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Demystifying Cloud Strategies: Public, Private, Hybrid, and Multi-Cloud As cloud adoption accelerates, understanding the core cloud computing models is key for technology professionals. In this post, I'll explain the major approaches and examples of how organizations leverage them. ☁️ Public Cloud Services are hosted on shared infrastructure by providers like AWS, Azure, GCP. Scalable, pay-as-you-go pricing. Examples: - AWS EC2 for scalable virtual servers   - S3 for cloud object storage - Azure Cognitive Services for AI capabilities - GCP Bigtable for large-scale NoSQL databases ☁️ Private Cloud Private cloud refers to dedicated infrastructure for a single organization, enabling increased customization and control. Examples:  - On-prem VMware private cloud - Internal Openstack private architecture - Managed private platforms like Azure Stack - Banks running private clouds for security ☁️ Hybrid Cloud Hybrid combines private cloud and public cloud. Sensitive data stays on-prem while leveraging public cloud benefits. Examples: - Storage on AWS S3, rest of app on-prem - Bursting to AWS for seasonal capacity - Data lakes on Azure with internal analytics ☁️ Multi-Cloud Multi-cloud utilizes multiple public clouds to mitigate vendor lock-in risks. Examples:  - Microservices across AWS and Azure  - Backup and DR across AWS, Azure, GCP - Media encoding on GCP, web app on Azure ☁️ Hybrid Multi-Cloud The emerging model - combining private infrastructure with multiple public clouds for ultimate flexibility. Examples: - Core private, additional capabilities leveraged from multiple public clouds - Compliance data kept private, rest in AWS and Azure  - VMware private cloud extended via AWS Outposts and Azure Stack Let me know if you have any other questions!

After reviewing 30+ SaaS contracts last quarter.... I've identified the 50 most commonly overlooked provisions that could save your business from costly disasters. The average enterprise now uses 130+ SaaS solutions, with critical business functions entirely dependent on third-party software. Yet 67% of SaaS agreements lack basic protections for: - Service interruptions - Data breaches - Vendor acquisition/bankruptcy - Unauthorized data usage The cost of these gaps? Companies lose an average of $218,000 per SaaS-related incident. 1. Service Level Agreement (SLA) Terms ☑️ Specific uptime commitments (99.9% isn't enough—define the measurement period) ☑️ Exclusions from SLA calculations (planned maintenance should be capped) ☑️ Meaningful compensation tied to impact (not symbolic credits) ☑️ Response time commitments for different severity levels ☑️ Escalation procedures with named contacts 2. Data Protection Provisions ☑️ Data residency requirements (specify geographic locations) ☑️ Processing limitations beyond standard privacy policies ☑️ Prohibition on de-anonymization attempts ☑️ Detailed breach notification timelines (24 hours should be standard) ☑️ Data return procedures upon termination (specify format) 3. Integration & API Requirements ☑️ API stability commitments with deprecation notice periods ☑️ Rate limiting disclosures and guarantees ☑️ Integration support obligations ☑️ Third-party connector maintenance responsibilities ☑️ Technical documentation updating requirements 4. Termination Rights & Processes ☑️ Partial termination rights for specific modules/services ☑️ Data extraction assistance requirements ☑️ Transition services obligations ☑️ Wind-down periods with reduced functionality ☑️ Post-termination data retention limitations 5. Liability Protections ☑️ Exception to liability caps for data breaches ☑️ Separate liability caps for different violation categories ☑️ Indemnification for vendor's regulatory non-compliance ☑️ Third-party claim procedures with vendor-provided defense ☑️ IP infringement remediation obligations 6. Service Evolution Safeguards ☑️ Feature removal notification periods (90+ days) ☑️ Version support commitments ☑️ Mandatory backward compatibility periods ☑️ Price protection for existing functionality ☑️ Training for significant interface changes Last month, a client using this checklist discovered their mission-critical SaaS provider had no formal commitments on API stability. After negotiation, they secured: - 180-day notice for any API changes - Technical support during transitions - Compensation for integration rework Three weeks later, the vendor announced a major API overhaul that would have cost $200K to adapt to without these protections. Want the expanded 50-point SaaS contract checklist with negotiation strategies for each provision? Comment "CHECKLIST" below and I'll send you the full resource. #contracts #saasagreements #saas #agreements #contractdrafting

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

Hey everyone! 👋 In my last post, I discussed some real-life use cases of shell scripting. Today, I'm thrilled to kick off a series where I'll dive deeper into five detailed use cases along with the actual shell script code. 🤓 🔍 𝗣𝗼𝘀𝘁 𝟭 𝗼𝗳 𝟱: 𝗠𝗼𝗻𝗴𝗼𝗗𝗕 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗕𝗮𝗰𝗸𝘂𝗽𝘀 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 (𝗥𝗲𝗮𝗹 𝗹𝗶𝗳𝗲 𝘂𝘀𝗲-𝗰𝗮𝘀𝗲) 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨: 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐲𝐨𝐮'𝐫𝐞 𝐚 𝐃𝐞𝐯𝐎𝐩𝐬 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐟𝐨𝐫 𝐭𝐚𝐤𝐢𝐧𝐠 𝐝𝐚𝐢𝐥𝐲 𝐛𝐚𝐜𝐤𝐮𝐩𝐬 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐞𝐧𝐭𝐢𝐫𝐞 𝐝𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐚𝐭 𝟖 𝐚.𝐦. 𝐀𝐝𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥𝐥𝐲, 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐫𝐞𝐦𝐨𝐯𝐞 𝐛𝐚𝐜𝐤𝐮𝐩 𝐟𝐢𝐥𝐞𝐬 𝐨𝐥𝐝𝐞𝐫 𝐭𝐡𝐚𝐧 𝐨𝐧𝐞 𝐝𝐚𝐲. 🔧 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: This scenario is easily managed using shell scripting and cron jobs. I'll break down the script and provide the code to show you exactly how it's done. 🧑💻 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗮𝘁𝘁𝗮𝗰𝗵𝗲𝗱 𝗰𝗼𝗱𝗲: 1.) It sets the current date and time in a specific format and assigns them to the variable now. 2.) constructs a filename for the backup file using the current date and time. 3.) defines the backup folder and creates the full path for the backup file. 4.) Creates a log file with the current month-year timestamp. 5.) Logs the start time of the backup operation. 6.) executes the mongodump command to perform the backup. 7.) Logs the finish time of the backup operation. 8.) Changes the ownership of the backup file and log file to shivam. 9.) Logs a message indicating that file permissions have been changed. 10.) Deletes backup files older than one day. 11.) logs a message indicating that old files have been deleted. 12.) Logs the finish time of the entire operation and adds a delimiter to the log file. 13.) Exit the script with a status of 0 (indicating success). 📝 Don't forget to hit the like button if you're excited about this series! And sharing is caring, so please share this post with your network. I'd love to hear your thoughts in the comments section below. Let's dive in! 💬💡 #DevOps #ShellScript #DevOpsEngineers #DevOpsTraining #Bash #Scripting #Automation #InterviewQuestions #DevOpsCommunity #ShareThisPost

Most SaaS founders don’t think about their SLA Until something breaks. • The server goes down. • A key customer threatens to churn. • A dispute lands in the inbox. And then they get panicked: "Wait... what did we actually promise in the SLA?" I’ve reviewed enough SaaS agreements to know the pattern. The same blind spots show up again and again. That’s why my team uses a simple SLA checklist. Here's 5 areas we always review to make sure it holds up when it matters most. 1) Service availability & performance • Clear uptime % and response time commitments • Maintenance window rules • How metrics are measured and reported 2) Compensation & penalties • Credits for downtime • Escalation rules and caps • How credits are claimed (and when they expire) 3) Support & response framework • Support tiers and hours • Response and resolution time commitments • Escalation paths and support channels 4) Security & compliance • Data protection measures • Backup and recovery procedures • Breach notification timelines • Data ownership and portability 5) Flexibility & exit • Review periods for SLAs • Termination triggers and notice periods • Data export and migration terms • Force majeure exclusions The best SLAs don’t overwhelm with legalese. They cover these five areas with precision so both sides know what to expect. Don’t wait for 2 AM downtime to test yours. Review these five areas before your next renewal or new customer signs on. --- ✍ Which of these five SLA elements do you see most often missing in SaaS contracts?

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

Comfy experiment chamber - control your environment! Ever installed a custom node and felt like your setup just exploded? Or downloaded an old workflow only to find it’s incompatible with the latest custom nodes? It’s one of the most frustrating parts of working with custom setups - everything works perfectly one day, and the next, it’s chaos. You’re left scrambling to figure out what broke, why it broke, and how to fix it. Trust me, I’ve been there. Too many times to count. But here’s the fix: the ComfyUI Environment Manager. Created by Akatz, a pro Comfy node dev, this tool is here to save you from endless headaches and wasted time. It lets you run ComfyUI inside containerized environments, making your workflow smoother and your setup safer. Here’s why this tool is a game-changer: 🔹 Isolate Your Environments: Run your workflows confidently, knowing every custom node and package is dialed in perfectly. No more random crashes or unexpected errors. 🔹 Easy Version Control: Switch between environments like a breeze without messing up your main setup. Need to test an older workflow? Done in seconds. 🔹 Security & Flexibility: Keep your machine safe from unwanted changes and experiment as wildly as you want in isolated environments. Try out new ideas without worrying about breaking things. Think of it as your personal sandbox. You can experiment with custom nodes, test out different workflows, and push the limits of what’s possible, all without the fear of breaking your main setup. It’s a safe space for creativity, where you can tweak, test, and tinker to your heart’s content. Git - https://lnkd.in/dedd_H2K Setup tutorial - https://lnkd.in/dVW-VDT6 Notion doc - https://lnkd.in/dwRFbjj2 #comfyui #environment #control #sandbox

How Well Do You Understand the System Design Ecosystem? Designing a modern, scalable system isn't just about picking the right database or breaking a monolith into microservices. It’s about understanding how all the layers, from infrastructure to orchestration, work together like a well-organized machine. Here’s a complete System Design Ecosystem, breaking it down into Core, Service, System, and Ecosystem layers. Whether you’re building your first backend or scaling to millions of users, these layers must work together perfectly to deliver performance, reliability, and scalability. Here’s what each layer includes: 1. Core Layer → Databases, Load Balancers, Storage, Caching, CDN, DNS, Search, API Gateway Foundational infrastructure that powers all modern apps. 2. Service Layer → Microservices, Message Queues, Service Discovery, Workflow Orchestration Handles modularity, communication, and task management in a service-oriented architecture. 3. System Layer → Monitoring, Logging, Security, Observability, Failover & Recovery, Config Mgmt Ensures visibility, reliability, and safety across distributed systems. 4. Ecosystem Layer → Orchestration (Kubernetes), CI/CD Pipelines, Scaling Strategies, Cost Management, Compliance & Governance Brings everything together for scale, automation, compliance, and cost efficiency. Save this if you're building scalable architectures or prepping for a system design interview. It's your blueprint to think beyond just services and build reliable ecosystems.

Don’t settle for a toy model. Distributed training is the key to scaling a prototype model to an enterprise model. But distributed systems have a lingo of their own. So here’s an intro. Distributed learning is the practice of training a single model using multiple GPUs or machines, which are coordinated to work in parallel by distributing the data, the model, or both. GPUs are processors with cores that are optimized for parallel computing, which is exactly what we want in distributed training. We want model training to happen in parallel. Parallelization strategies are ways to splits the task of training a model across different resources. 📊 Data Parallelism: Replicates the model, split the data. ✨ Model Parallelism: Splits the model's layers across GPUs. 🔩 Pipeline Parallelism: Splits the model, process it like an assembly line. 🧊 Tensor Parallelism: Splits a single layer's tensors across GPUs. But distributed training isn’t only about GPUs. Sometimes your model’s footprint may be too big for a single server (also called a node) or you may need more GPUs than a single server can hold. In this case, you would scale to multi-node training. The easiest way to scale your training job is to use cloud compute. These companies generally fall into a few categories (with some overlap): • Traditional Public Cloud: Wide array of services, including GPUs, as a small part of their overall infrastructure (e.g. Amazon Web Services (AWS), Microsoft Azure, Google Cloud). • Specialized GPU Cloud Providers: Focus exclusively on providing purpose-built GPU hardware and infrastructure for AI and machine learning workloads (e.g. Runpod, Lambda, Nebius, CoreWeave). • Serverless GPU Platforms: Platforms that abstract away infrastructure management, allowing users to deploy and scale models on-demand with a simple API call (e.g. Modal, Baseten). • Decentralized Compute: A network that pools computing power from a distributed network of individually owned machines to provide a collective resource (e.g. Prime Intellect). When you want to implement distributed learning in Python, you have several options. These frameworks fall into low- and high-level categories. Low-level frameworks like Ray (Anyscale), PyTorch, DeepSpeed.ai, and Accelerate (Hugging Face) serve as the building blocks of distributed learning, giving you maximum control, flexibility, and ability to customize your training pipelines. High-level frameworks like Axolotl and Unsloth AI specialize specifically in model fine-tuning, abstracting away the complexity of the lower-level frameworks. They make it easy to get started by providing ready-to-use solutions for specific fine-tuning tasks. There’s a lot more to scaling your model training than just this. If you’re interested in learning more, check out Zachary Mueller's course Scratch to Scale, which starts this September. 🔗 Scratch to Scale: https://lnkd.in/gKKuzaaH