Cloud Computing Solutions

One of the most important laws of frugal architecture is that you can’t optimize what you can’t measure. I learned this long before cloud computing. Growing up in Amsterdam during the energy crisis of the 1970s, we had things like car-free Sundays and rationed energy, but the detail that always stuck with me was closer to home. Households with their energy meter on the main floor of their homes used significantly less energy than those with it hidden in the basement. The same style of house, in the same city, yet dramatically different behaviour. About as clear of a signal as you can get that seeing data changes what you do with it. For years, in the absence of better sustainability metrics, usage (or consumption) was the best proxy we had. The meter was in the basement. With the AWS Sustainability Console, we bring the meter to your “living room”. It gives your builders direct access to Scope 1, 2, and 3 emissions data, broken down by service and Region, exportable via API, without ever touching sensitive cost and billing data. The right data, to the right people, through the right door. When carbon emission becomes just another metric in your observability stack sitting next to latency, cost, and error rates, it stops being a compliance exercise and starts becoming an architectural discipline. The world we are building in the cloud is the world we are leaving to our children. Measure it like it matters. Read more here: https://lnkd.in/efFjU7hG

The next era of datacenters is here. The demand for AI is growing rapidly, and with it comes the need to grow the cloud’s physical footprint. Historically, datacenters have been water-intensive and require using large amounts of higher carbon materials like steel. At Microsoft, we're building datacenters with sustainability in mind, and we're constantly innovating to find new ways to reduce our environmental impact. This includes: 🤝 A first-of-its-kind agreement with Stegra, backed by an investment from Microsoft’s Climate Innovation Fund (CIF) in 2024, to procure near zero-emissions steel from Stegra’s new plant in Boden, Sweden, for use in our datacenters. Powered by renewable energy and green hydrogen, Stegra's facility reduces CO2 emissions by up to 95% versus conventional steel production. By committing to purchase this green steel before it rolls off the line, Microsoft is sending a clear market signal, driving demand for cleaner materials and supporting Stegra’s growth. 💧 We also announced a major breakthrough to make our datacenters more sustainable: microfluidic in-chip cooling technology. Unlike traditional cold plates that sit atop chips, microfluidics brings cooling right inside the silicon itself. Engineers carve microscopic channels directly into the chip, letting liquid coolant flow through and absorb heat exactly where it’s generated. This approach is up to three times more effective than current methods. More efficient cooling allows datacenters to support powerful next-gen AI chips without ramping up energy use or investing in costly new gear. 💵 Through our CIF investments, we’ve catalyzed billions in follow-on capital for breakthrough solutions in low-carbon materials, sustainable fuels, carbon removal, and more. We just released a new whitepaper – Building Markets for Sustainable Growth – that distills five key lessons on how catalytic investment and partnership can move markets and accelerate a global transition in energy, waste, water, and ecosystems. Our journey toward sustainable datacenters is only beginning, and we recognize true progress requires collective action and investment. Read more from Building Markets for Sustainable Growth: https://msft.it/6041sq9xD

System design interviews can be a daunting part of the hiring process, but being prepared with the right knowledge makes all the difference. This System Design Cheat Sheet covers essential concepts that every engineer should know when tackling these types of questions. Key Areas to Focus On: 1. Data Management:    - Cache: Boost read operation speeds with caching mechanisms like Redis or Memcached.    - Blob/Object Storage: Efficiently handle large, unstructured data using systems like S3.    - Data Replication: Ensure data reliability and fault tolerance through replication.    - Checksums: Safeguard data integrity during transmission by detecting errors. 2. Database Selection:    - RDBMS/SQL: Best for structured data with strong consistency (ACID properties).    - NoSQL: Ideal for large volumes of unstructured or semi-structured data (MongoDB, Cassandra).    - Graph DB: For interconnected data like social networks and recommendation engines (Neo4j). 3. Scalability Techniques:    - Database Sharding: Partition large datasets across multiple databases for scalability.    - Horizontal Scaling: Scale out by adding more servers to distribute the load.    - Consistent Hashing: A technique for efficient distribution of data across nodes, essential for load balancing.    - Batch Processing: Use when handling large amounts of data that can be processed in chunks. 4. Networking:    - CDN: Distribute content globally for faster access and lower latency (e.g., Cloudflare, Akamai).    - Load Balancer: Spread traffic across multiple servers to ensure high availability.    - Rate Limiter: Prevent overloading by controlling the rate of incoming requests.    - Redundancy: Design systems to avoid single points of failure by duplicating components. 5. Protocols & Queues:    - Message Queues: Asynchronous communication between microservices, ideal for decoupling services (RabbitMQ, Kafka).    - API Gateway: Control API traffic, manage rate limiting, and provide a single point of entry for your services.    - Gossip Protocol: Efficient communication in distributed systems by periodically exchanging state information.    - Heartbeat Mechanism: Monitor the health of nodes in distributed systems. 6. Modern Architecture:    - Containerization (Docker): Package applications and dependencies into containers for consistency across environments.    - Serverless Architecture: Run functions in the cloud without managing servers, focusing entirely on the code (e.g., AWS Lambda).    - Microservices: Break down monolithic applications into smaller, independently scalable services.    - REST APIs: Build lightweight, maintainable services that interact through stateless API calls. 7. Communication:    - WebSockets: Real-time, bi-directional communication between client and server, commonly used in chat applications, live updates, and collaborative tools. Save this post and use it as a quick reference for your next system design challenge!

📌Turning Waste into Warmth: A Smarter Way Forward 🔁🔥 Finland is transforming how cities use energy by integrating sustainability directly into digital infrastructure. New underground data centers in Helsinki are designed not only to host servers but also to recycle the immense heat they generate. Instead of venting this waste energy, it’s captured and redirected into district heating systems that warm nearby homes and buildings. This closed-loop approach allows the same energy that powers cloud computing to heat thousands of apartments, reducing reliance on fossil fuels and cutting urban carbon emissions dramatically. Data centers, once known for their high energy consumption, are becoming key players in renewable urban ecosystems. This is the kind of circular solution modern facilities must aspire to. By integrating technology, engineering, and smart planning, even high-energy systems like data centres can become contributors to a greener city. For facilities and estates professionals, the message is clear: Sustainability isn’t always about new resources — it’s about using what we already have, better. The project underscores Finland’s leadership in green innovation — turning what was once environmental waste into community benefit. As cities worldwide search for climate solutions, this model shows how technology and sustainability can work hand in hand to reshape the future of energy. A powerful reminder of what’s possible when we rethink infrastructure with efficiency and environmental responsibility at the core. Sources: ✍️TechTimes #GreenEnergy #FinlandInnovation #SustainableCities #DataCenters #CleanTechnology #Infrastructure #Environmental #Technology

I reduced our Annual AWS bill from ₹15 Lakhs to ₹4 Lakhs — in just 6 months. Back in October 2024, I joined the company with zero prior industry experience in DevOps or Cloud. The previous engineer had 7+ years under their belt. Just two weeks in, I became solely responsible for our entire AWS infrastructure. Fast forward to May 2025, and here’s what changed: ✅ ECS costs down from $617 to $217/month — 🔻64.8% ✅ RDS costs down from $240 to $43/month — 🔻82.1% ✅ EC2 costs down from $182 to $78/month — 🔻57.1% ✅ VPC costs down from $121 to $24/month — 🔻80.2% 💰 Total annual savings: ₹10+ Lakhs If you’re working in a startup (or honestly, any company) that’s using AWS without tight cost controls, there’s a high chance you’re leaving thousands of dollars on the table. I broke everything down in this article — how I ran load tests, migrated databases, re-architected the VPC, cleaned up zombie infrastructure, and built a culture of cost-awareness. 🔗 Read the full article here: https://lnkd.in/g99gnPG6 Feel free to reach out if you want to chat about AWS, DevOps, or cost optimization strategies! #AWS #DevOps #CloudComputing #CostOptimization #Startups

A few months ago, I was juggling Terraform deployments on AWS and Azure across dev, test, and prod environments. As the project grew, managing separate states, avoiding drift, and keeping the code clean became a real challenge. This happens when you handle multi-cloud and multi-environment code without a proper configuration structure. Messy state files, deployment errors, and overwritten environments can follow. In my latest blog, I share tips to manage multi-cloud (AWS + Azure) and multi-environments (dev, test, prod): • Project structure • Variables & modules • State file • Best practices for running Terraform • Common pitfalls in multi-cloud Terraform 🔗 Find the blog link in the comments. 💬 I would love to know how you are managing your Terraform projects?

I entered the sustainability field to build a resilient future for people and the planet - not to wrestle with manual spreadsheets. But as many of us in this space have discovered, the time-consuming logistics of reporting are often a barrier to real progress. At Google, we’ve spent the last two years using our own environmental report as a testing ground for a better way. By leveraging Google Cloud tools to automate data ingestion and claim validation, we’ve shifted from weeks of manual data cleaning to on-demand strategic insights. These technologies don’t replace our experts. Instead, they free our team to focus on strategy and execution rather than repetitive, time-consuming data collection and validation. We’re already seeing how other companies can use these tools to make similar shifts. For example, Equinix moved from manual tracking to a system that collects data from 240+ global sites automatically. Learn more about how Google Cloud is helping sustainability teams spend more time on strategy, not spreadsheets. ⤵️ https://goo.gle/4scTUfR

Yesterday, Reuters reported that OpenAI finalized a cloud deal with Google in May. This might look like routine tech news. It is not. This is a strategic inflection point in the AI infrastructure wars. OpenAI, whose ChatGPT threatens the core of Google Search, is now paying Google billions of dollars to power its growth. This was not a partnership of choice. It was a partnership of necessity. Since ChatGPT launched in late 2022, OpenAI has struggled to meet soaring demand for computing power. Training and inference workloads have outpaced what Microsoft’s Azure alone can support. OpenAI had to expand. Google Cloud was the solution. For OpenAI, the deal reduces its dependency on Microsoft. For Google, it is a calculated win. Google Cloud generated $43 billion in revenue last year, about 12 percent of Alphabet’s total. By serving a direct competitor, Google is positioning its cloud business as a neutral, high-performance platform for AI at scale. The market responded. Alphabet shares rose 2.1 percent on the news. Microsoft fell 0.6 percent. There are only a handful of true hyperscalers in the U.S. AWS, Azure, and GCP dominate, with Oracle and IBM trailing behind. The appetite for compute is growing faster than any one company can satisfy. In this new phase of the AI era, exclusivity is a luxury no one can afford. Collaboration across competitive lines is inevitable. -s

“𝗦𝟯, 𝗔𝗗𝗟𝗦, 𝗚𝗖𝗦? 𝗝𝘂𝘀𝘁 𝘀𝘁𝗼𝗿𝗮𝗴𝗲, 𝗿𝗶𝗴𝗵𝘁?” Not quite. Here’s a better way to think about it 👇 𝗖𝗹𝗼𝘂𝗱 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 — 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗝𝘂𝘀𝘁 𝗮 𝗙𝗶𝗹𝗲 𝗗𝘂𝗺𝗽 Cloud storage is like a hotel for your data. It checks in from various sources — APIs, apps, pipelines. Some stay temporarily (like staging or temp files) Others are long-term guests (like audit logs or historical records) You control who can access it (IAM), what they can do (read/write), and how long it stays (retention policies) There’s even housekeeping involved — with lifecycle rules, versioning, deduplication, and cost optimization. ⚠️ 𝗪𝗵𝗮𝘁 𝗣𝗲𝗼𝗽𝗹𝗲 𝗧𝗵𝗶𝗻𝗸 𝗗𝗘𝘀 𝗗𝗼: "Just dump the data to S3 and move on." ✅ 𝗪𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗛𝗮𝗽𝗽𝗲𝗻𝘀:   • Design folder structures for efficient querying and partitioning   • Choose the right storage class (Standard, Infrequent Access, Glacier)   • Use optimal file formats (Parquet, ORC) and compression (Snappy, Zstandard)   • Set access controls, encryption, and auditing (IAM roles, KMS, logging)   • Enable direct querying (Athena, Synapse, BigQuery on GCS)   • Integrate storage across cloud platforms (multi-cloud architectures)   • Automate lifecycle management to control cost and reduce clutter   • Leverage features like S3 Select, signed URLs, and Delta format for smart access 📌 Takeaway: Cloud storage isn’t where data ends up — it’s where the journey begins. How you design and manage it defines the performance, scalability, and reliability of everything downstream. #data #engineering #reeltorealdata #python #sql #cloud

In true Silicon Valley fashion, the AI arms race is getting down to the silicon itself.🤺 The Big 3 hyperscalers, Amazon, Microsoft, and Google—traditionally NVIDIA’s biggest customers—are encroaching on its core turf by developing their own AI chips. Meanwhile, NVIDIA, the juggernaut of GPUs, is pushing into hyperscaler territory with DGX Cloud, offering AI infrastructure that could, in theory, make it less reliant on Big Tech clouds. Why does this matter? Because the silicon layer is a battleground for billions. 💸 Hyperscalers are tired of footing NVIDIA's massive GPU bill, so they’re investing big in in-house silicon to cut costs and assert control. Amazon’s Inferentia and Trainium chips, Google’s TPUs, and Microsoft’s Maia project are all about building tech stacks with minimal dependency on outside hardware. The goal? Price control and performance tailored to hyperscaler needs. For NVIDIA, this is about strategic survival. Its business model relies on selling chips that empower the same hyperscalers who are now racing to break free. DGX Cloud and partnerships with Oracle, Google, and Microsoft (ironically) are NVIDIA’s way of expanding beyond hardware sales into high-margin, AI-driven cloud services. NVIDIA is doubling down on services, building out a powerful software ecosystem, and offering a soup-to-nuts solution for enterprises wanting AI access without the infrastructure burden. If hyperscalers get their chips right, NVIDIA's dominance could be challenged. But if NVIDIA’s DGX Cloud gains traction, it’s a warning shot that it can play in hyperscaler territory too—and may lure AI workloads directly onto its infrastructure. The stakes have never been higher, so let the chips fall where they may.🌐