whole thing falls apart.

"Why do logs and metrics need different pipelines?"

Silence.

Most people jump into Prometheus and Grafana without understanding what they're actually solving. They know the tools. They can't explain the problem.

With observability, you're solving two completely different problems.

Logs tell you what happened. An error occurred. A request came in. A database query failed. These are events. Stories your application tells.

Metrics tell you how things are performing right now. Latency is 200ms. CPU is at 75%. You processed 500 requests per minute. These are measurements.

Different data types. Different collection methods. Different storage. That's where people get confused.

Last month in my DevOps bootcamp, we built a complete observability system for microservices on Kubernetes.

For logs, we used Fluentd sidecars that share a volume with the application container.

The app writes logs to the volume.
Fluentd reads and forwards them.
Clean separation of concerns.
At a small scale, you send logs straight to CloudWatch.

But when you're generating thousands of log lines per second, you add layers.

Lambda for formatting.
Kinesis for buffering.
OpenSearch for fast queries across petabytes of data.
S3 for long-term backup.

We kept 7 days in OpenSearch for active investigation. 30 days in CloudWatch. Years in S3 for compliance. Each layer has different cost and performance characteristics.

For metrics, Prometheus scrapes application endpoints every 30 seconds.

Developers instrument their code with Prometheus client libraries.
They expose a /metrics endpoint.
Prometheus pulls the data automatically.

We created ServiceMonitors that tell Prometheus which pods to scrape based on labels.

As soon as new pods come up, Prometheus discovers and scrapes them.
Then Grafana visualizes everything.
We imported pre-built dashboards from https://t.co/5wE21Lb4Q8 for Kubernetes monitoring.
And built custom panels for application-specific metrics.

Logs and metrics run in parallel.

When something breaks, metrics show you the spike. The error rate jumped. Latency went from 100ms to 2 seconds.

Then you check the logs. Filter for that time window. Find the stack traces. See exactly what failed.

You can't troubleshoot with just one. You need both perspectives.

We implemented it, troubleshot everything in a live call, generated real metrics and logs, and built dashboards in Grafana.

That's the difference between watching tutorials and actually understanding how systems work in production.

[Shopify @Shopify]

manage your store with @perplexity_ai Computer

do market research, generate product images, and design a theme in parallel

another day, another agent where you can run your business