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Joyatres Technology originated from the idea that there exists a class of readers who respond better to online content and prefer to learn new Technology at their own pace from the comforts of their class rooms
We are a live & interactive e-learning platform with the mission of

making learning accessible to everyone. We offer instructor-led courses, along with 24/7 on-demand support to achieve highest course completion rates in the industry! Our real-life projects, 24*7 Support, Personal Learning Managers ensure that your learning goals are met!

09/07/2026

𝗧𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗲𝘃𝗲𝗿𝘆 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗮𝘀𝗸𝘀 𝗮𝗯𝗼𝘂𝘁 𝗖𝗼𝗽𝗶𝗹𝗼𝘁:
"Does our data leave our tenant?"
Here's the actual flow, step by step and the answer is in step 4.

𝗛𝗼𝘄 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗮𝗯𝗿𝗶𝗰 𝘁𝘂𝗿𝗻𝘀 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 𝗶𝗻𝘁𝗼 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀:

𝟭. 𝗧𝗵𝗲 𝗜𝗻𝗽𝘂𝘁
• Your prompt, the chat history from the session, your 𝘂𝘀𝗲𝗿 𝘁𝗼𝗸𝗲𝗻, and system metadata with a metaprompt
• Note the user token. Everything downstream runs under 𝘆𝗼𝘂𝗿 𝗶𝗱𝗲𝗻𝘁𝗶𝘁𝘆, not a shared service account

𝟮. 𝗚𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴: 𝗧𝗵𝗲 𝗣𝗮𝗿𝘁 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀
• Copilot pulls from the Fabric workspaces and items 𝘆𝗼𝘂 𝗵𝗮𝘃𝗲 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼, the data models and schemas, and item metadata
• "You" is doing a lot of work in that sentence. Copilot cannot ground on data you couldn't already open yourself
• 𝗬𝗼𝘂𝗿 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝘆. This is the single most important thing to understand about enterprise Copilot

𝟯. 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
• Grounding, context assembly, and prompt enhancement happen inside 𝗙𝗮𝗯𝗿𝗶𝗰 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆
• The model never sees your raw question alone. It sees a constructed prompt with the relevant schema and context attached

𝟰. 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗖𝗮𝗹𝗹
• The preprocessed input goes to 𝗔𝘇𝘂𝗿𝗲 𝗢𝗽𝗲𝗻𝗔𝗜 𝗦𝗲𝗿𝘃𝗶𝗰𝗲, managed by Microsoft
• Tokenizer, embeddings, processing, response
• Explicitly 𝗻𝗼𝘁 the public internet, and 𝗻𝗼𝘁 public OpenAI services. That distinction is the whole compliance story
Your data stays within your capacity's geographic region and compliance boundary

𝟱. 𝗣𝗼𝘀𝘁𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
• 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗰𝗵𝗲𝗰𝗸𝘀, query evaluation, and additional model calls if needed
• Output filtering happens on the way back, not just on the way in

𝟲. 𝗥𝗲𝘁𝘂𝗿𝗻
• The result comes back to the user through Copilot in Fabric or Power BI Desktop
• Consuming 𝗙𝗮𝗯𝗿𝗶𝗰 𝗰𝗮𝗽𝗮𝗰𝗶𝘁𝘆 along the way, which is worth watching on your bill

𝗧𝗵𝗲 𝘁𝘄𝗼 𝘁𝗵𝗶𝗻𝗴𝘀 𝘄𝗼𝗿𝘁𝗵 𝘁𝗮𝗸𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀 𝗱𝗶𝗮𝗴𝗿𝗮𝗺:
Grounding is scoped by existing permissions, so your Copilot rollout is only as safe as your workspace access model already was. 𝗙𝗶𝘅 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 you enable Copilot, not after.

And the model runs inside a 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁-𝗺𝗮𝗻𝗮𝗴𝗲𝗱 𝗔𝘇𝘂𝗿𝗲 𝘀𝗲𝗿𝘃𝗶𝗰𝗲, isolated from public endpoints. That's the answer to the question your security team is about to ask.

Have you audited workspace permissions before turning Copilot on?

PS: Found this useful?
Join 3,000+ AI architects and engineering leaders from Microsoft, Google, IBM, PwC and others reading my weekly newsletter 𝗗𝗶𝗮𝗿𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁.

I break down real enterprise AI systems, agentic patterns, and what actually works in production.

09/07/2026

AI Agents: 5 Architecture Patterns Every AI Architect Should Know

Building an AI Agent is not just about connecting an LLM to a few tools.
The ex*****on pattern you choose determines the system's latency, cost, reliability, scalability, and level of autonomy.

Here are 5 important patterns:

🔹 1. Single-Shot / Direct Invocation
Input → LLM → Output
Best for classification, extraction, summarization, and simple transformations.

🔹 2. Iterative ReAct
Decide → Act → Observe → Repeat
Best for tool-using agents, research, database lookups, and dynamic problem solving.

🔹 3. Planner–Executor
Plan → Decompose → Execute → Aggregate
Useful for complex workflows where tasks can be independently executed and monitored.

🔹 4. Reflexive Agent
Generate → Critique → Refine
Useful when output quality matters and iterative improvement is valuable.

🔹 5. Verifier-Gated Agent
Generate → Verify → Pass/Reject → Execute
Critical for high-risk workflows such as payments, compliance, authorization, and policy enforcement.

🏗️ Production-grade Agent Architecture

These patterns don't have to exist in isolation.

A robust system can combine:
Planner + ReAct Executors + Memory/State + Reflexive Review + Verifier + Guardrails + Observability

The key principle is:
Don't make an agent more autonomous than the problem requires.
Start with the simplest pattern that solves the problem, then introduce planning, iteration, reflection, or verification only when they provide measurable value.

Right pattern → Right use case → Reliable AI system.

09/07/2026

Understanding VLF (Virtual Log Files) in SQL Server — Explained Simply 🚀

While learning SQL Server Transaction Log internals, one concept that caught my attention is VLF — Virtual Log File.

Think of the SQL Server transaction log (.LDF) like a large warehouse.

Instead of managing the entire warehouse as one huge space, SQL Server divides the log internally into smaller logical sections called VLFs.

📌 A few key points I learned:

🔹 VLF = Virtual Log File
A VLF is a logical section inside the physical transaction log file.

🔹 VLF ≠ separate .LDF file
Multiple VLFs together make up the transaction log.

🔹 Why VLFs?
They help SQL Server manage transaction-log space, including writing log records and reusing log space.

🔹 How are VLFs created?
When the transaction log is initially created or grows, SQL Server creates VLFs. The number and size depend on the SQL Server version and growth size.

🔹 Too many VLFs can be a problem
Repeatedly growing the log in very small increments can create excessive VLFs, potentially increasing overhead during operations such as recovery and startup.

🔹 LSN vs VLF

A simple way I remember it:

LSN → "Where is this log record in the sequence?"
VLF → "Which logical section of the log contains this space?"

The bigger picture:

Transaction → Log Records → LSN → Transaction Log → VLFs → Log Reuse → Log Growth

💡 My takeaway:
Proper transaction-log sizing and growth configuration are important DBA responsibilities. VLFs may be invisible during normal application work, but they become very important when troubleshooting transaction-log growth, recovery, and performance.

💬 Share your thoughts

For SQL Server DBAs:

1️⃣ How do you normally check VLF health in your environments?
2️⃣ Have you ever encountered a database with an unexpectedly high VLF count?
3️⃣ What is your preferred approach for configuring transaction-log growth?
4️⃣ In your experience, when has VLF configuration actually impacted recovery or startup performance?

I'd love to hear your real-world experiences and approaches.

09/07/2026

Autovacuum was running… but the dead tuples weren’t going away.”
we had a production PostgreSQL cluster where monitoring showed an unusual pattern.
One high-transaction table was growing rapidly, autovacuum activity was increasing, and transaction ID age was moving in the wrong direction.
The application was online.
Replication was healthy.
CPU and memory looked normal.
At first, it looked like autovacuum simply needed to run more aggressively.
But before changing any parameters, I wanted to understand why VACUUM wasn’t making progress.
I started with:
SELECT pid, usename, xact_start, state, query
FROM pg_stat_activity
WHERE xact_start IS NOT NULL
ORDER BY xact_start;
That exposed the real problem:
A long-running transaction had been left open for hours.
PostgreSQL uses MVCC, so VACUUM cannot remove dead tuples that may still be visible to an older transaction snapshot.
Autovacuum wasn’t broken.
It was doing exactly what PostgreSQL was designed to do.
I then checked:
SELECT relname,
n_live_tup,
n_dead_tup,
last_autovacuum,
autovacuum_count
FROM pg_stat_user_tables
ORDER BY n_dead_tup DESC;
The affected table had accumulated a significant number of dead tuples despite repeated autovacuum runs.
After confirming with the application team that the long-running transaction was safe to terminate, we ended it and allowed VACUUM to remove the dead tuples and make that space reusable.
We also reviewed:
• autovacuum_vacuum_scale_factor
• autovacuum_vacuum_threshold
• autovacuum_max_workers
• autovacuum_vacuum_cost_limit
But the important lesson was not to start by tuning autovacuum.
The better troubleshooting sequence was:
Table growth → Dead tuples → VACUUM activity → Oldest transaction → Root cause
We then added monitoring for long-running transactions, dead-tuple growth, transaction ID age, and autovacuum activity.
Production lesson:
When VACUUM appears ineffective, don’t immediately make it more aggressive.
First ask:
“What is preventing PostgreSQL from cleaning up?”
Sometimes the problem isn’t VACUUM.
It’s the transaction VACUUM is waiting to outlive.

09/07/2026

Microsoft Purview Data Loss Prevention (DLP)
Sensitive data can be shared through emails, Teams, SharePoint, OneDrive, devices, cloud applications, and even AI applications.
Microsoft Purview DLP helps organizations identify, monitor, and protect sensitive information from being accidentally or intentionally shared with the wrong people🛡️
🔎 DLP is more than simple keyword matching. It can detect sensitive information using:
• Sensitive information types and patterns
• Keyword matching
• Regular expressions
• Context and proximity analysis
• Machine learning and advanced detection methods 🤖

🌐 Beyond Microsoft 365: Inline Web Traffic DLP
One interesting capability is protecting sensitive information when users interact with web applications.
Using Microsoft Edge for Business and Network Data Security, organizations can help protect sensitive data across:
🤖 AI applications such as ChatGPT, Google Gemini, and DeepSeek
☁️ Supported cloud applications
🌐 Other web applications and services

This helps extend data protection beyond traditional Microsoft 365 services such as Exchange, SharePoint, and OneDrive.
🛡️ When sensitive data is detected, DLP can:
⚠️ Notify users
🚫 Block risky actions
🔒 Restrict data sharing
📝 Monitor activities
🔓 Allow overrides with business justification
💡 The key takeaway: DLP is not just about blocking users. It helps organizations protect sensitive information while allowing employees to work securely.
⚠️ Note: Some AI-related and Inline Web Traffic DLP capabilities mentioned above are currently in Preview and may have different availability or requirements

09/07/2026

𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐈𝐧𝐭𝐮𝐧𝐞 — 𝐌𝐨𝐝𝐞𝐫𝐧 𝐄𝐧𝐝𝐩𝐨𝐢𝐧𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭

Microsoft Intune is Microsoft’s cloud-based Unified Endpoint Management (UEM) service. It helps organizations manage and secure devices, applications, users, and organizational data from a central cloud platform.

☁️ How Microsoft Intune Works

The basic flow is:

IT Admin → Intune → Users & Devices → Policies / Apps / Security → Compliance & Reports

1. Administrator creates policies in the Intune admin center.

2. Policies are assigned to users or device groups.

3. Devices enroll or register according to the organization's management model.

4. Intune delivers configuration profiles, applications, security policies, scripts, updates, and other management settings.

5. Devices check in and report status back to Intune.

6. Administrators use reports and monitoring to identify issues and take corrective action.

🧩 What can Intune manage?

📱 Devices

Windows

macOS

iOS/iPadOS

Android

Supported Linux scenarios

📦 Applications

Microsoft 365 apps

Win32 applications

Line-of-business applications

Mobile applications

App Protection Policies

🛡️ Security

Compliance policies

Security configuration

Microsoft Defender integration

Conditional Access integration

Endpoint security policies

⚙️ Configuration

Wi-Fi

VPN

Certificates

Restrictions

Device configuration

Scripts and automation

🎯 When should you use Intune?

Intune is particularly useful when an organization needs to:

✅ Centrally manage corporate devices
✅ Support remote and hybrid workers
✅ Deploy applications at scale
✅ Configure Windows and mobile devices remotely
✅ Enforce security and compliance requirements
✅ Support BYOD scenarios
✅ Protect organizational data within supported applications
✅ Integrate endpoint management with Microsoft Entra ID and Microsoft 365
✅ Build a modern, cloud-based endpoint management strategy

🔥 Important Intune Components

MDM → Manage the device
MAM / App Protection → Protect organizational data inside supported apps
Compliance → Evaluate whether devices meet requirements
Conditional Access → Use signals such as compliance to help control access
Endpoint Security → Configure security controls and integrate with security services
Reporting → Monitor devices, applications, policies, and compliance

💡
🚀 Key Takeaway

Microsoft Intune = Cloud-based endpoint management + application management + security + compliance + reporting.

The goal is simple:

👥 Empower Users → 💻 Manage Devices → 📦 Protect Apps & Data → 🛡️ Improve Security

09/07/2026

Day 14 of 50 Days of ServiceNow ITSM

📌 Topic: Root Cause Analysis (RCA) 🔍

Don’t just fix the symptom — find the cause!

When the same incident keeps happening, a temporary fix is not enough. RCA helps us identify the underlying cause and prevent the problem from recurring.

🔎 What did we cover?

✅ What is Root Cause Analysis?
✅ Why is RCA important?
✅ 5 Whys Technique
✅ Fishbone / Ishikawa Diagram
✅ Pareto Analysis
✅ Symptoms vs Root Cause
✅ Real-time production example
✅ RCA in the ServiceNow Problem process

💡 Real-Time Example:

VPN disconnects repeatedly → Restart VPN ❌ → Investigate logs 🔍 → Find certificate renewal failure → Fix the renewal process ✅

🎯 Key Takeaway:

Symptom = What happened?
Root Cause = Why did it happen?
Permanent Fix = How do we prevent it from happening again?

09/07/2026

𝐓𝐡𝐞 𝐀𝐈 𝐬𝐤𝐢𝐥𝐥 𝐠𝐚𝐩 𝐢𝐧 𝟐𝟎𝟐𝟔 𝐢𝐬𝐧'𝐭 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐭𝐡𝐢𝐧𝐤.

It's not between people who know AI and people who don't. Everyone "knows AI" now. The gap is between people who use one tool for everything and people who've built a toolkit.

That second group ships in hours what takes the first group days. Here's what their toolkit looks like:

𝐓𝐡𝐞 𝐬𝐤𝐢𝐥𝐥 𝐭𝐡𝐚𝐭 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐢𝐞𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐞𝐥𝐬𝐞:

1. Prompt Engineering : Every tool below is only as good as the instruction you give it. ChatGPT, Claude, Gemini, Perplexity. This isn't one skill among ten. It's the one that unlocks the other nine.

Automation (where AI stops being a novelty):

2. Workflow Automation : Wire AI into processes that run without you. Make, Zapier, n8n, Power Automate. Anyone can prompt once. The leverage comes from workflows that repeat automatically.

3. Chatbot Building : Smart chatbots for support and engagement. Botpress, Voiceflow, ManyChat. When a conversation pattern repeats a hundred times daily, automate it.

Visual and creative (the production collapse):

4. Video Generation : Engaging video without editing skills. Runway, Pika, Synthesia, HeyGen. What used to need a video editor and a full afternoon now takes minutes.

5. Image Generation : Visuals from prompts. Midjourney, DALL·E, Leonardo, Ideogram. The bottleneck moved from production skill to taste and ideas.

6. Presentations : Ideas to polished slides. Gamma, Tome, Beautiful.ai, Canva AI. The painful part of a deck was never the content. It was the formatting.

Content (the 70/30 split):

7. Content Writing : Blogs, captions, emails, scripts. ChatGPT, Jasper, Copy.ai, Notion AI. The winning move isn't letting AI write for you. It's using it for the first 70% then adding the judgment that makes it worth reading.

8. Audio and Voice : Voiceovers, podcasts, audio content. ElevenLabs, Murf, Descript. Professional audio used to require a studio. Now it requires a prompt and good taste.

Knowledge (the quiet superpower):

9. Research and Summarization : Digest a 60-page report in minutes. Perplexity, Elicit, Scholarcy, Humata. Being able to consume and synthesize information fast changes how quickly you learn and decide. This compounds across everything else.

10. Career Optimization : ATS-friendly resumes, cover letters, interview prep. Kickresume, Teal, Rezi. The job search process has its own AI toolkit now.

Here's what I've learned watching people who actually ship:

They don't know all ten. They picked 2-3 that match their actual work and got genuinely good. A marketer who masters prompting, content writing, and image generation outperforms one who dabbles in all ten.

09/07/2026

Choosing an LLM provider isn't just about choosing the best model.

You're also choosing:

• What you pay for
• Who serves it
• How it scales
• How much infrastructure you control

I noticed this while adding 3 different providers to Decode (the coding agent I'm building from scratch).

Specifically, I picked providers that represent 3 different ways of running inference:

𝟭/ 𝗚𝗲𝗺𝗶𝗻𝗶 → 𝗕𝘂𝘆 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹

This is the traditional proprietary API approach.

Google owns the model and infrastructure.
You send requests and pay for usage.

I added Gemini because its generous free tier makes it an easy way to experiment with Decode without paying anything.

𝟮/ 𝗢𝗽𝗲𝗻𝗥𝗼𝘂𝘁𝗲𝗿 → 𝗕𝘂𝘆 𝘁𝗵𝗲 𝘀𝗲𝗿𝘃𝗶𝗻𝗴

OpenRouter gives you one interface to a huge pool of models.

So you can experiment with different models without worrying about the underlying infrastructure.

3/ Modal → 𝗦𝗲𝗿𝘃𝗲 𝗼𝗽𝗲𝗻-𝘄𝗲𝗶𝗴𝗵𝘁 𝗺𝗼𝗱𝗲𝗹𝘀 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳

This gives you more control over the inference layer.

With Modal Endpoints, I can serve Qwen3.6 35B, GLM-5.2, or even swap in my own fine-tuned model.

Without changing anything in the agent harness.

But the most interesting difference is the economics...

Instead of paying per token, you can pay for GPU compute while the workload runs.

This is attractive for large, bursty workloads.

For example, imagine processing 1,000 documents containing roughly 30M input tokens.

Using Sonnet, my napkin math puts that at roughly $97.

Process the same workload batched at ~3,000 tokens/sec on a single H200 through Modal, and you're looking at under 3 hours.

At $4.54/hour, that's roughly $13 of GPU compute.

Of course, serverless GPU inference isn't automatically cheaper.

If Decode asks me for confirmation and I leave an H200 sitting idle overnight, I could burn another ~$45 doing absolutely nothing 😂

That's the point…
There isn't one "best" way to run inference.

• Interactive workloads might favor pay-per-token APIs.
• Large asynchronous workloads might favor serverless GPUs.
• Fine-tuned models might push you toward serving open weights yourself.

Which is why the most important architectural decision I made was keeping all of this outside the agent loop.

The loop doesn't know whether it's talking to Gemini, OpenRouter, Modal, or whatever provider I add next.

It simply requests the next completion.

Agent loop → Model interface → Provider

Keep that boundary clean and you can change the infrastructure underneath as your workload changes.

I break down exactly how I implemented this inside Decode in Lesson 2 of my open-source Building a Coding Agent From Scratch series.

09/07/2026

If I were preparing for a Network Security Engineer role today, here’s the roadmap I’d follow:

1. Networking Fundamentals
→ OSI Model
→ TCP/IP
→ IP Addressing & Subnetting
→ Routing & Switching
2. Network Services & Protocols
→ DNS
→ DHCP
→ NAT
→ Common Ports & Protocols
3. Threats & Attack Vectors
→ Reconnaissance
→ Scanning
→ MITM Attacks
→ DoS/DDoS
→ Password Attacks
4. Firewalls & Access Control
→ Packet Filtering
→ Stateful Inspection
→ ACLs
→ Firewalls
→ DMZ
5. Secure Communication
→ VPNs
→ TLS/SSL
→ HTTPS
→ SSH
→ PKI Basics
6. Monitoring & Detection
→ IDS vs IPS
→ SIEM Fundamentals
→ Packet Analysis
→ Log Monitoring
7. Wireless Security
→ WPA2/WPA3
→ Rogue Access Points
→ Wireless Hardening
8. Security Best Practices
→ Network Segmentation
→ Least Privilege
→ Patch Management
→ System Hardening
9. Hands-On Practice
→ Home Lab
→ Wireshark analysis
→ Troubleshooting Exercises
10. Advanced Topics
→ Zero Trust Architecture
→ Cloud Network Security
→ SDN
→ Modern Network Architecture

One thing I’d do differently today:
I wouldn’t start with tools, I’d start with networking.

Network Security is a combination role, and it can get overwhelming quickly.

Build strong networking fundamentals first; the best security engineers understand how systems communicate and where traffic flows.
→ Zero Trust Architecture
→ Cloud Network Security
→ SDN
→ Modern Network Architecture

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