Suraj Paliwal

Real Kafka Problems Asked in Flipkart-Style Interviews 1. Duplicate Order Events Scenario:- Your Order Service publishes order-created events to Kafka. Suddenly customers are charged twice. Root Cause:- Possible reasons: Producer retries Consumer reprocessing due to offset commit failure Non-idempotent consumers Kafka guarantees at-least-once delivery, so duplicates can occur. Solution:- Use idempotent producers Store eventId in DB Make consumer idempotent Example logic: if(eventAlreadyProcessed(eventId)) {   return; } processOrder(); 2. Consumer Lag During Sale (Big Billion Day Scenario) Scenario During a huge sale, Kafka consumer lag becomes millions of messages. Root Cause:- Too few partitions Heavy processing in consumer thread Database bottleneck Poor partition strategy can cause one partition doing most work. Solution:- Increase partitions Add more consumers Use async processing Batch database writes 3. Kafka Topic Disk Full Scenario Kafka brokers suddenly stop accepting messages. Root Cause Topic retention misconfigured → data not deleted. If disk fills up and retention is ignored, Kafka performance degrades severely. Solution:- Configure retention log.retention.hours=168 Monitor topic size Enable compaction where needed 4. Event Replay Causes Business Issues Scenario:- After fixing a bug, engineers replay Kafka events. Unexpected results: Duplicate emails Duplicate payments Multiple workflow triggers Event replay can break systems if consumers are not idempotent. Solution:- Idempotent processing Event versioning Replay-safe consumers 5. Event Ordering Problem Scenario:- Inventory events are processed out of order. Example:- inventory=5 inventory=10 inventory=3 But consumer processes: inventory=10 inventory=5 inventory=3 Cause:- Kafka guarantees ordering only within a partition. Solution:- Use key-based partitioning. Example:- producer.send(new ProducerRecord<>("inventory", productId, event)); 6. Producer Flooding Kafka Cluster Scenario:- Thousands of microservices send events simultaneously. Kafka brokers become overloaded. When many producers and consumers compete for resources, brokers can be overwhelmed. Solution:- Throttling Backpressure Separate producer/consumer clusters

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Your salary as a DevOps Engineer: ₹10 LPA Your friend’s salary as a DevOps Engineer: ₹35 LPA Same tools. Same certifications. The difference? They think DevOps in business language. “Scaling” → means handling traffic with optimum infra “Reports” → written for business impact, not tool stats “Pipelines” → mean faster releases, fewer rollbacks “Costs” → planned before usage, not justified after “Automation” → measured in time and cost saved “Uptime” → means $ not lost in downtime “Infra design” → tuned for performance Think tech for business results, not resumes. Stop earning the “good tech, poor comms” salary. 54K+ read my DevOps and Cloud newsletter: https://lnkd.in/gg3RQsRK What do we cover:  DevOps, Cloud, Kubernetes, IaC, GitOps, MLOps 🔁 Consider a repost if this is helpful.

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I did some random Maths again to compare single-income v/s dual income household 👉 Case 1: Dual Income • You: 50 LPA post-tax • Spouse: 25 LPA post-tax • Expenses: 15 LPA • Savings: 60 LPA/year • Invested at 12% annually 👉 Case 2: Single Income • You: 50 LPA post-tax • Expenses: 15 LPA • Savings: 35 LPA/year • Invested at 12% annually Not taking into account the increments and inflation for now Now fast forward 15 years. • Case 1 = ~27 Cr • Case 2 = ~15.7 Cr That’s a ₹11+ Cr gap. And this is with just a 25 LPA second income The more the merrier Dual income isn’t only about money. • Less stress if one job is lost • More freedom to take career risks • Shared goals, faster compounding In a world where layoffs, visa stress, and burnout are real - A working spouse can be your ultimate financial safety net Considering the expenses in tier-1 city these days, dual-income is not optional anymore

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The SDE-1 interview round nobody prepares for - actually asked at Google, Uber, and Flipkart.  It's not pure DSA. It's not system design. It's DSA inside design patterns - and the questions look easy until the extension hits.  Google - SDE-1/2  1. Web Crawler  - Design a crawler starting from a seed URL. How would you swap duplicate-detection - hash set, bloom filter, distributed dedupe - without rewriting the crawl loop?  - Add prioritization swappable at runtime - BFS by depth, priority queue by relevance, polite per-domain rate limiting.  2. Real-Time Collaborative Document Editor  - Design a system where multiple users edit the same document live. Two users typing at the same character position — how do you stop one from overwriting the other?  - Support per-user undo / redo — Alice undoes only her own edits — while the doc stays consistent for everyone.  Uber - SDE-1  1. Ride Surge Pricing Engine  - Design real-time price adjustment by demand and driver supply per region. How would you swap surge algorithms - multiplier, time-of-day curve, ML-driven - without redeploying?  - A region needs a festival override - cap surge at 1.5x, then auto-decay. How does your design absorb this?  2. Distributed Task Queue  - Design a distributed task queue with workers picking by priority. Task B only runs after Task A - how does your design express dependencies and prevent cycles?  - Failed tasks need exponential backoff retry, then a dead letter queue after N attempts. How do you compose retry + DLQ without forking the worker loop?  Flipkart - SDE-1  1. Twitter / X Feed System  - Design a feed of the most recent tweets from everyone a user follows. How do you keep it fast when they follow 1,000 accounts, without scanning every tweet?  - A user wants to mute one account temporarily. How does your design apply the filter without rebuilding the merge?  2. BookMyShow Ticket Booking  - Design seat booking for a movie show. Two users tap the same seat at the same moment - only one should succeed. How does your design prevent the double-book without serializing every request?  - Users can hold seats for 5 minutes before paying. How does auto-release work without a polling thread per booking?  The pattern across all 6: the base is solvable by almost everyone. The extension is where they decide Hire or No Hire.  If you're only practicing the base and stopping when it works - you're practicing for a round that doesn't exist anymore.  Which of these 6 would you attempt first?

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<10 LPA → You write code. Between 10–20 LPA → You write better code. Between 20–30 LPA → You design systems. Between 30–50 LPA → You solve business problems. >50 LPA → You solve problems that directly impact company revenue. At senior levels, it’s less about code… …and more about impact. #CareerGrowth #TechTruths #EngineeringLife #Leadership #ProductMindset #TechCareers #WorkplaceWisdom

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Microsoft pays around 60+ LPA, and 75+ LPA CTC for more than 4+ years of experience at L61 Level 𝗥𝗼𝘂𝗻𝗱 1 – 𝗗𝗦𝗔 (𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗼𝗹𝘃𝗶𝗻𝗴): • Task: Choose a Data Structure and define two methods: • save(int num): Store incoming data. • best(): Return and remove the most frequent element. In case of a tie, return the most recently entered number. • Focus: Data structure selection, handling tie-breaking conditions, and optimizing for time complexity. 𝗥𝗼𝘂𝗻𝗱 2 – 𝗟𝗼𝘄-𝗟𝗲𝘃𝗲𝗹 𝗗𝗲𝘀𝗶𝗴𝗻 (𝗟𝗟𝗗): • Problem: Design a Snake and Ladder Game. • Requirements: Implementation of core entities (Board, Player, Dice, Snake, Ladder) and game logic. • Expectation: Discussion on class relationships, modularity, and clean code. Successfully implemented approximately 95% of the functional code during the session. 𝗥𝗼𝘂𝗻𝗱 3 – 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 (𝗛𝗟𝗗/𝗟𝗟𝗗 𝗛𝘆𝗯𝗿𝗶𝗱): • Problem: Design a Residential Elevator Control System (Ground + 10 floors). • Constraints: * Single shaft, real-time display updates (UP, DOWN, STOPPED). • Efficiency: Minimize wait time and unnecessary movement. • Physical Constraint: Deceleration distance (requires two floors to stop). • Safety: Door interlocks, emergency stop handling, and maintenance mode. • Focus: Deep-dive LLD with architectural trade-offs. Focused on state management (State Pattern), request scheduling algorithms, and safety fail-safes. 𝗥𝗼𝘂𝗻𝗱 4 – 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗔𝗱𝘃𝗶𝘀𝗼𝗿 (𝗔𝗔) 𝗥𝗼𝘂𝗻𝗱: • Discussion: Resume and past professional experiences. Extensive discussion on the most complex technical problems solved and the specific challenges faced. • Coding Task: Implement the stoi (string to integer) function in C++. • Focus: Handling edge cases (overflow, underflow, whitespace, non-numeric characters) and production-grade validation logic. The Microsoft SDE-2 process demands a high level of proficiency in object-oriented design and the ability to translate complex, multi-requirement physical systems (like elevators) into logical software components. While DSA remains a baseline, the emphasis is heavily on scalability, safety, and extensible class architecture.

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Razorpay interview, Software Engineer round. Continuing from Part 1. The interviewer wasn’t testing payment APIs. He was testing whether I understood how distributed systems fail. 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 𝟯: 𝗔𝘀𝘆𝗻𝗰 𝗦𝗲𝘁𝘁𝗹𝗲𝗺𝗲𝗻𝘁 𝗖𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻 Payment accepted instantly. Settlement happens later. User sees: “Payment Successful”, But funds may still be processing. Fix: • Payment Status • Settlement Status Never lie to users about transaction state. Razorpay payment flow docs: https://shorturl.at/juiFJ 𝗣𝗥𝗢𝗕𝗟𝗘𝗠 𝟰: 𝗥𝗲𝗱𝗶𝘀 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 𝗗𝘂𝗿𝗶𝗻𝗴 𝗣𝗲𝗮𝗸 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 Your idempotency protection disappears. Now duplicate requests flood the system. Fix: • Redis replication + failover • Fallback DB deduplication • TTL cleanup strategy Redis Sentinel docs: https://shorturl.at/2oKvN --- 𝗕𝗲𝘀𝘁 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗳𝗼𝗿 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗱𝗲𝗲𝗽𝗹𝘆: 📘 Designing Data-Intensive Applications https://dataintensive.net 📘 ByteByteGo System Design https://lnkd.in/dBgsAYfT 📘 Stripe Engineering Blog https://lnkd.in/dQcMfE_x 📘 High Scalability http://highscalability.com 📘 System Design Primer https://lnkd.in/dP_qr5Z2 --- 𝗧𝗵𝗲 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿’𝘀 𝗳𝗶𝗻𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: “What’s the hardest part of building payment systems?” 𝗜 𝗮𝗻𝘀𝘄𝗲𝗿𝗲𝗱: “Making sure money is never lost. Even when everything else fails.” He nodded. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗻 𝗜 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱: Payment systems are not CRUD apps with a Pay button. They are reliability engineering problems disguised as product features. And honestly, that’s what makes backend engineering interesting.

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💰 Atlassian → 40+ LPA 💰 Google → 40+ LPA 💰 Amazon → 40+ LPA 💰 Flipkart → 30+ LPA Shocking truth? For fresher SDE roles, these companies hire you mainly on DSA skills — not on tech stacks, frameworks, or fancy projects. Yet, 90% of candidates fail. I know, because I failed two such interviews myself before learning from rejection. ❌ Mistakes to avoid while preparing DSA: - Jumping to solutions → you never train your brain to think. - Rushing problems → depth > number of questions. - Skipping contests → mugging sheets won’t prepare you for real interview pressure. - Ignoring time & space analysis → brute force won’t scale in interviews. - Inconsistent practice → studying in bursts doesn’t build long-term recall. Understanding these mistakes is just the first step; you won’t improve until you practice them in real scenarios. The best way to develop this mindset? Mock DSA sessions with senior SDEs. They’ll pinpoint where you’re going wrong, push you to think beyond standard solutions, and prepare you for real interview pressure. ✔️ Connect with them here for FREE: https://lnkd.in/gD_MMAYH Real-time coding feedback from these experienced engineers will help you handle pressure, avoid mistakes, and crack the DSA interviews. They also help with end-to-end interview preparation, including upskilling, resume review and job search, so try it once. ✨ ✅ Consistency + strong basics + practice under pressure = success. #dsatips #dsastrategy #interviewprep #techinterviews #paidpartnership #dsamistakes

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🚨 Same experience. 🚨 Same tech stack. 🚨 Same company. Yet one engineer earns ₹18 LPA 💸 while another gets ₹42 LPA 🚀 What changed? ❌ Not luck ❌ Not “hard work” alone 👉 Business impact. Two engineers 👇 👨‍💻 Engineer A Knows AWS ☁️ Uses Kubernetes ⚙️ Builds CI/CD pipelines 🔄 👨‍💻 Engineer B Knows AWS ☁️ Uses Kubernetes ⚙️ Builds CI/CD pipelines 🔄 ALSO reduces cloud bills by 30% 📉💰 And THAT changes everything. Because companies don’t really pay for: ❌ Effort alone They pay for: ✅ Cost savings ✅ Efficiency ✅ Ownership ✅ Business value That’s exactly why FinOps is exploding right now 📈 Cloud cost has become one of the biggest expenses for companies. And engineers who can: 🔥 optimize AWS costs 🔥 reduce Kubernetes waste 🔥 right-size infrastructure 🔥 improve cloud efficiency become high-value assets 💎 Reality of 2026 👇 ⚠️ Knowing tools = Average ⚡ Understanding optimization = Advantage Anyone can deploy infrastructure. Very few engineers can: 👉 save lakhs in cloud cost 👉 scale systems efficiently 👉 prevent unnecessary spending That’s what creates: 💰 higher salaries 🚀 faster promotions 🏆 stronger career growth So if you want to grow faster in Cloud & DevOps: Don’t just learn: ☁️ AWS ⚙️ Kubernetes 🏗️ Terraform Also learn: ✅ FinOps ✅ Cost Optimization ✅ Resource Efficiency ✅ Cloud Governance Because engineers who save money… often become more valuable than engineers who only deploy resources. Ask yourself 👇 Are you just managing cloud infrastructure… or actually creating business value? 💡 ♻️ Repost if you agree. #FinOps #AWS #CloudComputing #DevOps #Kubernetes #Terraform #CloudEngineering #CostOptimization #CloudCareers #DevOpsEngineer #AWSCloud #PlatformEngineering #InfrastructureAsCode #CareerGrowth 🚀

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Swiggy asked these SQL questions in a Senior Data Engineering interview (5+ YOE) CTC – 44 LPA | Difficulty: Intermediate 1.⁠ ⁠Compute YoY growth in revenue per customer 2.⁠ ⁠Find suppliers with stock < 50 for 2+ consecutive days 3.⁠ ⁠Rank employees by total sales per store for 2023 4.⁠ ⁠Get customers who placed orders from 3 different locations in 10 days 5.⁠ ⁠Find top 5 busiest restaurants by orders per day 6.⁠ ⁠Retrieve the last successful payment per user 7.⁠ ⁠Pivot rider onboarding data into month-wise columns 8.⁠ ⁠Find restaurants with 4+ star ratings consistently for 6 months 9.⁠ ⁠Compare revenue trends between premium and regular users MoM 10.⁠ ⁠Detect schema drift in historical feedback JSON data 11.⁠ ⁠Find delivery partners with 50+ deliveries but none in last 30 days 12.⁠ ⁠Identify users who ordered on all Sundays in the past month 13.⁠ ⁠Rank dishes by popularity in each cuisine by month 14.⁠ ⁠Find duplicate user accounts using phone/email matching 15.⁠ ⁠Get top cities with most delivery delays during peak hours 16.⁠ ⁠Calculate 7-day moving average of total order value per city 17.⁠ ⁠Find customers whose first and last order are on the same day 18.⁠ ⁠List customers who ordered from 3+ cuisines in the same week 19.⁠ ⁠Detect failed orders that later succeeded on retry 20.⁠ ⁠Identify login streaks of 3+ days for active users If you are looking for a platform to help you prepare for Data Engineering roles in top tech companies, then I’d suggest checking Bosscoder Academy. 👉🏻 Check them here: https://bit.ly/4lld14q They have helped over 800+ tech professionals to switch to their dream companies, and you too can switch your career with their help through: 💡A structured curriculum covering ETL, Big Data, Cloud, Sql and Advanced Data Ops. 💡Mentorship from experts at Google, Samsung, and more. 💡Hands-on projects to build a portfolio in Big Data and data processing. 💡Resume building, mock interviews & 100% job switch assistance. #dataengineering #swiggy #sql #interviewprep #bigdata #careertransition #data #Collab

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Same experience. Same tech stack. Same city. One earns ₹20 lakhs. The other earns ₹70 lakhs. The difference? Company type. 💼 Service Companies (TCS, Infosys, Wipro): • Senior Engineer: ₹18-25L • Pay model: Billable hours • Focus: Cost efficiency, volume • Increments: 8-12% annually Product Companies (Google, Microsoft, Amazon): • Senior Engineer: ₹70-100L • Pay model: Innovation + impact • Focus: IP creation, scalability • Total comp: Base + equity (₹30-40L in stocks) Why the 4X gap? Service firms measure output in hours worked. Product firms measure output in problems solved. And here's what's changing in 2025: → More engineers are rejecting service offers → Tier-2 cities seeing product company expansions → Equity becoming 30-40% of total compensation in product roles → AI/automation hitting service companies harder The "safe career path" of service companies? It's becoming the LOW-PAYING path. If you're in a service company earning ₹20L, Someone with YOUR exact skillset is earning ₹70L at a product company. That's not a gap. That's a different league. Are you playing in the right one? Source: Reddit r/IndianWorkplace, Deel-Carta Report 2025 #ProductVsService #TechCareers #CareerSwitch #SoftwareEngineering

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🚨 Oracle rescinds IIT & NIT job offers amid “internal restructuring” Oracle has reportedly pulled back campus offers made to students from institutes including IIT Hyderabad, Indian Institute of Technology, Kanpur, Indian Institute of Technology, Kharagpur, Indian Institute of Technology, Roorkee and multiple NITs, citing “internal restructuring” and headcount-related changes. For many students, Oracle was a Day 0 recruiter-and losing these roles at the end of the placement cycle has created deep uncertainty. Students like Aditya Kumar Barawal of NIT WARANGAL have publicly shared how their pre-placement offers were revoked after months of preparation and expectation. In parallel, the All IIT Placement Committee (AIPC), led by convenor John Jose, is reportedly weighing a two-year bar on Oracle’s participation in IIT campus placements, similar to earlier actions against firms that rescinded offers. This flashpoint comes against the backdrop of Oracle’s broader global workforce overhaul and AI-focused pivot, which has already led to significant layoffs and restructuring across its India operations. The immediate fallout is being felt by graduating engineers who must now re-enter a highly competitive job market at the last moment, even as placement cells and alumni networks try to open new doors. #Oracle #IIT #NIT #CampusHiring #JobOffers #IndianIT #TechTalent #AIAndJobs #WorkforceRestructuring #EarlyCareer #PlacementCells #EthicalHiring #FutureOfWork #TalentManagement #EngineeringGraduates #SaaS #Cloud #AIShift

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I’ve seen people go from 3 LPA → 20 LPA I’ve seen people go from 4.5 LPA → 24 LPA I’ve seen people go from 6 LPA → 30 LPA+ All because they focused on Data Engineering the right way. They DIDN’T: ❌ Learn 20 tools ❌ Do 10 certifications ❌ Memorize definitions ❌ Watch random videos daily Instead, they did this: ✅ Mastered SQL, Python, PySpark ✅ Practiced real-world scenarios ✅ Built 2–3 solid projects ✅ Learned ADF + Databricks + ADLS deeply ✅ Understood how to debug & optimize pipelines ✅ Practiced explaining architecture clearly ✅ Focused on consistency, not perfection Because Deloitte / PwC / EY / KPMG don’t hire you for tools. They hire you for clarity, problem-solving, and architecture thinking. If you improve your basics → Your entire career changes. 𝗞𝗲𝗲𝗽𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗶𝗻 𝗺𝗶𝗻𝗱, 𝗜 𝗵𝗮𝘃𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗮 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲 𝗳𝗼𝗿 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗚𝘂𝗶𝗱𝗲 👉 https://lnkd.in/eQRtDTRq LIKE & RESHARE if this helped 🙌

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Atlassian pays 30–40 LPA to SDE-1 and 70–80 LPA to SDE-2 And after sitting in enough interview rooms, one thing became obvious: 𝗗𝗦𝗔 alone is no longer enough to stay relevant. I’ve seen candidates solve every question perfectly and still not get hired. And I’ve seen others struggle with DSA but get a strong yes. The difference showed up the moment the problem stopped being clean. When things got messy, some engineers kept searching for the right pattern. Others started asking better questions, clarifying constraints, and shaping the problem itself. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘀𝗶𝗴𝗻𝗮𝗹 𝘄𝗮𝘀. The biggest gap I noticed? Engineers who could connect: → AI → data → systems vs those who could only write clean functions. In today’s world, understanding how things interact matters more than solving one function fast. And it was obvious who had built something real. End-to-end builders talked in: → trade-offs → failures → decisions That’s also why I find the approach at Masters' Union interesting. Their PGP in Applied AI & Agentic Systems is for engineers who want to go beyond cracking interviews and actually build with AI: https://lnkd.in/g6gx6zc8 And their UG in Data Science & AI is for those starting out early in this direction. Checkout: https://lnkd.in/gYCHbdkr → Industry-led curriculum with tech leaders from Google, Microsoft, Amazon → 200+ CXOs and operators teaching → Internships with EY, Zomato, Razorpay from Year 1 → Students building AI systems like chatbots, fraud detection systems, recommendation engines, and agents from Semester 1 → 60+ student-built startups → 6 Shark Tank pitches → ₹10 Cr+ student revenue → ~₹20 LPA (UG), ~₹33 LPA (PG) 𝗧𝗵𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗯𝗮𝗿 𝗵𝗮𝘀𝗻’𝘁 𝗯𝗲𝗰𝗼𝗺𝗲 𝗵𝗮𝗿𝗱𝗲𝗿. 𝗜𝘁 𝗵𝗮𝘀 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝘀𝗵𝗶𝗳𝘁𝗲𝗱. From: “Can you solve this fast?” To: “Can you think through something unclear, messy, and real?” What matters more today? #connections #hiring #interviews #faang #ai #MastersUnion #AppliedAI #EngineeringCareers #TechHiring

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Stripe pays 50-60 LPA for SDE-1 role and 1-1.1 crore for SDE-2 role This is the problem they asked You have a monolith with giant DB. Its having 50 TB of data and your monoliths get 100K writes per second. How will you migrate to microservice? You will think of dual writes first It won’t work as write to your new DB or monolith DB could fail. It might also increase latency of requests You will have to use - 1. Strangler pattern 2. CDC pipeline 3. Shadowing Every SQL or Postgres maintain write ahead log. You use debezium on top of it to convert logs intk messages and then stream to kafka for high throughput and write to your new database. Make sure to partition kafka on user_id and introduce idempotency for consistency. This way you will be able to have new writes but what about old 50TB data? Tell me in comments Once this is done you are ready to cutover. Keep serving your request from monolith but send it to microservice asynchronouslly to verify responses are same. Later transfer 1% traffic, then 10%, then 50% and finally 100% #microsoervices

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Why are only SBC → PBC jumps celebrated, not tough PBC → PBC switches? In 2025, I switched to Salesforce after spending almost 3 yrs at Microsoft. And let me tell you, this switch did NOT come easy. There were rejections from big names like Google, and others along the way. But here’s what I realized 👇 ✔️ A career move isn’t just about chasing a brand name. ✔️ It’s about choosing where you’ll actually grow, be challenged, and own impact. When I was preparing, I kept thinking → “If I just clear the DSA rounds, I’ll crack it.” But solving 200+ problems wasn’t enough. In interviews, I still stumbled when it came to patterns in Trees, Graphs, or tricky edge cases. That’s when I learned the real difference: 1️⃣ Pattern-based prep > random problem solving 2️⃣ Mocks for thinking clarity, not just coding speed 3️⃣ Real-world system design practice to explain trade-offs 4️⃣ Consistent feedback from mentors who’ve cracked these interviews themselves This shift not only helped me crack Salesforce but also made me a stronger engineer overall. And if you’re preparing for your next big switch while managing a 9-5, I highly recommend getting guidance from industry experts at Bosscoder Academy. 🔗 Explore their programs here: https://bit.ly/42dUMXc They’ve already helped 2,200+ SDEs make their switch to top product-based roles with a well-structured curriculum for DSA, SD, and full-stack development, 1:1 mentorship from industry experts, and 100% job switch support. #career #jobwitch #systemdesign #dsa #collab #tech #pbc #growth

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From Production —>1.05 LPA to .NET Developer —> 2.2 LPA, then QA —> 5 LPA, then Data Engineer —> 13 LPA, and now SDE / Senior DE —> 24+ LPA — all within just 5 years of experience. Every step felt like starting over, but each one taught me something important… the transition isn’t hard when you understand what the market truly expects. I wasn’t lucky. I was rejected more than 20 times. And in the beginning, I had no roadmap, no mentor, no clarity. I explored DevOps, Infra, MLOps, even Data Science — but nothing clicked, because the actual industry expectations were very different from what I imagined. Only when I figured out: • What companies really look for • What to learn first • What to ignore • How to present my past experience • And how to build a clean transition story …everything changed. I cracked 10+ offers in a very short time. So here’s the truth: Just “knowing Data Engineering” won’t get you placed. Knowing how to transition into it will. If you’re coming from a background that feels unrelated to data, you don’t have to spend months figuring this out the hard way. If you need clarity, direction or real guidance for your transition, https://lnkd.in/g6b9S8f6 #DataEngineering #CareerSwitch #LearningJourney #Cloud #Mentorship

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🚨 CS / IT Students & Software Engineers — WAKE UP Layoffs are not coming. They are HERE. 👉 2023–2025: 500,000+ tech layoffs globally ✋ Last Layoff - Oracle 30K+ 👉 Big names. Big salaries. Gone. 👉 Skill ≠ Job Security anymore Let that sink in. --- ❌ The Old Rule is DEAD > “Learn coding → Get job → Safe life” That formula is obsolete. Today: AI writes code ⚡ Companies cut cost 💸 Average engineers get replaced 🤖 --- ⚠️ If You Don’t EVOLVE, You EXIT Simple. You are not competing with: Your classmates Your colleagues You are competing with: AI tools 10x engineers Global talent 🌍 --- 💡 Survival = Upgrade If you’re in CS/IT, this is your new minimum stack: 🔥 1. Master LLM Tools Prompt engineering AI-assisted development Automation mindset If you’re not using AI daily → you’re already behind. --- ⚡ 2. Vibe Coding > Traditional Coding Build fast Prototype faster Ship ideas, not perfection Speed is the new IQ. --- 🚀 3. Learn 1 Extra Skill (Non-Negotiable) Pick ANY: Product thinking UI/UX Marketing Sales Communication 👉 Tech + Business = Power combo --- 🧠 4. Think Like a Creator, Not Employee Ask: “What can I build?” Not: “Who will hire me?” --- 🛑 Plan B is NOT Optional You need: Freelancing skill 💻 Side income 💰 Startup idea 💡 Because… > Salary can stop anytime. Skills + Income streams don’t. --- 📉 Reality Check If you: Only know basic coding Avoid new tools Wait for campus placement 👉 You are already in the layoff pipeline. --- 🧩 The Smart Move Start NOW: Build projects with AI Share work online Solve real problems Create value --- 🔥 Final Truth > Job security is a myth. Skill security is real. --- 👊 Your Choice Adapt → Grow → Win OR Ignore → Delay → Layoff list You may see my YouTube Channel with Python,ML,DS,AI automation, Go High level playlist --- By Dr Balaji Shetty Empowering Engineers for the AI Era 🚀

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