Referred Link -  

https://www.linkedin.com/posts/aditya-hicounselor_ai-sql-nosql-activity-7378057053979844608-z-AX

𝗔𝘀 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗲𝘃𝗼𝗹𝘃𝗲 𝘁𝗼𝘄𝗮𝗿𝗱 𝗔𝗜, 𝘀𝗰𝗮𝗹𝗲, 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀, 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹.

It’s not just SQL vs NoSQL anymore. It’s about choosing the right database for the data, the workload, and the business outcome.

𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 👇

1. 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 (SQL) Databases
Structured, reliable, and transaction-safe.
🔹 Banking | ERP | Order Management
Examples: MySQL | PostgreSQL | SQL Server

2. 𝗖𝗼𝗹𝘂𝗺𝗻𝗮𝗿 Databases
Built for analytics & fast aggregations.
🔹 Data Warehousing | BI | OLAP
Examples: Redshift | ClickHouse | HBase

3. 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 Databases
Optimized for metrics, events, and IoT data.
🔹 Monitoring | IoT | Real-time Analytics
Examples: InfluxDB | TimescaleDB | QuestDB

4. 𝗜𝗻-𝗠𝗲𝗺𝗼𝗿𝘆 Databases
Ultra-low latency & millisecond responses.
🔹 Fraud Detection | Real-time Apps | Caching
Examples: Redis | SAP HANA | Ignite

5. 𝗚𝗿𝗮𝗽𝗵 Databases
Focus on relationships & connections.
🔹 Social Networks | Recommendations | Knowledge Graphs
Examples: Neo4j | JanusGraph | Cosmos DB

6. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 Databases
Flexible schemas for semi-structured data.
🔹 CMS | User Profiles | Mobile Apps
Examples: MongoDB | Couchbase | Firestore

7. 𝗩𝗲𝗰𝘁𝗼𝗿 Databases
Powering AI & semantic search.
🔹 Generative AI | RAG | Similarity Search
Examples: Pinecone | Milvus | Weaviate

8. 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 Databases
Store data as objects aligned with code.
🔹 CAD/CAM | Complex Applications
Examples: db4o | ZODB

9. 𝗕𝗹𝗼𝗰𝗸𝗰𝗵𝗮𝗶𝗻 Databases
Immutable, secure, and decentralized.
🔹 Supply Chain | Provenance Tracking | Identity Verification
Examples: BigchainDB | CovenantSQL

Tags:
#SQL #NoSQL 

Referred Link - https://www.linkedin.com/posts/vsadhwani_want-to-switch-from-software-engineer-to-activity-7339675261728083968-qaIF 

Study this roadmap:

You already know the SDLC processes – now dive into how to build, run and deploy cloud-native apps

3-Month Foundation :
• Learn core Linux (permissions, systemd, logging)
• Understand networking (VPC, DNS, NAT, firewalls)
• Pick a cloud provider (AWS / GCP / Azure / OCI)
↳ Focus on IAM, Compute, Storage, networking basics, billing fundamentals etc
• Learn Python for automation (already familiar? think how you can use with Boto3, SDKs, scripting)
• Learn Infrastructure as Code (Terraform / Pulumi/ AWS Cloudformation)
• Explore cloud-native patterns (microservices, managed services)
↳ Target: Junior Cloud Engineer roles (ave comp ~$100k)

You already know Git — now use it to:
• Version control your Terraform infrastructure
• Track changes to IaC and cloud automation scripts
• Set up GitOps-style deployment patterns

3-Month Advanced Track :
• Pick a specialty (Networking, Data, Databases, DevOps, Security, AI/ML etc)
If you picked DevOps:
• Work with containers (build/run with Docker)
• Deploy apps using Kubernetes (pods, services, configs)
• Set up CI/CD pipelines (using cloud-specific services)
• Implement cloud security concepts (IAM, roles, secrets management)
• Configure monitoring & logging (Cloud-Specific or Prometheus/Grafana)
• Automate end-to-end workflows(from build to deploy) using Python or Shell (SDKs, scripts, cron) - [this is very advanced]
↳ Target: Mid-level Cloud Engineer roles (ave comp ~$125k)

Leverage your SWE skills:
• Containerize existing apps
• Build pipelines for test → deploy
• Refactor into cloud-native services

Quick Wins:
- Complete Qwiklabs or provider-specific cloud labs
- Sign up for free tiers and experiment (watch your credit limits)
- Build and deploy 3 cloud-native projects
- Earn a practitioner or specialty certification (based on your tech exposure)

Free Resources to start, here's your one stop shop: https://lnkd.in/dF9xVE9X

Do this next: You've already built apps - now think which cloud-based services you'd need to run it on cloud (deploy it with Terraform + CI/CD)

Hands-on experience >> watching videos or reading blogs.

Remember, you're not starting from scratch. As a Software Engineer, you already think in systems.
Now think in cloud systems 

Tags:

#FreshersLearning, #LearningRoadmap, #CloudEngineer,  

Referred Link - https://www.linkedin.com/posts/aishwarya-pani-63a476167_dataengineering-azuredataengineer-apachespark-activity-7337326842099470337-8reh

𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗙𝗿𝗲𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀:

1. 𝗘𝗻𝗱-𝗧𝗼-𝗘𝗻𝗱 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗔𝘇𝘂𝗿𝗲
https://lnkd.in/gykp5HNy

2. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 | 𝗔𝗽𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀
https://lnkd.in/gBcDNUDK

3. 𝗔𝗽𝗮𝗰𝗵𝗲 𝗦𝗽𝗮𝗿𝗸 | 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 & 𝗦𝗮𝗹𝗲𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/gjZ8vuxA

4. 𝗢𝗹𝘆𝗺𝗽𝗶𝗰𝘀 𝗗𝗮𝘁𝗮 | 𝗔𝘇𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/gb_gwT3R

5. 𝗜𝗥𝗖𝗧𝗖 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/g34szQAV

6. 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗙𝘂𝗹𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
https://lnkd.in/gsPWybWw

7. 𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 | 𝗥𝗲𝗮𝗹 𝗖𝗿𝗶𝗰𝗸𝗲𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
https://lnkd.in/gE-qnnrD

8. 𝗔𝘇𝘂𝗿𝗲 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲
https://lnkd.in/gMM2MDcz 

Tags: 

#DataEngineering hashtag#AzureDataEngineer hashtag#ApacheSpark hashtag# 

Referred Link - https://www.linkedin.com/posts/vsadhwani_if-youre-looking-to-get-hands-on-with-cloud-activity-7333878302664712193-cbOm

 Referred Link - https://www.linkedin.com/feed/update/urn:li:activity:7330961599794425856/

These 8 channels will teach you more than any university degree 👇

Don’t waste time scrolling through endless ML/AI tutorials – these are the ones worth your time:

1.) Andrej Karpathy: https://lnkd.in/gcbV65Qd
Former Tesla AI Director and OpenAI cofounder who builds neural networks from scratch with clear code anyone can follow. His "Zero to Hero" series is legendary for making deep learning accessible.

2.) sentdex: https://lnkd.in/gt42jCgd
Harrison Kinsley creates real AI applications showing every step from blank file to working solution. Covers everything from stock prediction to computer vision with practical Python code.

3.) 3Blue1Brown: https://lnkd.in/gzjCSA-E
Grant Sanderson delivers stunning visual explanations of math concepts underlying ML. His neural network series makes complex math easy to understand.

4.) Krish Naik: https://lnkd.in/gDc4gbfm
Industry practitioner providing end-to-end ML project tutorials with coding exercises. Great for data scientists looking for implementation-focused content.

5.) Yannic Kilcher: https://lnkd.in/gdnNE32V
Explains the latest AI research papers in the simple terms the same day they're released. Breaks down transformers, diffusion models, and reinforcement learning without the academic jargon.

6.) Serrano Academy: https://lnkd.in/gTGaUSmY
Luis Serrano explains complex ML concepts using simple analogies and visualizations. Perfect for beginners wanting to grasp fundamental concepts.

7.) DeepLearningAI: https://lnkd.in/guPvZuCt
Andrew Ng's channel offering structured courses on ML/DL fundamentals. Features interviews with AI leaders and practical tutorials from industry experts.

8.) MIT OpenCourseWare: https://lnkd.in/gQpAgCEb
Top-tier university-level courses on math foundations powering modern ML. Connects theory to practice, explaining why algorithms work, not just how to use them.

Tags:

#ArtificialIntelligence, #MachineLearning, #E-Learning, #FreshersLearning,