Cloud Solutions for Tracking Systems

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

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

Multi-Cloud + DevOps: Simplify Operations with PowerShell! Managing multiple clouds while keeping up with DevOps workflows can be overwhelming. PowerShell provides a unified solution to automate tasks across AWS, Azure, GCP, and CI/CD pipelines. Tip: Automate Multi-Cloud Resource Inventory with PowerShell Track and manage resources across multiple clouds in one go! Use PowerShell to fetch inventory data from Azure, AWS, and GCP and generate a consolidated report. Example: Multi-Cloud Inventory Script # Azure Inventory $azureResources = Get-AzResource | Select-Object Name, ResourceGroupName, Location # AWS Inventory $awsInstances = (Get-EC2Instance).Instances | Select-Object InstanceId, InstanceType, State # GCP Inventory (via gcloud CLI integration) $gcpResources = Invoke-Expression "gcloud compute instances list --format=json" | ConvertFrom-Json # Consolidate and Export to CSV $allResources = @($azureResources + $awsInstances + $gcpResources) $allResources | Export-Csv -Path "C:\CloudInventory\MultiCloudResources.csv" -NoTypeInformation Write-Output "Multi-cloud inventory exported. Perfect for DevOps tracking!" What it does: Retrieves resources from Azure, AWS, and GCP. Consolidates data into a single CSV file for reporting. Enhances DevOps workflows by automating resource tracking. Why it’s powerful: This approach eliminates manual tracking, enhances visibility across clouds, and integrates easily into DevOps pipelines for seamless operations. A game-changer for multi-cloud teams! #PowerShell #DevOps #MultiCloud #CloudManagement #Automation #TechTips #IT #Azure #GCP#Google #Microsoft #Amazon #Cloud

Why is observability non-negotiable for Network Engineering at hyperscale? With hundreds of thousands of interconnections, routing decisions happening every second, and petabits of traffic flowing through virtualized infrastructure, visibility is your key to success. In traditional environments, engineers can often SSH into a device, run a few show commands, correlate some logs, and find the problem. But in cloud-scale environments, where a single VPC misconfiguration, route leak, or transit gateway bottleneck could silently impact thousands of mission-critical customers, this model breaks down completely! Observability at scale is an engineering discipline. To truly scale networking operations, you must integrate observability into the system itself, not as an afterthought, but as a first-class element of the design process. Over time, I’ve learned this key lesson: “You can’t automate or scale what you can’t first observe, measure, and understand”. Here's how we tackle this challenge in high-scale environments: 1. Flow logs are just the beginning: Flow Logs are invaluable for tracking who talked to whom and when. But at scale, you must go beyond logs: - Structured telemetry for interface stats, drop counters, BGP neighbor health, and path churn. - Custom annotations tag traffic with metadata (region, owner, service). - Correlation engines to match telemetry across layers (network, compute, storage, edge). The goal is not just knowing that something broke; it's understanding why, where, when, and what the blast radius is. 2. Metrics without context are noise. It's easy to collect thousands of metrics; it's much harder to define the ones that actually matter. We focus on: - Golden signals: traffic volume, drop rate, latency, retransmits. - SLIs/SLOs per customer segment and traffic class. - Intent vs. Reality: alert when the actual state diverges from the declared network intent. 3. Distributed tracing for the network: Service-oriented teams already use distributed tracing (e.g., X-Ray, OpenTelemetry) for app performance. We’re applying the same concept to network flows. By correlating flow data, metadata tags, and telemetry snapshots across systems, we can: - Trace a packet’s journey across VPCs, AZs, and TGWs, - Reconstruct time-based events leading up to an incident, - Detect silent degradations long before customers do. 4. Building observability pipelines, not dashboards: Too often, observability is reduced to “let’s make a dashboard.” Instead, we treat observability as a pipeline, not just a UI: - Ingest raw telemetry, flow logs, BGP state, metrics. - Enrich with metadata (service, zone, owner, severity). - Process for patterns and anomalies (e.g., drops, excessive churn). - Act via alerts, auto-remediation, or ticketing. Good observability pipelines feed humans and automated systems that detect and resolve issues before they affect customers! Ensure to subscribe to my The Routing Intent newsletter for more!

$500k in spoiled vaccines vs. $50k in preventive tech. The difference? Not just technology—it’s proactive ownership. Some companies: - Depend on manual checks - React after the damage is done - Accept losses as "the cost of business" But the smarter ones? They’re preventing loss before it happens—by embedding real-time monitoring into their cold chain logistics. Here’s how leading providers are doing it with Azure: 1️⃣ IoT sensors are installed in transport containers to monitor temperature and humidity, feeding data directly into Azure IoT Hub. This integration allows logistics companies to access real-time data in their systems without disrupting operations. 2️⃣ Data flows seamlessly into Azure IoT Hub, where pre-configured modules handle the heavy lifting. The configuration syncs easily with ERP and tracking software, so companies avoid a complete tech rebuild while gaining real-time visibility. 3️⃣ Instead of piecing together data from multiple sources, Azure Data Lake acts as a secure, scalable repository. It integrates effortlessly with existing storage, reducing workflow complexity and giving logistics teams a single source of truth. 4️⃣ Then, Azure Databricks processes this data live, with built-in anomaly detection directly aligned with the current machine learning framework. This avoids the need for new workflows, keeping the system efficient and user-friendly. 5️⃣ If a temperature anomaly occurs, Azure Managed Endpoints immediately trigger alerts. Dashboards and mobile apps send notifications through the company’s existing alert systems, ensuring immediate action is taken. The bottom line? If healthcare companies want to reduce risk truly, proactive monitoring with real-time Azure insights is the answer. In a field where every minute matters, this setup safeguards patient health and reputations. Now, how would real-time monitoring fit into your logistics strategy? Share your thoughts below! 👇 #Healthcare #IoT #Azure #Simform #Logistics ==== PS.  Visit my profile, @Hiren, & subscribe to my weekly newsletter: - Get product engineering insights. - Discover proven development strategies. - Catch up on the latest Azure & Gen AI trends.