Shashank Garg

Join Balkrishna Basisthnarayan, Senior Solutions Engineer at Percona, for an interactive webinar on the key differences between MySQL and PostgreSQL. Discover how subtle and non-subtle differences in features like JSON_TABLE() and MERGER() can impact your project. Learn about performance, scalability, and the challenges involved in database administration. Get insights on how to choose the right database and avoid common pain points: https://bit.ly/4c9ClXI

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15 heuristics that will help you pass system design interviews: 1. Latency + Global → CDN ↳ Deliver data from edge servers to reduce latency 2. Read + Bottleneck → Cache ↳ Store frequent reads in cache to reduce database load 3. Write + Spike → Queue ↳ Buffer writes for asynchronous processing during traffic spikes 4. Distributed + Transaction → Saga ↳ Handle multi-service transactions with compensating steps 5. ACID + Relational → SQL ↳ Use a SQL database for strong consistency and transactional integrity 6. Flexible + Scale → NoSQL ↳ Use NoSQL for schema flexibility and horizontal scalability 7. SQL + Scale → Shard DB ↳ Federate and partition data across shards to scale effectively 8. Load + Growth → Scale Out ↳ Add servers to handle extra traffic 9. Traffic + Reliability → Load Balance ↳ Distribute requests evenly for performance 10. Core + Failure → Redundancy ↳ Replicate core services to avoid single points of failure 11. Durability + Faults → Replication ↳ Replicate data for availability and recovery 12. Requests + Spike → Throttle ↳ Limit excessive requests to prevent server overload 13. Load + Spike → Autoscale ↳ Add or remove server capacity automatically to handle changing load 14. Realtime + Updates → WebSockets ↳ Use WebSockets for live, bidirectional communication 15. Retry + Safety → Idempotent ↳ Make operations safe to retry without side effects using idempotency Credit:Neo Kim

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Modernizing Legacy PowerBuilder Applications: Why Documentation Matters More Than Ever As organizations across Canada—federal departments, provincial ministries, and public agencies—move forward with application modernization, one challenge stands out: PowerBuilder systems with little to no documentation. Many experienced PowerBuilder developers have retired, and more will retire soon. Without proper documentation, maintaining, supporting, and modernizing these critical systems becomes risky and costly. ✅ My Experience: Over decades in IT consulting, I’ve worked extensively with PowerBuilder environments and understand the complexity of these legacy systems. ✅ My Solution: Leveraging Microsoft Copilot, I’ve developed a streamlined process to generate comprehensive, accurate documentation for PowerBuilder applications. This includes: Architecture diagrams Data flow and business logic mapping Component-level descriptions Modernization readiness reports By combining human expertise with AI-powered tools, we can accelerate documentation efforts, reduce costs, and ensure your systems are ready for future modernization projects. If your organization is planning to modernize PowerBuilder applications—or simply needs better documentation for ongoing maintenance—let’s connect. I’d be happy to share insights on how this approach can help you de-risk your modernization journey. #ApplicationModernization #PowerBuilder #AIinGov #DigitalTransformation #LegacySystems #PublicSectorInnovation #MicrosoftCopilot #GovTech

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🚨 FREE DevOps Interview Bible — 408 Questions Across All Core Domains! After tons of requests and feedback, I’m excited to drop this ultimate DevOps Interview Guide — packed with 408 real-world, scenario-based, and role-specific questions. 💡 Topics Covered: ✅ AWS DevOps (Basic to Expert Scenarios) ✅ Site Reliability Engineering (SRE) ✅ Kubernetes (Beginner to 7+ Years Level) ✅ Jenkins (CI/CD, Pipelines, Plugins, HA, Blue/Green, Jenkins X) ✅ Terraform (from IaC fundamentals to Sentinel, CDK & cost optimization) ✅ Ansible (Playbooks, Roles, Inventories, Advanced Patterns) ✅ Shell & Python (including production troubleshooting scripts & DevOps automation) Whether you’re: 🔍 Preparing for your first DevOps role 🚀 Upskilling for a promotion 💼 Or mentoring junior engineers… This document will sharpen your edge. 📌 Download it. Study it. Share it. And follow me for more free DevOps learning drops, cheat sheets, and project templates. 👉 Want breakdowns of Terraform, Jenkins, or Kubernetes sections in upcoming posts? Comment your topic below! #DevOps #AWS #Terraform #Ansible #Jenkins #SRE #Kubernetes #ShellScripting #PythonForDevOps #IaC #CI_CD #CloudComputing #TechCareers #DevOpsInterview #CloudEngineer #LinuxAdmin #DevOpsTools #KubernetesInterview #FollowMeForMore #TechResources #TechCommunity #JobSearch #FreeResources #InterviewReady #JenkinsPipeline #TerraformModules #LearnDevOps

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🔑 Core Modern System Design Principles 1. Scalability Horizontal scaling (add more machines) preferred over vertical (bigger machine). Use load balancing to distribute traffic. Employ caching (CDN, Redis, Memcached) to reduce repeated computations. Database scaling: sharding and replication. 2. Reliability & Fault Tolerance Assume failures will happen (networks drop, servers crash). Use replication across availability zones/regions. Failover mechanisms to reroute traffic when components fail. Apply idempotency in APIs so retries don’t break data. 3. Performance & Efficiency Optimize for latency (fast response time) and throughput (high volume). Use asynchronous processing (queues like Kafka, RabbitMQ, SQS). Profile bottlenecks before scaling blindly. Employ content delivery networks (CDNs) for static assets. 4. Security by Design Zero-trust principles: least privilege access for users & services. Encrypt data in transit (TLS) and at rest (AES, KMS). Apply rate limiting, authentication, and authorization. Regular audit logging and monitoring. 5. Modularity & Loose Coupling Break systems into microservices or modular components. Use well-defined APIs for communication (REST, gRPC, GraphQL). Design for independent deployability. 6. Observability Log structured events for debugging. Collect metrics (Prometheus, CloudWatch). Implement distributed tracing (Jaeger, OpenTelemetry). Define SLAs, SLOs, SLIs for reliability goals. 7. Resilience & Recovery Apply circuit breakers (stop cascading failures). Use bulkheads (isolate failures to one component). Automate disaster recovery with backups and multi-region replication. Chaos engineering to test failure preparedness. 8. Cost Awareness Architect for cloud efficiency (pay-per-use services). Use auto-scaling to avoid over-provisioning. Apply data lifecycle management (tiered storage). 9. Data Management Choose storage wisely: SQL for structured, relational data. NoSQL for flexible, scalable, unstructured data. Event sourcing for auditability and history. Implement consistency models (strong, eventual, causal) based on needs. Apply caching layers to offload databases. 10. Evolvability Use CI/CD pipelines for fast iterations. Write automated tests (unit, integration, end-to-end). Support feature flags and A/B testing. Backward compatibility in APIs. ✅ In practice, modern system design = Cloud-native + Microservices + Automation + Observability + Security-first mindset.

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𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 1.34: 𝗣𝗲𝗿 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗿𝗲𝘀𝘁𝗮𝗿𝘁 𝗽𝗼𝗹𝗶𝗰𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗲𝗿𝗲 A long-standing limitation in Kubernetes has been removed. Restart behavior is no longer tied to the Pod level. With Kubernetes 1.34, each container can now have its own restart policy, and rules can be applied to specific exit codes. The new model is far more flexible: • 𝗚𝗿𝗮𝗻𝘂𝗹𝗮𝗿𝗶𝘁𝘆: Policies like Always, Never, or OnFailure can now be set per container. • 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: Exit-code rules allow containers to retry recoverable failures while failing fast on critical errors. • 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Restarts happen in place, cutting recovery from up to a minute down to seconds, while preserving volumes and networking. This matters in practice. A migration container can now run once and never restart. A machine learning job can retry GPU memory errors without being rescheduled. A microservice with logging and monitoring sidecars can give each one restart logic suited to its role. The feature is still in alpha and gated, but it is worth testing. It reduces complexity, improves reliability, and reflects the way modern workloads actually fail. I break down examples, exit-code design patterns, and migration guidance in the full write-up: https://lnkd.in/enXiUvjU #Kubernetes #CloudNative #DevOps #MLOps #PlatformEngineering

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🔎 What is an API Gateway? An API Gateway acts as a single entry point for all client requests in a Microservices architecture. Instead of: Client → Product Service Client → Order Service Client → Inventory Service We have: Client → API Gateway → Microservices ⚙ In Spring Boot We Use: 👉 Spring Cloud Gateway 👉 With Service Discovery via Netflix Eureka Example configuration: spring.cloud.gateway.routes[0].id=product-service spring.cloud.gateway.routes[0].uri=lb://product-service spring.cloud.gateway.routes[0].predicates[0]=Path=/api/product spring.cloud.gateway.routes[1].id=order-service spring.cloud.gateway.routes[1].uri=lb://order-service spring.cloud.gateway.routes[1].predicates[0]=Path=/api/order 🔹 lb:// means → use Load Balancer 🔹 Gateway fetches instances from Eureka 🔹 Routes request dynamically 🧠 Why API Gateway is Important? Without API Gateway: ❌ Client must know all service URLs ❌ Security implemented in every service ❌ Hard to manage logging, monitoring With API Gateway: ✅ Centralized authentication ✅ Load balancing ✅ Rate limiting ✅ Logging & monitoring ✅ Circuit breaker support ✅ Cleaner architecture 🎯 Definition API Gateway is a centralized entry point in a microservices architecture that routes client requests to appropriate services and handles cross-cutting concerns like security, logging, load balancing, and rate limiting. In real-world systems, API Gateway is not optional — it’s essential for scalability and maintainability 🔥 #Microservices #SpringBoot #Java #APIGateway #SystemDesign #CloudArchitecture #BackendDevelopment

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Want to build scalable, real-time systems like WhatsApp or Uber? Start by learning these top 8 API styles. APIs power the backbone of modern applications - from microservices to real-time chat to IoT. Each architecture style brings its own strengths, and choosing the right one depends on the use case. 1. SOAP – XML-based with strict standards; used in enterprise-grade systems requiring high security. 2. REST – Most common style, uses HTTP methods (GET, POST, etc.) for scalable, resource-based interactions. 3. GraphQL – Query only the data you need. Ideal for client-optimized requests. 4. gRPC – High-performance framework over HTTP/2. Great for microservices and inter-service calls. 5. WebSocket – Real-time, bi-directional communication on a single connection. Used in chat apps, gaming, etc. 6. Webhook – Event-driven callbacks from server to client; used in automation and integrations. 7. MQTT – Lightweight protocol for IoT and low-power devices. Publish-subscribe model. 8. AMQP – Reliable, asynchronous messaging using queues. Enterprise-grade communication layer. Use this guide to choose the right protocol based on latency, scalability, and message flow needs. 🔁 Save this post & tag a tech buddy. Follow Lakshmi Marikumar & Everyone Who Codes (EWC) for tech career & job search insights. We have a simple mission: helping engineers get hired! #systemdesign #API #softwareengineering #techjobs #interviews #backend #softwareengineer #softwaredeveloper #hiring #jobsearch

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System Design #Day5 CAP Theorem says a distributed system can ensure only two out of three: - Consistency – All nodes have the same data - Availability – Every request gets a response - Partition Tolerance – System works even if some nodes can’t communicate

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How to Search for Azure Jobs in Canada – A Simple Guide Are you looking to start or advance your career in Azure cloud technologies in Canada? Here’s a simple guide to help you get started with your job search! 1. Explore Job Search Websites The first step is to visit popular job platforms where Azure roles are frequently posted: LinkedIn: Create a strong profile highlighting your Azure skills (Azure Fundamentals, Azure Admin, Azure DevOps, etc.). Indeed: Use keywords like “Azure”, “Cloud Engineer”, “Azure Administrator” and set your location to Canadian cities like Toronto, Vancouver, or Calgary. Glassdoor: You can not only find job listings but also get insights into company reviews and salaries. Workopolis: Another job search platform catering to the Canadian market. 2. Use Keywords for Azure-Specific Roles When searching for roles, use specific Azure-related job titles like: Azure Cloud Engineer Azure Solutions Architect Azure DevOps Engineer Azure Administrator Cloud Security Engineer (with Azure focus) This will help you narrow down the search to only relevant positions. 3. Leverage Microsoft Careers Microsoft regularly hires for Azure roles and posts job openings on their Microsoft Careers portal. If you're passionate about working directly with Azure products and services, this is the perfect place to check. 4. Network with Azure Professionals Networking is key. Connect with professionals in the Azure ecosystem on LinkedIn or join local cloud meetups in Canada. The more people you know in the industry, the more likely you are to hear about job opportunities first. 5. Use Azure Certifications to Stand Out Azure certifications like AZ-900, AZ-104, or AZ-303 will make your resume stand out. Employers in Canada value certified professionals, so be sure to mention your certifications when applying. 6. Check Local Job Boards & Company Websites Many Canadian companies post Azure job opportunities directly on their websites. Also, explore local job boards for specific provinces or cities like: Alberta Job Search (for jobs in Alberta) Toronto Jobs (for opportunities in the GTA) 7. Apply with Confidence Always tailor your resume and cover letter to each job. Highlight your Azure skills, cloud experience, and any relevant certifications. Don’t forget to showcase your hands-on projects, whether through personal work, certifications, or practical labs. Azure is one of the most in-demand skills in the tech world today, and Canada is no exception! So start applying today, and take the next step toward a successful career in cloud computing. Need Help Gaining Real-Time Expertise? If you need any guidance or hands-on experience, feel free to reach out to us! We are experienced professionals in the Azure cloud platform and will help you build end-to-end expertise. Whether it's for job preparation or technical skills development, we've got you covered every step of the way. Let’s make your Azure career journey a success! #canada #interview #Azure

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🚀 Continuing my DevOps Interview Q&A Series 🚀 Today’s focus is on SRE (Site Reliability Engineering), one of the most critical roles bridging DevOps and reliability at scale. Here are some frequently asked SRE Interview Questions from basic to advanced level 👇 🔹 What is Site Reliability Engineering (SRE), and how does it differ from traditional DevOps? 🔹 Explain the concept of SLIs, SLOs, and SLAs with real-world examples. 🔹 What is an error budget, and how do SRE teams use it to balance reliability and innovation? 🔹 What are the “Golden Signals” of monitoring in SRE? 🔹 How do you implement effective monitoring and alerting strategies? 🔹 Explain the difference between proactive monitoring and reactive incident response. 🔹 What are common sources of toil in SRE, and how do you eliminate them? 🔹 How do you perform capacity planning and scaling in large distributed systems? 🔹 How does chaos engineering help in improving system reliability? 🔹 What’s the role of blameless postmortems, and how do they improve learning culture? 🔹 How do you define and measure availability in a distributed application? 🔹 What is mean time to detect (MTTD) and mean time to recover (MTTR), and how do SREs improve them? 🔹 How can you integrate SRE principles into CI/CD pipelines? 🔹 What tools and practices do SREs commonly use for incident management and observability? 🔹 How do SREs collaborate with development teams to prioritize reliability vs. feature delivery? 🔹 Explain the concept of error budget policies with an example. 🔹 How would you design a high-availability architecture for a mission- critical application? 🔹 What’s the difference between incident response, disaster recovery, and business continuity in SRE? 🔹 How do you handle on-call fatigue and ensure healthy rotations for SRE teams? 🔹 Share a real-world example where automation reduced toil and improved reliability. 🔹 Your application latency suddenly spikes, and customers are reporting slowness. How would you investigate and resolve this issue 🔹 You’re on-call, and you receive multiple alerts at night. How do you prioritize and handle them? 🔹 The service is breaching its error budget due to frequent outages. How would you decide whether to pause feature releases or continue deployments? 🔹 How would you negotiate with product managers when reliability targets (SLOs) conflict with aggressive feature delivery goals? 🔹 You notice that SREs spend a lot of time manually restarting failed pods in Kubernetes. How would you automate this? 🔹 Backups are running daily, but verification is manual and error-prone. How would you improve reliability here? #DevOps #SRE #SiteReliabilityEngineering #Reliability #Observability #InterviewPreparation

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I have a goal to run all full-blown Treatwise 5 microservices written in Go on AWS under $100/month. While it's very possible, I have done some insane infrastructure optimizations: ✅ VPC Optimization — Cut down VPC to provision only in 2 AZs ✅ Network Cost Reduction — Leverage VPC endpoints for most intra-VPC network connectivity without traversing the public internet to save on egress and NAT costs ✅ Database Strategy — Using RDS Serverless V2. RDS Proxy added more cost and maintained a persistent connection pool which prevents true scale to zero. I removed it and managed database pooling at the application layer. ✅ Compute Optimization — Scheduled all services on ECS Fargate with minimum resources and scale to zero for non-critical workloads. Only Identity service can't tolerate cold-start, scheduled as always-on. ❌ The Culprit? I thought serverless was going to help me with ElastiCache for Redis too until I realized the base minimum cost for 1000 EPU is ~$90/month, while my previous cluster-mode with one node was at $40/month. 💡 Lesson: Always review costs and thinking serverless is going to save you all the time. All the cost optimizations I implemented were with the intention of not affecting reliability, performance and high availability. ⚠️ NOTE: For any serverless offering, if it doesn't offer scale-to-zero with minimal cost on startup, probably not worth it. #cloudengineering #cloudcomputing #softwarengineering #awscloud #serverless

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Understanding Docker Containers: A Technical Deep Dive Docker containers are pivotal for modern development and DevOps. Here's a concise look at their technical underpinnings: Linux Kernel Features: Containers rely on namespaces (PID, Network, Mount, UTS, IPC, User) for process isolation, cgroups for resource management, and capabilities for fine-grained privilege control. Union Filesystems: OverlayFS or AUFS enables layered images with a Copy-on-Write mechanism, optimizing storage and build efficiency. Use docker history <image> to analyze layers. Container Runtime: The Docker Daemon, containerd, and runc manage the container lifecycle. Alternatives like Kata Containers or gVisor offer enhanced isolation. Networking: Bridge (default), Host, and Overlay networks support flexible connectivity. Inspect with docker network inspect. Security: Run containers as non-root, scan images with tools like Trivy, and leverage Seccomp/AppArmor/SELinux for syscall and access restrictions. For production, consider Kubernetes for orchestration. Dive deeper to master containerized workflows! #Docker #DevOps #Containerization

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