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𝗔𝗻𝗸𝗶𝘁𝗮: You know 𝗣𝗼𝗼𝗷𝗮, last Monday our new data pipeline was live in cloud and it failed terribly. Literally had an exhaustive week fixing the critical issues. 𝗣𝗼𝗼𝗷𝗮: Ohh, so don’t you use Cloud monitoring for data pipelines? From my experience always start by tracking these four key metrics: latency, traffic, errors, and saturation. It helps you to check your pipeline health, if it's running smoothly or if there’s a bottleneck somewhere.. 𝗔𝗻𝗸𝗶𝘁𝗮: Makes sense. What tools do you use for this? 𝗣𝗼𝗼𝗷𝗮: Depends on the cloud platform. For AWS, I use CloudWatch—it lets you set up dashboards, track metrics, and create alarms for failures or slowdowns. On Google Cloud, Cloud Monitoring (formerly Stackdriver) is awesome for custom dashboards and log-based metrics. For more advanced needs, tools like Datadog and Splunk offer real-time analytics, anomaly detection, and distributed tracing across service. 𝗔𝗻𝗸𝗶𝘁𝗮: And what about data lineage tracking? How do you track when something goes wrong, it's always a nightmare trying to figure out which downstream systems are affected. 𝗣𝗼𝗼𝗷𝗮: That's where things get interesting. You could simply implement custom logging to track data lineage and create dependency maps. If the customer data pipeline fails, you’ll immediately know that the segmentation, recommendation, and reporting pipelines might be affected. 𝗔𝗻𝗸𝗶𝘁𝗮: And what about logging and troubleshooting? 𝗣𝗼𝗼𝗷𝗮: Comprehensive logging is key. I make sure every step in the pipeline logs events with timestamps and error details. Centralized logging tools like ELK stack or cloud-native solutions help with quick debugging. Plus, maintaining data lineage helps trace issues back to their source. 𝗔𝗻𝗸𝗶𝘁𝗮: Any best practices you swear by? 𝗣𝗼𝗼𝗷𝗮: Yes, here’s what’s my mantra to ensure my weekends are free from pipeline struggles - Set clear monitoring objectives—know what you want to track. Use real-time alerts for critical failures. Regularly review and update your monitoring setup as the pipeline evolves. Automate as much as possible to catch issues early. 𝗔𝗻𝗸𝗶𝘁𝗮: Thanks, 𝗣𝗼𝗼𝗷𝗮! I’ll set up dashboards and alerts right away. Finally, we'll be proactive instead of reactive when it comes to pipeline issues! 𝗣𝗼𝗼𝗷𝗮: Exactly. No more finding out about problems from angry business users. Monitoring will catch issues before they impact anyone downstream. In data engineering, a well-monitored pipeline isn’t just about catching errors—it’s about building trust in every insight you deliver. #data #engineering #reeltorealdata #cloud #bigdata

Hybrid Cloud Is Becoming the Default Enterprise AI Platform For years, “cloud-first” was treated like a universal truth. But as AI moves from proofs of concept to production, enterprise platforms are being stress-tested in ways traditional application stacks never were. From my perspective, hybrid cloud isn’t a compromise anymore—it’s quickly becoming the most practical operating model for modern enterprises. AI changes the cost conversation because it introduces workloads that are compute-hungry and often always-on. When you’re training models, fine-tuning continuously, or running inference at scale across the business, the economics can shift fast. Elasticity is still valuable, but predictability becomes just as important—especially when leadership wants reliable unit costs and fewer billing surprises. Latency also stops being an optimization goal and becomes a hard requirement. There are plenty of use cases where you simply can’t afford the round trip to a distant region and back. When decisions need to happen in milliseconds, placing inference closer to users, devices, and operations is less about preference and more about making the system viable. Then there’s data gravity, governance, and sovereignty. Real-world enterprises don’t get to pretend that sensitive data, jurisdictional rules, and internal controls are optional. Many organizations will keep critical datasets and portions of the AI pipeline close to where the data is created and governed, because that’s often the simplest path to compliance and risk reduction. What’s emerging is a practical three-tier model that I expect to become the norm: public cloud for speed, experimentation, and burst capacity; on-prem for consistent production workloads that benefit from tight control and predictable economics; and edge for real-time inference where latency and availability are non-negotiable. The winning strategy isn’t choosing one environment—it’s placing workloads where they run best, technically and financially. If you’re shaping your enterprise platform strategy for 2026, start with this: are you optimizing around an ideology, or around workload reality? #HybridCloud #EnterprisePlatforms #AIInfrastructure #CloudComputing #EdgeComputing #PlatformEngineering #FinOps #DataGovernance #EnterpriseIT #DigitalTransformation

Hybrid Cloud vs Multi-cloud 👇 ✅ Hybrid Cloud When thinking about a hybrid model, this means you want to incorporate both on-prem and in the cloud. And despite what the internet tells us, on-prem is still very popular and very-much needed for some organizations. The goal with hybrid would be to run some functions locally (maybe for compliance or bandwidth purposes) while other functionality/app stacks run in the cloud. A good thing to do here as well is make your services burstable. For example, if you're running out of resources in your on-prem datacenter, you can "burst" those resources to the cloud. Some noticeable tools/platforms in this arena are: 1. Azure Local (used to be Azure Stack HCI). 2. Google Anthos 3. EKS Anywhere 4. AWS Outposts ✅ Multi-cloud When thinking about multi-cloud, it's when you use more than one cloud to run your functionality and application stacks. A good example is if you want to use Azure Active Directory (AD)/Entra for authentication and authorization for your Google Kubernetes Engine (GKE) cluster. One big thing to point out here is to understand what exactly you want to do with multi-cloud. For example, I have clients that ask to build multi-cloud, but when we dive into the needs together, it turns out they want redundancy, which could be implemented in one cloud with multi-region instead of multi-cloud. The most important factor to keep in mind here, as with all technology implementations, is to ensure you know what you actually need prior to implementing anything. As with any good SDLC process, come up with, architect, and plan first. #cloud #kubernetes #devops

𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝗪𝗵𝘆 𝗟𝗼𝗸𝗶 & 𝗧𝗲𝗺𝗽𝗼 𝗼𝗻 𝗖𝗹𝗼𝘂𝗱 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗢𝘂𝘁𝘀𝗵𝗶𝗻𝗲 𝗘𝗟𝗞 & 𝗝𝗮𝗲𝗴𝗲𝗿 For teams hosting modern applications, choosing the right observability tools is paramount. While the ELK stack (Elasticsearch, Logstash, Kibana) and Jaeger are popular choices, I want to make a strong case for considering Loki and Tempo, especially when paired with Google Cloud Storage (GCS) or AWS S3. Here's why this combination can be a game-changer: 🚀 Scalability Without the Headache: 1 . Loki: Designed for logs from the ground up, Loki excels at handling massive log volumes with its efficient indexing approach. Unlike Elasticsearch, which indexes every word, Loki indexes only metadata, leading to significantly lower storage costs and faster query performance at scale. Scaling Loki horizontally is also remarkably straightforward. 2 . Tempo: Similarly, Tempo, a CNCF project like Loki, offers a highly scalable and cost-effective solution for tracing. It doesn't index spans, but rather relies on object storage to store them, making it incredibly efficient for handling large trace data volumes. 🤝 Effortless Integration: Both Loki and Tempo are designed to integrate seamlessly with Prometheus, the leading cloud-native monitoring system. This creates a unified observability platform, simplifying setup and operation. Imagine effortlessly pivoting from metrics to logs and traces within the same ecosystem! Integration with other tools like Grafana for visualization is also first-class, providing a smooth and intuitive user experience. 💰 Significant Cost Savings: The combination with GCS or S3 buckets truly shines. By leveraging the scalability and cost-effectiveness of object storage, you can drastically reduce your infrastructure costs compared to provisioning and managing dedicated disk for Elasticsearch and Jaeger. The operational overhead associated with managing and scaling storage for ELK and Jaeger can be substantial. Offloading this to managed cloud storage services frees up valuable engineering time and resources. 💡 Key Advantages Summarized: 1 . Superior Scalability: Handle massive log and trace volumes with ease. 2 . Simplified Integration: Seamlessly integrates with Prometheus and Grafana. 3 . Significant Cost Reduction: Leverage the affordability of cloud object storage. 4 . Reduced Operational Overhead: Eliminate the complexities of managing dedicated storage. Of course, every team's needs are unique. However, if scalability, ease of integration, and cost savings are high on your priority list, I strongly encourage you to explore Loki for logs and Tempo for traces, backed by the power and affordability of GCS or S3. Implementation screenshots shown below took me less than 2 nights to implement using argo-cd + helm + kustomize ... https://lnkd.in/gZyB5VZj #observability #logs #tracing #loki #tempo #grafana #prometheus #gcp #aws #cloudnative #devops #sre

FinOps for hybrid cloud – what’s new & what’s changing? Hybrid cloud is not just "cloud plus a data center." It’s a collision of compute models, pricing logic, tagging systems, and visibility gaps. Here’s what’s really changing: 1. Tags don’t travel across borders. Cloud gives you native tags. On-prem gives you naming conventions. AI agents? Good luck tracing anything at all. In hybrid setups, every platform speaks a different tagging dialect - and most FinOps tools expect a single language. Fix: Build a translation layer. Map every tag, label, and telemetry source into a common schema - like TBM 5.0 or FOCUS. Tag what you can, map what you can’t. It’s not perfect tagging - it’s consistent context. 2. You can't optimize what teams don’t understand. Infra teams think in CPU hours. Finance teams think in GL codes. Product teams care about cost per outcome - but don’t have access to the numbers. This mismatch is the reason why cost optimization efforts get ignored or delayed. Fix: Switch to unit cost storytelling. Frame every FinOps discussion around “cost per customer session,” “cost per API call,” or “cost per insurance claim.” When engineers and CFOs look at the same KPI, magic happens. 3. On-prem idle isn't free - it’s just invisible. Cloud idle shows up on your bill. On-prem idle hides in your racks. The myth? “We already paid for it, so it's fine.” The reality? That unused capacity is a silent cost. And nobody's being held accountable for it. Fix: Treat idle as risk premium or future demand buffer. Then allocate its cost like insurance - spread across services that rely on its availability. 4. Observability costs are the new FinOps blind spot. You added logs, traces, metrics… Now your observability bill is bigger than your AI spend. In hybrid systems, telemetry doesn’t just help - it hurts if left unchecked. Fix: Implement telemetry budgets. Decide: Which spans matter? Which logs convert to insight? Observability without prioritization is just expensive noise. 5. AI breaks every legacy cost model. Hybrid cloud isn’t just servers and services anymore - it’s agents, LLMs, retrievers, and pipelines spread across multiple systems. One task might touch five clouds, three APIs, and burn a million tokens - none of it showing up clearly in your FinOps dashboard. Fix: Shift your thinking from asset-level costs to outcome-level costing. Ask: What did this workflow produce? What did it consume? Build cost models around outcomes, not just infrastructure units. Even without perfect observability, this mental model helps separate profitable AI patterns from budget sinkholes. Hybrid cloud isn’t the future. It’s already here. And FinOps can’t be cloud-only anymore. If your current FinOps strategy can’t handle these realities - you’re just watching spend, not governing it. #FinOps #Mavvrik

📍 Enterprise Network Resilience Solution: How Can Enterprises Eliminate Single Points of Failure Through Secure High-Availability Network Design? Network resilience isn't built on luck; it’s built on rigorous engineering and the systematic elimination of Single Points of Failure (SPOFs). As a Network Security Engineer, understanding every layer of a network architecture is fundamental. Security is not limited to the firewall; it requires a complete understanding of how the Edge, Core, Distribution, Access, and Cloud layers work together to deliver a secure, resilient, and scalable infrastructure. 📌 Here is a breakdown of the core technical pillars supporting this infrastructure: 1- Edge (Fortigate Firewall) Tier: The Edge Security Tier is the first line of defense in an enterprise network architecture. Deployment of a FortiGate HA (High Availability) cluster (Active/Passive or Active/Active). This secures the network edge and guarantees uninterrupted traffic inspection and seamless failover if a primary gateway drops. 2- Core Switching Tier (Layer 3): Implementation of an MCLAG Stack (Multi-Chassis Link Aggregation). By leveraging MCLAG at the L3 core, we eliminate Spanning Tree Protocol (STP) blocking loops, maximize bandwidth utilization, and build a highly resilient, high-speed backbone. 3- Redundancy and Aggregation: Access switches are dual-homed to the core via cross-chassis LACP (EtherChannel) trunk links. If a core switch or a physical link fails, sub-second deterministic convergence ensures zero impact on end-users. 4- Access Switching Tier (Layer 2): Strict VLAN segmentation at the access layer to isolate user traffic, minimize broadcast domains, and enforce zero-trust security principles internally. 📌 The Result: A highly adaptable, scalable, and fault-tolerant architecture ready to power corporate campuses, enterprise environments, or modern data centers. Security is not limited to the firewall; it requires a complete understanding of how the Edge, Core, Distribution, Access, and Cloud layers work together to deliver a secure, resilient, and scalable infrastructure. Architecture knowledge is what transforms a technician into an engineer. #NetworkEngineering #CyberSecurity #Fortinet #Networking #HighAvailability #Infrastructure #Cisco #EnterpriseNetwork #SystemsArchitecture

Building a Resilient, Secure, and Scalable Enterprise Network : Over the past few days, I’ve been working on designing and implementing a multi-layer, redundant network architecture that balances performance, security, and high availability. Here’s what the setup includes: 🔹 Layer 3 Core Switching with HSRP for gateway redundancy 🔹 LACP Link Aggregation between core switches for bandwidth and failover 🔹 FortiGate Firewall with SD-WAN for dual ISP load balancing and failover 🔹 VLAN Segmentation (10, 20, 30) for access control and traffic isolation 🔹 DHCP Failover across core switches for uninterrupted IP assignment 🔹 Windows Server with NIC Teaming for redundancy and higher throughput 🔹 SSH-enabled switches for secure remote management 🔹 Port Security & DHCP Snooping to prevent MAC spoofing and rogue DHCP servers 🔹 Spanning Tree PortFast for faster host connectivity 🔹 DMZ Implementation for secure server hosting (Domain Controller) 🔹 User workstations successfully joined to the Domain Controller for centralized authentication and policy management . 💡 Key Benefits: ✅ High availability through dual ISPs, redundant gateways, LACP, and NIC teaming ✅ Improved security with VLAN isolation, port security, and DHCP snooping ✅ Better network performance via traffic segmentation and load balancing ✅ Secure administration through SSH-based switch management ✅ Centralized user management and authentication through Active Directory integration ✅ Future-ready architecture to scale with business growth This project was a great opportunity to merge security best practices with network redundancy techniques, ensuring both uptime and protection. #Networking #Cisco #FortiGate #SDWAN #HSRP #DHCP #LACP #PortSecurity #VLAN #NetworkSecurity #ITInfrastructure #NetworkEngineering #WindowsServer #NICTeaming #SSH #ActiveDirectory

Azure Cloud Project – Hybrid Infrastructure I’m excited to share my Azure Cloud Project where I designed and implemented a Hybrid Cloud Infrastructure connecting the On-Premises environment with Microsoft Azure. The goal was to design a secure, scalable, and fully managed hybrid setup — ensuring centralized control, traffic inspection, and seamless communication between cloud and on-prem resources. 🌐 Project Overview 🔹Built a Hybrid Cloud Infrastructure connecting the On-Premises environment with Microsoft Azure for secure and centralized management. 🔹Two VNets (VNET1 & VNET2) connected via VNet Peering for internal communication. 🔹VNET1: Contains the Jump Server with Azure Bastion for secure RDP access without exposing any public IPs. 🔹VNET2: Hosts two Web Servers behind an Azure Load Balancer for availability and performance. 🔹Implemented Azure Firewall with Application, Network, and DNAT Rules to control and inspect all traffic flows. 🔹Configured User Defined Routes (UDR) so that all network traffic passes through the Firewall, ensuring consistent policy enforcement. 🔹Applied an NSG on VNET2, allowing only RDP access from VNET1, adding an extra security layer. 🔹Created a Private DNS Zone for internal name resolution between Azure and On-Prem resources without using public DNS. 🔹Integrated Azure Log Analytics Workspace to collect and monitor Firewall logs, VM performance, and On-Prem telemetry for full visibility. 🔹Enabled Microsoft Defender for Cloud to provide continuous security assessments and recommendations. 🔹Connected the On-Premises network using a Private Switch and External NIC for the ADM1 server, ensuring hybrid communication with Azure. 🔹Configured an Azure File Share (Drive S:) for the Web Servers via a Service Endpoint — SMB (TCP 445) access is restricted to the VNet/subnet scope (or can be via Private Endpoint) so the file share is not exposed to the public internet. 🔹Deployed Windows Admin Center (WAC) in the On-Premises environment and connected it to Azure via the Gateway Subnet, providing centralized management and monitoring for both On-Prem and Azure virtual machines from a single interface. 🎯 This project helped me strengthen my understanding of network security, hybrid connectivity, and Azure infrastructure management — combining both cloud and on-premises systems under one secure architecture. hashtag #Azure hashtag #CloudComputing hashtag #MicrosoftAzure hashtag #HybridCloud

If you’re starting your Cloud / DevOps journey in 2025 — make sure Prometheus and Grafana are on your roadmap.. Here’s a quick overview: Both Prometheus and Grafana are observability tools focused on monitoring — → Prometheus collects and stores metrics (plus alerting). → Grafana visualizes those metrics through rich dashboards. Companies can: - self-host them on VMs or Kubernetes, - or choose managed offerings from major cloud providers (AWS, Azure, GCP) + grafana cloud Let’s dive in further: Prometheus handles: ↳ Collecting and storing metrics (from servers, containers, apps) ↳ Alerting when thresholds are breached (via Alertmanager) ↳ Infrastructure monitoring with exporters ↳ SLA/SLO tracking Grafana shines at: ↳ Building rich, interactive dashboards for metrics ↳ Business KPI monitoring alongside system data ↳ Log & metric correlation for root cause analysis ↳ Visualizing trends for capacity planning Why are they preferred for monitoring: • Real-time visibility across systems • Faster incident response with both historical + live data • Simplified application performance tracking (latency, error rates, throughput) • Deep insights into container & Kubernetes health (pods, resources, clusters) • Open-source and cloud-agnostic → no vendor lock-in That’s why they’ve become the go-to choice for modern observability. ⸻ Monitoring isn’t just about collecting data — it’s about using the right tools to see the bigger picture and act before things break. Question for you: Have you used Prometheus & Grafana mainly for infra monitoring, or also for business KPIs? • • • If you found this useful.. 🔔 Follow me (Vishakha) for more Cloud & DevOps insights ♻️ Share so others can learn as well!

🚀 𝐅𝐢𝐧𝐎𝐩𝐬 𝐁𝐞𝐲𝐨𝐧𝐝 𝐏𝐮𝐛𝐥𝐢𝐜 𝐂𝐥𝐨𝐮𝐝: 𝐀 𝐋𝐨𝐨𝐤 𝐀𝐭 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐀𝐧𝐝 𝐇𝐲𝐛𝐫𝐢𝐝 𝐂𝐥𝐨𝐮𝐝 In my latest Forbes Technology Council article, I delve into how FinOps practices are evolving beyond public cloud environments to encompass private and hybrid clouds, enhancing financial governance, resource utilization, and service delivery. 💡 𝐓𝐡𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐨𝐟 𝐇𝐲𝐛𝐫𝐢𝐝 𝐚𝐧𝐝 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐂𝐥𝐨𝐮𝐝 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐬 Hybrid and private clouds offer greater control over sensitive data and critical business operations while leveraging the scalability of public cloud services. However, this flexibility introduces challenges in cost tracking and management across different environments. Without unified cost management, organizations risk overpaying and underutilizing resources. 🔑 𝐊𝐞𝐲 𝐅𝐢𝐧𝐎𝐩𝐬 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐟𝐨𝐫 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 𝐚𝐧𝐝 𝐇𝐲𝐛𝐫𝐢𝐝 𝐂𝐥𝐨𝐮𝐝𝐬 Unified Cost Tracking and Reporting: FinOps should provide a single view of cloud costs across all platforms, enabling transparency up to the CFO level. Cost Allocation and Showback/Chargeback Models: Assigning costs to specific departments encourages accountability and promotes efficient resource usage. Operational Optimization: Regularly check for idle resources, rightsize instances, and decommission unnecessary services to reduce wastage. Automation and Cost Control: Implement automation to scale resources based on demand and turn off non-production environments during off-hours. Governance and Compliance: Ensure cloud environments meet strict governance and regulatory requirements, avoiding compliance risks and budget overruns. 🔄 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐅𝐢𝐧𝐎𝐩𝐬 𝐰𝐢𝐭𝐡 𝐈𝐓𝐈𝐋 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 By aligning FinOps with ITIL best practices, organizations can balance cost efficiency with service quality. This integration enhances service strategy, operation, and continual service improvement, providing financial insights that inform cloud service design and operational decisions. 🌐 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐅𝐢𝐧𝐎𝐩𝐬 𝐢𝐧 𝐇𝐲𝐛𝐫𝐢𝐝 𝐂𝐥𝐨𝐮𝐝 As hybrid and multicloud strategies become more prevalent, extending FinOps beyond public cloud silos is essential. Integrating FinOps with ITIL processes allows enterprises to optimize cloud spending, ensure compliance, and drive efficiency across hybrid and private clouds, navigating the next generation of cloud complexity. #FinOps #HybridCloud #PrivateCloud #CloudComputing #ITIL #CloudStrategy #CloudCostManagement #DigitalTransformation