Scaling on AWS: Avoiding Database and Storage Bottlenecks

Lately while researching how to achieve a true "Zero-downtime migration" of a petabyte-scale storage system from on-premise to AWS, I found out that there are so many out-of-the-box solutions AWS has on offer - most notably, AWS DataSync. (It is built for file system, objects not Databases btw) Where a "big bang" copy-and-switch simply isn't possible, Data sync helps by incremental, scheduled syncing of files and objects from on-prem storage directly into Amazon S3. In this video I have detailed out my research how using canary we can achieve zero downtime migration. https://lnkd.in/gzCNscT6 #AWS #CloudMigration #CloudArchitecture #TechLeadership #DevOps

𝗦𝗻𝗼𝘄𝗳𝗹𝗮𝗸𝗲 𝘁𝘂𝗿𝗻𝗲𝗱 𝗱𝗮𝘁𝗮 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗶𝗻𝘁𝗼 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗽𝗼𝘄𝗲𝗿 At Snowflake, compute and storage don’t compete. They scale independently. That changes how modern data systems are designed. Without separation: • queries slow down under load • workloads interfere with each other • scaling becomes expensive and inefficient With Snowflake, teams run 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗼𝗻 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗱𝗮𝘁𝗮 — 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝗺𝗽𝗮𝗰𝘁. The DevOps lesson: 𝗦𝗰𝗮𝗹𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀, 𝗻𝗼𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀. When systems are decoupled, performance becomes predictable. At ServerScribe, we help teams design architectures that scale without bottlenecks. Is your system tightly coupled — or built to scale independently? 👇 #DevOps #ServerScribe #Snowflake #DataEngineering #Scalability #Cloud #SRE

🚀 Interesting AWS Fact You Probably Didn’t Know! Did you know… A large part of the internet actually runs on AWS? 😲 From startups to global giants, companies rely on AWS for: • Scalability during traffic spikes • High availability (almost zero downtime) • Pay-as-you-go cost model Even more interesting 👇 If a major AWS region faces issues, it can impact multiple apps you use daily — all at once. That’s why AWS focuses heavily on: ✔ Multi-region architecture ✔ Fault tolerance ✔ Disaster recovery 👉 Lesson: Cloud is not just about hosting… it’s about designing for failure. How do you ensure high availability in your architecture? #AWS #CloudComputing #DevOps #CloudEngineer #TechFacts

Amazon S3 Files went GA on April 7 — and it fundamentally changes how we approach cloud storage architecture. I just published a deep dive on the AWS Builder Center. If you've ever copied data from S3 → EFS → processed → copied it back… you already know the pain this solves. Now: • Your data stays in S3 • You mount it via NFS • Your tools (and agents) can operate directly on the data — no movement required The sync model is designed for real workloads: → Metadata is available immediately → Small files are pulled proactively → Large files are served directly from S3 In the article, I break down: → When to use S3 Files vs. EFS vs. FSx for Lustre → Why read-bypass impacts your cloud cost significantly → How agentic AI workloads are emerging around this pattern This doesn't seems just a new feature — it can remove an entire class of architectural workarounds we’ve accepted for years. Link in comments 👇 #AWS #AmazonS3 #CloudOps #CloudArchitecture #DevOps #AWSCommunityBuilders

Navigating the AWS ecosystem can be challenging, especially when deciding which compute service best fits your architecture. As an IT Engineer, I often get asked about the differences between these core services. Here is a high-level breakdown of the AWS Compute family to help you make an informed decision: 🔹 EC2 (Elastic Compute Cloud): The foundation. It provides virtual servers (instances) where you have full control over the OS and stack. Ideal for applications requiring custom configurations. 🔹 Lambda: The king of Serverless. Run code without provisioning or managing servers. You only pay for the compute time you consume. Perfect for event-driven tasks. 🔹 ECS (Elastic Container Service) & EKS (Elastic Kubernetes Service): Your go-to for containerization. ECS is AWS’s native container orchestrator (highly integrated), while EKS is the managed Kubernetes service for those who need industry-standard orchestration. 🔹 Fargate: Serverless compute for containers. It works with both ECS and EKS, removing the need to manage the underlying EC2 instances. You focus on the containers; AWS handles the rest. 🔹 AWS Batch: Designed for batch computing. It efficiently plans, schedules, and executes your batch computing workloads across the full range of AWS compute services. Key Takeaway: There is no "one size fits all." The choice depends on your need for control versus your desire for operational simplicity. What is your "go-to" compute service for new projects? #AWS #CloudComputing #ITEngineering #DevOps #Serverless #TechCommunity #CloudArchitecture

🚀 Exploring the Power of AWS Services ☁️ Diving deeper into the AWS ecosystem and understanding how its core services work together to build scalable, secure, and high-performance applications. 🔹 Compute: EC2, Lambda, ECS, EKS, Auto Scaling 🔹 Storage: S3, EBS, EFS, FSx, Snowball 🔹 Networking: VPC, Route 53, ELB, CloudFront, Direct Connect 🔹 Databases: RDS, DynamoDB, Aurora, Redshift, ElastiCache 🔹 Security: IAM, KMS, Cognito, GuardDuty, WAF Each service plays a critical role in designing modern cloud architectures — from serverless applications to containerized microservices and big data solutions. 💡 Key takeaway: AWS is not just about individual services, but how you combine them to build efficient, reliable systems. #AWS #CloudComputing #DevOps #Serverless #CloudArchitecture #Learning #Technology

🚀 Big News in the Cloud Space! As of April 2026, AWS has introduced a game-changing feature — *Amazon S3 Files*. For the first time, S3 buckets can now be mounted directly as a shared file system, bringing a whole new level of flexibility for developers and data engineers. 🔹 Key Highlights: • Native file system access to S3 • Supports NFS mounts • Works seamlessly with EC2, Lambda, EKS, and ECS • Enables high-performance, low-latency data access This innovation bridges the gap between object storage and traditional file systems, making it easier to build scalable, data-intensive applications without changing existing workflows. 💡 This is a big step toward simplifying cloud architectures and improving developer productivity. Excited to see how this evolves and how teams start leveraging it in real-world use cases! #AWS #AmazonS3 #AWSWorld #DataEngineering #Devops #CloudInnovation #CloudComputing

The "Self-Healing" Fleet: AWS Auto Scaling | Day 11/100 YouTube: https://lnkd.in/dQWmums8 Documentation: https://lnkd.in/dws-GVRd In this "Cloud Story," we move beyond single servers to build a self-healing, high-availability architecture. I used Launch Templates as the DNA for our servers and Auto Scaling Groups (ASG) as the manager. The highlight? A "Chaos Test" where I manually terminated a running server, only to watch AWS automatically detect the failure and launch a perfectly configured replacement in seconds. No manual intervention, no downtime—just pure cloud automation. Watch Day 10: https://lnkd.in/d25tUNTt 🌟 ABOUT THE SERIES: I am building 100 AWS projects in 100 days to go from cloud beginner to AWS Architect. Join me on this journey as we build real-world engineering skills together! #AWS #100DaysOfAWS #AutoScaling #CloudAutomation #DevOps #EC2 #HighAvailability #SRE #CloudStories #PriyanshuMandani #CloudEngineering #AWSCloud

Exploring the power of AWS Lambda and serverless architecture. AWS Lambda enables developers to run code without provisioning or managing servers, allowing teams to focus more on building applications while AWS handles the underlying infrastructure. Key benefits of AWS Lambda: • Automatic scaling based on demand • Pay only for the compute time used • Faster deployments with reduced operational overhead • Native integration with AWS services • Ideal for event-driven architectures Common use cases include APIs, automation workflows, file processing, scheduled tasks, and real-time backend services. Serverless computing continues to play an important role in building scalable, efficient, and modern cloud solutions. #AWS #AWSLambda #Serverless #CloudComputing #DevOps #SRE #Architecture #Cloud #Technology

Top 5 AWS Cost Hacks for 2026 (That Actually Move the Needle) Cloud bills creeping up? You’re not alone. The good news: a few smart optimizations can drive 40%+ savings quickly—without major re-architecture. Here are the highest-impact AWS cost hacks I’m seeing work across environments: 🔹 1. Right-Size Everything (20–40% savings | fastest win) Most environments are over-provisioned from “peak load” assumptions that no longer apply. - Use Compute Optimizer (free) - Target ~40–60% utilization - Shift to Graviton for better price/performance - Clean up idle resources (EBS, snapshots, Elastic IPs, unused LBs) 🔹 2. Use Savings Plans (Up to 72% savings) Stop paying On-Demand for predictable workloads. - Start with Compute Savings Plans (flexible) - Use 1-year terms initially - Let recommendations guide you 👉 This is often the highest ROI move. 🔹 3. Leverage Spot Instances (Up to 90% savings) Perfect for fault-tolerant workloads: - Batch jobs, CI/CD, ML, big data - Use Spot Fleet + diversification - Handle 2-min interruptions 👉 Massive savings when applied correctly. 🔹 4. Optimize Storage Aggressively (40–80% savings) Hidden costs add up fast. - S3 Intelligent-Tiering + lifecycle policies - Delete old snapshots/logs - Move GP2 → GP3 - Reduce CloudWatch retention 🔹 5. Maximize Elasticity & Serverless Pay only for what you use. - Use Lambda, Fargate, Aurora Serverless v2 - Enable Auto Scaling - Schedule non-prod shutdowns 👉 Especially impactful for dev/test + spiky workloads. 💡 Quick Wins to Start Today - Enable Cost Explorer, Budgets, Trusted Advisor, Cost Optimization Hub - Tag everything properly - Combine: Right-size + Savings Plans + Spot #AWS #CloudOptimization #FinOps #CloudCost #Serverless #SolutionArchitect