Engineering Case Studies And Best Practices

Hospitals are healing patients faster with 30-year-old Australian technology. Most healthcare facilities still operate in the dark. SolarTube skylights channel natural sunlight through reflective tubes directly into patient rooms and treatment areas. No electricity needed. Just free healing light all day. The healthcare transformation numbers: ↳ Faster patient recovery rates documented ↳ 15% staff productivity increase ↳ Reduced eye strain for medical professionals ↳ Lower patient anxiety during procedures Think about that. Tigoni Medical Center in Kenya installed SolarTubes in their COVID-19 facility. Healthcare workers reported less fatigue, increased alertness during long shifts. Patients showed dramatically improved morale and energy levels. At Rogaska Medical Center, natural daylight flooded clinics without unwanted heat. Staff comfort improved. Patient outcomes followed. Italian dental offices meeting occupational daylight standards found something unexpected: patients felt less anxious. Procedures became more comfortable. Natural light calmed nerves that fluorescent bulbs couldn't. Traditional Healthcare Lighting: ↳ Fluorescent tubes causing eye strain ↳ High electricity costs ↳ Artificial environments ↳ Staff fatigue increases SolarTube Healthcare Reality: ↳ Natural light reduces stress hormones ↳ Serotonin production increases ↳ Circadian rhythms regulate properly ↳ Recovery accelerates naturally But here's what stopped me cold: We're medicating depression while keeping people in artificial light. Jim Rillie invented this solution in the 1980s. Launched Solatube International in 1991. Now 2 million units worldwide bring natural light indoors. Healthcare facilities that adopt it see measurable improvements. Staff wellness increases. Patient satisfaction scores rise. Recovery times shorten. The Multiplication Effect: 1 hospital = hundreds healing faster 100 facilities = thousands of staff energised 1,000 installations = healthcare transformed At scale = medicine working with nature VCC in the UK experienced enhanced well-being building-wide. Staff and patients reported feeling calmer, healthier, happier. Simply from abundant daylight. We're not just installing skylights. We're installing wellness. One beam of natural light at a time. Follow me, Dr. Martha Boeckenfeld for innovations that heal environments and people. ♻️ Share if you believe healthcare should harness nature's healing power.

✍️ From pipe to a functional prototype where imagination meets engineering. Constructing a DIY remote-control SUV 🚙from PVC pipes. This innovative project merges practical engineering with creative problem-solving, demonstrating how accessible materials can become sophisticated prototypes. 🛠️Step-by-Step construction process: 1️⃣ Blueprint & material planning: Create detailed sketches of your SUV design, calculate pipe dimensions, and source all electronic and mechanical components. 2️⃣ Shape & form the structure: Cut PVC pipes to size and use controlled heat application to bend sections into your desired frame geometry. 3️⃣ Build the foundation: Connect frame components using PVC fittings and adhesive, creating a robust yet lightweight chassis that can handle movement and stress. 4️⃣ Integrate electronics: Mount motors, wheels, battery pack, and RC receiver system, ensuring proper weight distribution and secure connections. 5️⃣ Test & Optimize: Conduct performance trials, fine-tuning steering response, motor speed, and overall vehicle stability for smooth operation. 💡Educational & professional value: ✓ Cost-Effective Innovation: Demonstrates rapid prototyping without expensive materials ✓ Technical Skill Development: Builds expertise in fabrication, assembly, and system integration ✓ Creative Engineering: Proves that innovation thrives with resourcefulness and imagination ✓ Systems Thinking: Develops understanding of mechanical, electrical, and structural design principles Through this tangible experience, learners develop problem-solving abilities, master real-world engineering skills, and gain the self-assurance to bring their visions to life. Video credit: schmdnm32 #Practicalskills #Engineering #PVCmodel #Creativity #Learningbydoing #DIY #mechanical #science #construction #SUV #handsonlearning #craftmanship

A junior pinged me late night … “Hey, sorry for disturbing so late, but the hub-allocation service just stopped writing events. I’ve been staring at the logs for an hour and can’t see it.” I was two chapters deep in a book, but a production freeze at Flipkart waits for no one. Ten minutes later we were on a call, screens shared, coffee in hand. What we saw: 1. CPU was fine, DB healthy. 2. Message Queue consumer lagging — but only for one partition. The suspect commit: a “tiny” config change that slipped past review because “it’s just YAML.” What we did: 1. Replayed the partition in staging → reproduced the freeze in 30 seconds. 2. Flipped the feature flag off, deployed a hotfix. 3. Wrote a one-liner unit test that fails if the critical topic/partition mapping ever changes without a version bump. Total downtime: 23 minutes. Total learning: off the charts. Three takeaways I shared with the team the next morning: 1. Small changes aren’t small in distributed systems. A single-line config tweak can strand an entire message bus. 2. Cultivate “safe-to-ping” culture. The bravest thing that junior engineer did wasn’t debugging at 1 a.m.; it was sending that message before things spiraled. 3. Automate the guardrails. Post-mortems are great, but a failing test is louder than any Confluence page. AfterMath: That junior pushed the unit test themselves, opened the merge request, and led the retro. Next sprint, they volunteered to refactor our event-routing configs; because now they own the problem. These are the moments that turn capable engineers into future tech leads.

"Ryan Singer and his Shape Up method is an incredible system that takes as input 'kludgy, clumsily scaled product org unsure about what to build and how' and delivers as output 'a focused, clear thinking set of teams that has a rhythm, a method, and a purpose.” That's how Des Traynor (co-founder of Intercom) described the impact Ryan Singer and the Shape Up method have on teams. Ryan spent nearly two decades refining a product development approach at 37signals that helped the company build super-successful products with small teams. Based on these lessons, he wrote a book called "Shape Up: Stop Running in Circles and Ship Work that Matters," which describes a different way of working that an increasing number of companies are adopting. If your team used to run smoothly but is now struggling to ship great product, this episode is for you. In our conversation, Ryan shares: 1. Why traditional Agile and Scrum methods often lead teams into endless cycles of work without meaningful shipping milestones. 2. The “appetite-driven” approach to product development where teams set fixed timeboxes (usually six weeks maximum) and vary the scope instead of expanding timelines. 3. The exact process for running effective “shaping” sessions that collaboratively define projects before committing resources. 4. How to adapt Shape Up principles to your company’s unique context, even if it’s nothing like Basecamp. 5. A step-by-step approach to transitioning from Scrum to Shape Up by piloting the methodology with a single team before broader implementation. 6. Practical techniques for bridging the engineering-design divide by bringing technical and product perspectives together earlier in the process. 7. The powerful “breadboarding” and “fat marker sketching” techniques that help teams align on solutions without getting lost in high-fidelity details. 8. The clear warning signs that your current development process is failing before it’s too late to change course. 9. Proven strategies to implement Shape Up methods, whether you’re working in a startup or enterprise environment. 10. Why the PM role shifts upstream in Shape Up, focusing more on problem definition than project management. Listen now 👇 • YouTube: https://lnkd.in/g87Z-CSt • Spotify: https://lnkd.in/geE-jFbM • Apple: https://lnkd.in/gRNSCHy7 Thank you to our wonderful sponsors for supporting the podcast: 🏆 WorkOS — Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lenny 🏆 Merge — A single API to add hundreds of integrations into your app: http://merge.dev/lenny 🏆 Airtable ProductCentral — Launch to new heights with a unified system for product development: https://lnkd.in/g72e_Eie

Apache Iceberg has levels to it: - level 1 Creating and querying tables. Understand manifest files (row-level metadata) and snapshots (table-level metadata). Learn the IDENTITY and TRANSFORM partition types (e.g., days(), bucket()), because partition columns aren’t physical columns anymore. Master these basics and you’ll never mistake Iceberg for “just another Hive table.” - level 2 Reliable writes & schema evolution ACID guarantees on object storage: every write creates a new immutable snapshot; readers see the last-committed snapshot—no “eventual consistency” wobbles. Schema evolution that actually works: add, drop, rename, reorder columns. Column IDs keep queries stable even when names change. Partition spec evolution: start with days(event_time), change to hours(event_time) later—existing data keeps its original spec - level 3 Time travel, branching & row-level deletes SELECT * FROM my_table VERSION AS OF 2025-04-01; ⏪—query any snapshot. Branches & tags = Git for data: test compaction on a branch, fast-forward if it looks good. Position vs. equality deletes: choose between deleting specific rows in a file or all rows that match a predicate. These features make GDPR erasure and replay debugging boring—and that’s a good thing. - level 4 Maintenance & performance tuning rewrite_data_files → bin-packs tiny files; rewrite_manifests → collapses metadata bloat. expire_snapshots & remove_orphan_files keep S3/GCS bills sane. Hidden partition pruning: compute skips partitions automatically—no need for event_date columns. Watch the scan plan (EXPLAIN ANALYZE) to verify pruning and filter-pushdown are working. A single stray CAST can destroy performance. Remember to make your WHERE clause sargable! - level 5 Ecosystem mastery & advanced patterns Query the metadata tables (my_table.files, …history, …manifests) for instant ops dashboards. Streaming upserts with Flink or Spark Structured Streaming using MERGE INTO + row-level deletes. Trino, DuckDB, Snowflake, Databricks, BigQuery all speak Iceberg now—design once, query anywhere. Combine with branch-per-feature pipelines to enable true CI/CD for analytics. Use Iceberg’s compiler-style metadata to treat your data lake like a versioned database, not a folder of Parquet files. What else did I miss for mastering Iceberg?

Being good at DSA & CP ≠ Being good at real-world software engineering.  I’ve seen this happen so many times, someone crushes coding rounds but struggles once they’re building systems.  Why?  Because real-world engineering isn’t just about solving problems. It’s about handling scale, concurrency, memory, and reliability, all at once.  Take a basic API. Sounds easy, right?  Now add multithreading, async calls, memory leaks, and thousands of requests per second—and suddenly, it’s chaos.  This is where CS fundamentals make or break you.  Here are 25 topics to help you bridge the gap between DSA and real-world projects:  ➥Concurrency and Multithreading   1. Thread Safety – Keeping shared data safe.   2. Mutex and Locks – Controlling access to resources.   3. Semaphores – Managing resource limits.   4. Condition Variables – Synchronizing threads properly.   5. Deadlocks and Starvation – Spotting and fixing them.   6. Atomic Operations – Performing thread-safe updates.   7. Thread Pools – Efficiently managing tasks.   8. Producer-Consumer Problem – Solving real-world concurrency issues.  ➥Memory Management   9. Heap vs Stack – When to use what.   10. Memory Leaks – Finding and fixing them.   11. Garbage Collection – How it works and where it fails.   12. Object Pooling – Reusing objects to save memory.   13. Paging and Segmentation – OS-level memory handling.   14. Caching Strategies – LRU, LFU, and cache eviction.  ➥Networking and Security   15. TCP/IP Basics – How connections actually work.   16. DNS Resolution – What happens when you hit enter on a URL.   17. SSL/TLS Handshake – How secure connections are set up.   18. OAuth and Token-Based Auth – Securely handling user sessions.   19. Session Management – Preventing hijacks and managing state.   20. Firewalls and Proxies – Protecting your network.   21. Load Balancers – Distributing traffic without breaking systems.  ➥System Design and Architecture   22. Event-Driven Systems – Managing async workflows.   23. Microservices Architecture – Building distributed systems.   24. Database Indexing – Making queries faster at scale.   25. CAP Theorem – Understanding consistency, availability, and partitioning trade-offs.  DSA gets you interviews.  CS fundamentals help you build systems that work.  – P.S: I’ve been getting 10+ queries regarding DSA, HLD, and LLD daily So, to answer all, I’ve launched my One Stop Resource guide for aspiring software engineers. This guide help you with: - full roadmap of DSA, HLD, and LLD for interviews - good resources that I used included to save you time - lots of problems and case studies for DSA and system design Here’s the link: https://lnkd.in/e-detVTg (220+ students are already using it)

Learning from your own mistakes is good; learning from others’ is efficient. Intel is a fascinating case study in slow erosion - it didn’t fall off a cliff, it wandered down a well-paved road of reasonable decisions that calcified into drift. Lessons from the slow fade: 1. Paranoia is a process, not a poster Andy Grove lived “Only the paranoid survive.” After him, Intel kept the slogan, lost the muscle. They missed the smartphone boom, underestimated GPUs, got complacent in manufacturing. → Schedule paranoia. Put “what would kill us?” on the calendar and fund the answers. 2. The Opportunity Cost of Saying “No” Intel turned down Apple’s request to make chips for the first iPhone. That one decision foreclosed entry into mobile - the biggest platform shift of the century. →A reflexive “no” protects today’s P&L but mortgages tomorrow’s TAM. Explore the upside before you shut the door. 3. The Innovator’s Dilemma Is Real Intel’s CPU business was the proverbial creosote bush: so profitable it poisoned everything planted nearby. Phones, graphics, accelerators were starved. Those niches became the on-ramps for rivals. →Set up separate, empowered teams to chase disruptive bets, even at short-term pain. Beware the margin jail. 4. On Time is a Feature For decades, Intel’s Tick–Tock cadence - new process one year, new architecture the next - was the industry's metronome. Then came the long 10nm delay, a recipe that slipped for years, and the beat broke. Buyers diversified, then normalized diversification. → In B2B, reliability is something customers buy. 5. Speed beats size Intel once set the industry’s tempo. Then TSMC and Samsung iterated faster, while NVIDIA seized the AI GPU wave. Scale without cycle-time discipline becomes a molasses machine. → Fight entropy with smaller pods, WIP limits, cycle-time KPIs. 6. Process heroics without customer proof is theater New fabs are glamorous. Empty fabs are expensive. “Build it and they will come” isn’t a strategy. → Utilization, not hope, should gate big spends. Secure anchor tenants first, pour concrete second. 7. Vertical-integration romance meets service-business reality Intel’s heritage is IDM (Integrated Device Manufacturer): design and manufacturing under one roof. Expanding into a foundry (building chips for others) sounds adjacent, but it’s a service business. Winning means boring glue: PDKs, IP libraries, packaging, predictable ramps. → Specs win headlines; service wins purchase orders. 8. Don’t stack all your risk on one critical path Intel’s 10nm push packed too many “firsts” into one roll. Downstream roadmaps assumed it would all land. When the base slipped, everything slipped. → Elegant portfolios include side doors. Redundancy is the real elegance. Crowns are rarely lost in battle. They’re misplaced in drift. For founders, the rent for staying on the throne is simple: paranoia, speed, and reinvention.

There’s a role quietly shaping the future of enterprise AI: the forward-deployed engineer. AI breakthroughs are everywhere. But the real challenge? Deployment. 1. What is a Forward-Deployed Engineer? Not your typical software developer. An FDE embeds directly inside a business unit. They don’t build generic products; they build custom solutions for one specific client or team. They’re part engineer, part product thinker, part internal consultant. ⸻ 2. What do they actually do? Think of the FDE as a tactical operator. They: - Sit with end users to map real workflows - Identify friction, bottlenecks, and inefficiencies - Build full-stack solutions; from data pipelines to UIs - Integrate AI models into legacy systems - Iterate fast and deploy often They don’t hand off code; they own it until it works. ⸻ 3. Why does this role matter now? AI is no longer about proving what’s possible. It’s about making it practical. Enterprises don’t need another model demo. They need someone who can translate AI into business outcomes and that works inside their actual stack, with their actual people. FDEs make that leap happen. ⸻ 4. What makes a great FDE? Not just technical skill. You need: - Product sense: What’s worth automating? - Systems thinking: How does this process actually work? - Communication: Can you align engineers, users, and execs? - Grit: Can you navigate internal politics and outdated systems? ⸻ 5. Why this isn’t just “a dev with a customer-facing hat” This is a distinct mindset. It’s not about building the perfect system. It’s about building something that works here and now, under constraints, in production, at scale. FDEs don’t just deliver software. They deliver change. Learn more about this role in this recent blog from a16z: https://lnkd.in/gvRsiVim

What happens when technology evolves faster than your sales process can adapt? The last fifteen years, startups focused on building software around very well understood processes. We had built an assembly line for software sales, SDR to AE to customer success manager. We calculated ratios between these three total cost of sales and drove the factory to ever improved yields. AI is upending all of that. The underlying workflows are changing so quickly, software buyers no longer know what the ideal processes are, much less which is the best software to buy. Model capabilities have evolved at 10x improvements every two years. Users are grappling to understand how to take advantage of these advances while boards are pressing teams to adopt AI. A combination of all these factors has led to a reinvention of customer success : the forward deployed engineer. Forward deployed engineers (FDEs) are the new customer success managers, the new solutions architects. They spend their time working with customers, understanding business challenge, and using technology to solve them - selling usage & outcomes. In a software sales environment where buyers seek education, the underlying technology is advancing very quickly and there’s no stability. There’s no surprise that this role has become critical. OpenAI has offered consulting services as well as Anthropic for custom enterprise deployments. Anthropic builds specialized enterprise implementation teams. Sierra employs agent engineers. Palantir created this model. Their core insight, success comes from delivering outcomes on some software platform is now the standard for mid-market and enterprise software. The costs simply don’t justify themselves below price points of $100,000 or less per contract. Staffing a FDE costing $200k for a $10k contract - the math doesn’t work. These forward-deployed engineers take the core platforms of AI and then mold them and tune them to work, defining new ways of building sales and marketing. Marketing and engineering teams - for example, agent managers. The ability for customer success managers of the future to vibe code new platforms to deliver success on a basic platform is real . And it will be a requisite for these teams in an age where customer expectations of delivering value are shorter than ever.

Struggling to showcase your Azure Data Engineering skills beyond the usual tutorials? Imagine working on interesting scenarios like a Lead Data Engineer or Architect similar to what students do at top B-Schools I am excited to share The Azure Data Engineering & Analytics Casebook, a free collection of 15 case studies designed in the style of B-School challenges. These span multiple industries and problem types, giving you portfolio-worthy scenarios to practice on. It took me four months and conversations with more than 100 data engineers across industries to design these scenarios. More will follow as the community grows. Note: This is the Challenge Edition. It contains detailed business problems, objectives, and constraints, but no datasets or ready-made solutions. The goal is for you to architect your own answers first. In the next phase, I will open-source my complete solutions and datasets on GitHub. This is just the beginning and I plan to refine and expand based on your feedback. I will keep adding new cases and refining existing ones with community feedback. One promise I can make today is this: every free resource I share will surpass most paid content, because it will evolve through real iterations and collective input. If you are interested in contributing, I would love to collaborate in building high-quality free resources for the data engineering community :)