Workflow Scalability Solutions

Power Automate Work Queues are not built for scale! That's a fact. When you think about scalability in Power Automate, one thing that will definitely come to mind at some point is queues and workload management. While you might be able to survive without them in some event-based transactional flows that only process a single item at a time, but whenever you process tasks in batches, or when RPA gets involved, you'll need queues. Power Automate comes with Work Queues out of the box. And you would think that's your go-to queueing mechanism for scaling. After all, it's at scale that you really need those queues - to de-couple your flows and make it easier to maintain, support, debug them, as well as make them more robust and efficient. Queues is a must even at medium scale. Heck, we use them even in small scale implementations. But the surprising thing about Power Automate Work Queues is that they are not fit for high scale implementations. And that is by design! The docs themselves (link in the comments) explicitly state that if have high volumes or if you dequeue (pick up work items from the queue for processing) concurrently, you should either do it within moderate levels or use something else. If you try and use Power Automate Work Queues for high scale implementations (more than 5 concurrent dequeue operations or hundreds/thousands of any type operations involving the queues), you'll get in trouble. There can be all sorts of issues that could happen - your data may get duplicated, you may accidentally deque the same work item in multiple concurrent instances, or your flows might simply get throttled or even crash. This is because of the way they're build and the way they utilize Dataverse tables for storing work items and work queue metadata. So, if you do want to scale, it's best to use an alternative. And, obviously, Microsoft wouldn't be Microsoft if they didn't have an alternative tool to do that. The docs themselves recommend Azure Service Bus Queues for high throughput queueing mechanisms. Another alternative could also be Azure Storage Queues, but that only makes sense if the individual work items in your queue can get large (lots of data or even documents) or when you expect your queue to grow beyond 80GB (which is possible in very large scale implementations). Otherwise, Azure Service Bus Queues are absolutely perfect for very large volumes of small transactions. On top of that, they have some very advanced features for managing, tracking, auditing and otherwise handling your work items. And, of course, there's a existing connector in Power Automate to use it. So, while I do love Power Automate Work Queues, I'll only use them in relatively small scale implementations. And for everything else - my queues will go to Azure. And so should yours.

Challenges faced in LLM Deployments in Enterprise Environments. As enterprises increasingly adopt large language models (LLMs) to transform workflows, the transition from prototypes to production environments reveals critical architectural challenges. One recurring issue? API rate limits. While small-scale systems handle dozens of users seamlessly, scaling to serve 50,000+ employees often triggers cascading 429 errors during peak usage. This isn’t just a technical hiccup, it’s a systemic challenge that requires rethinking architecture to ensure reliability and performance at scale. The solution lies in distributed architecture patterns: Intelligent load balancing across geographically dispersed API endpoints (e.g., US-East, EU-West, Asia-Pacific). Circuit breaker mechanisms to reroute traffic during regional throttling events. Real-time monitoring dashboards to track RPM utilization while adhering to data residency mandates. Beyond the technical complexities, there’s also a financial dimension. Token-based pricing models often force enterprises to maintain 3-5x capacity buffers to avoid service degradation during spikes, a costly yet necessary trade-off for reliability. Scaling LLMs is not just about adding capacity; it’s about building resilient systems that anticipate demand surges. AI gateways with predictive auto-scaling algorithms, leveraging historical traffic patterns, calendar events, and real-time queue depths, are key to staying ahead of the curve. Solving these issues requires not just technical expertise but also a shared commitment to innovation and operational excellence. For those working on similar challenges, I’d love to hear how you’re addressing scalability in your LLM deployments! Let’s keep the conversation going. #AI #ArtificialIntelligence #Innovation #Technology #FutureOfWork #DigitalTransformation #CloudComputing #EnterpriseArchitecture #Scalability #APIDevelopment

#Workflow Managers! Workflow managers like #Nextflow, #Snakemake, #CWL, #WDL (#cromwell), #ensembl‑hive, and others act as orchestrators/conductors. They: 🔹 Define dependencies between tasks (e.g. FASTQ → alignment → variant calling) 🔹 Use executors to send jobs to HPC, cloud, Kubernetes, etc. (e.g. Slurm, AWS Batch, LSF, SGE) 🔹 Track status, retries, logging, error handling, and provenance 🔹 Allow workflows to be reproduced and resumed, even mid‑execution with caching 🔹 They support containers, resource specs, and automatic parallelisation through portable DSLs or config ➿ Workflow Patterns Workflow managing tools essentially build and run Directed Acyclic Graphs (DAGs). Common execution patterns use asynchronous type communication and include: 🪭 Fan – one task splits into multiple parallel jobs (e.g. process 100 samples). 🍸 Funnel – results gathered and merged back into one downstream task. ⛔ Semaphore or Barrier – wait until all tasks in a stage finish before continuing. ❓ Conditional execution – run tasks only if e.g. QC fails. These patterns enable flexible, parallel, and reproducible pipelines across all major systems. ℹ️ Scaling, Performance & IO Tips 🔸 Batch and Chunk High-Memory or Heavy-IO Jobs/ Divide-and-Conquer Strategy For memory-intensive tools, partition/split data (e.g. chromosomes, bam file regions) and run parallel subprocesses before merging (funnelling) - this is beneficial to reduce RAM requirements and helps to mitigate exit 137 OOM issues. 🔸 Beware Heavy I/O Steps Tasks like indexing or sorting in many tools can saturate disk space. Use local scratch space (e.g. `$TMPDIR`) or use RAM-disks/IO optimised compute instances, and delete intermediate files as soon as they’re no longer needed. 🔸 Specify Resources Explicitly Always define accurate CPU, memory, and time requirements with slight contingency. Overcommitting kills performance; under-allocating introduces job failures. 🔸 Leverage Caching & Resume Features Nextflow, Snakemake, CWL, WDL and ensembl-hive all support resuming where things did not complete or something changed - ideal for long-running or costly tasks. It saves costs and time (and the environment). Watch out for unintended non-deterministic patterns that may break serialisation in Nextflow! (I've been bitten by this!). 🔸 Authorise Executors Thoughtfully Aim for executors that work with containerisation (Docker, Singularity/apptainer etc), but tune your cluster/batch submission parameters (e.g. job arrays vs scatter, progressive best fit, spot allocation etc). 🔸 Avoid Workflow Overhead Thousands of small jobs can slow down the scheduler. Group trivial tasks where possible. Hope this acts as a good reminder/quick guide, let me know in the comments if you have any other workflow-manager-agnostic, or workflow-manager-specific tips and tricks - which workflow manager do you most predominantly use?

Clay just shipped something I've been waiting for: Functions. Every time I start a new table, I rebuild the same stuff from scratch: my enrichment waterfall, my scoring model, my signal sequences. Same logic, different table, every time. 𝗕𝘂𝗶𝗹𝗱 𝗶𝘁 𝗼𝗻𝗰𝗲 𝗶𝗻 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗶𝘁 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲. Edit one function and it updates across every table using it automatically. This is what makes Clay actually scalable across a whole team. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗿𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝘁𝗵𝗮𝘁 𝘁𝗮𝗸𝗲 𝗮 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝘀𝗲𝘁 𝗼𝗳 𝗶𝗻𝗽𝘂𝘁𝘀, 𝗿𝘂𝗻 𝗮 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗼𝗳 𝗲𝗻𝗿𝗶𝗰𝗵𝗺𝗲𝗻𝘁𝘀, 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗼𝘂𝘁𝗽𝘂𝘁𝘀 — 𝗮𝗹𝗹 𝗳𝗿𝗼𝗺 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗰𝗼𝗹𝘂𝗺𝗻. Instead of rebuilding the same enrichment logic across dozens of tables, you build it once as a function and reference it everywhere. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 👇️ 𝗔𝗻𝘆𝗼𝗻𝗲 𝗰𝗮𝗻 𝘂𝘀𝗲 𝘁𝗵𝗲𝗺, 𝗯𝘂𝘁 𝘆𝗼𝘂 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝘁𝗵𝗲 𝗹𝗼𝗴𝗶𝗰. Functions lets Ops teams enable more marketers, SDRs, and other non-power users to run workflows on their own — using logic you've already built and validated. These users can call on functions to perform tasks without risk of breaking the systems behind it. Soon, anyone can access these functions via MCP in places like Claude and ChatGPT. 𝗖𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗽𝗿𝗼𝗽𝗮𝗴𝗮𝘁𝗶𝗻𝗴 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆. When you edit a function and publish, every table that references it picks up the change. No hunting through workbooks, no missed updates, no version inconsistency. Edit Mode gives you a sandboxed environment to test changes before you publish, so you can safely iterate without disrupting live workflows. 𝗦𝗵𝗮𝗿𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘄𝗼𝗿𝗸𝘀𝗽𝗮𝗰𝗲𝘀. This one is for our agency partners. If you have enrichment logic that you use regularly for multiple clients, use a shareable link to duplicate functions into other workspaces and reduce time rebuilding the same workflows. Check out our functions library for more best practice functions from the Solutions Engineering team at Clay. 𝗢𝗻𝗲 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝗮𝗻 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗼𝗹𝘂𝗺𝗻 𝗴𝗿𝗼𝘂𝗽. One function column replaces an entire column group. A 12-step enrichment sequence that used to sprawl across a dozen columns now collapses into one. Your tables stay clean, you stay well under column limits, and navigation is dramatically faster — particularly when you're working with 40K+ rows. Think of it this way: if enrichments are atoms, functions are molecules — compound, reusable building blocks that snap into any workflow.

Here’s an impossible choice: use cloud-first file streaming tools that ignore on-premises infrastructure, or stick with traditional file sync workflows that can't scale to distributed teams. Cloud-based NAS and similar collaboration platforms excel at cloud streaming. Still, they typically require you to abandon your on-premises storage, struggle with render farm integration, and lack the necessary APIs for production automation. When your render nodes need to pull data through an ISP bottleneck instead of accessing shared storage directly, you immediately feel the performance hit. The core requirements for modern VFX workflows are clear: ✅ Hybrid architecture supporting both on-premises and cloud storage ✅ File streaming with progressive hydration for remote artists ✅ Direct storage access for render farms and services—no client required ✅ Full API for pipeline automation ✅ Client delivery without forced application downloads This is the VFX data distribution problem that nobody has solved—until now. With our new file streaming feature (I named it “Slipstream”), we've built exactly this. Active Everywhere enables true hybrid workflows: sync between data centers and cloud, stream files to remote workstations, and deliver to clients—all while your render farms access storage directly at line speed. The technical proof is in production. Cameron Target has offered a ShotGrid webhooks integration (available on GitHub) that demonstrates programmatic job management using our API—creating and destroying Hybrid Work jobs as shots move through the pipeline, with progressive hydration working directly in Nuke. For a full technical breakdown and implementation details, check out Cameron's article on the Resilio Blog (link in comments).

Someone asked me about my fundamental choices for a solid AI at Scale solution. Here is my response and why. Top 6 coming at you… 1. Amazon Bedrock – Foundation Models as a Service • Why: Lets me tap Anthropic, Meta, Amazon, Cohere, and others without building model infra. • Mass impact: One endpoint for multiple models = democratized access for devs and enterprises. • Governance: Bedrock Guardrails + Knowledge Bases give me control over safety and retrieval. 2. LangChain / LangGraph – Agent & Workflow Framework • Why: I need composability—memory, retrieval, multi-step orchestration, agent routing. • Mass impact: It lowers the barrier for thousands of devs who don’t want to re-invent orchestration logic. • Future-proof: Works across models, integrates with Bedrock, OpenAI, or open-source. 3. Vector Database (Pinecone / Weaviate / OpenSearch Serverless) • Why: RAG is the only way to make AI useful at scale with enterprise data. • Mass impact: Makes private knowledge searchable and usable by anyone, not just data scientists. • Enterprise fit: I’d lean OpenSearch Serverless inside AWS for tight compliance and ops. 4. Step Functions / Temporal – Deterministic Orchestration • Why: n8n/Zapier are great at the edge, but at scale I need durable, replayable, high-SLA orchestration. • Mass impact: Keeps long-running AI workflows reliable (days-to-weeks sagas, retries, state). • Choice: Step Functions if staying fully AWS, Temporal if I want portability. 5. Streamlit / Gradio (or equivalent low-code front end) • Why: To “bring AI to the masses,” the user interface must be simple, visual, and quick to iterate. • Mass impact: Enables non-technical users to experiment and deploy lightweight apps without waiting on IT. 6. OpenTelemetry + Grafana – Observability & Trust Layer • Why: If I don’t monitor prompts, outputs, latency, cost per call, and guardrail triggers, the system becomes a black box. • Mass impact: Building trust at scale requires transparency and feedback loops. • Bonus: Can plug into CloudWatch/Datadog; gives business KPIs tied to AI performance.  How I’d Deploy Them Together • Bedrock is my model backbone. • LangChain/LangGraph orchestrates agentic logic. • Vector DB powers RAG + personalization. • Step Functions/Temporal handle reliable, large-scale workflows. • Streamlit/Gradio put AI in human hands fast. • OpenTelemetry/Grafana ensure I can prove it’s working, safe, and ROI-positive.

Kubernetes Scaling Strategies: Horizontal Pod Autoscaling (HPA): Function: Adjusts the number of pod replicas based on CPU/memory usage or other select metrics. Workflow: The Metrics Server collects data → API Server communicates with the HPA controller → The HPA controller scales the number of pods up or down based on the metrics. Vertical Pod Autoscaling (VPA): Function: Adjusts the resource limits and requests (CPU/memory) for containers within pods. Workflow: The Metrics Server collects data → API Server communicates with the VPA controller → The VPA controller scales the resource requests and limits for pods. Cluster Autoscaling: Function: Adjusts the number of nodes in the cluster to ensure pods can be scheduled. Workflow: Scheduler identifies pending pods → Cluster Autoscaler determines the need for more nodes → New nodes are added to the cluster to accommodate the pending pods. Manual Scaling: Function: Manually adjusts the number of pod replicas. Workflow: A user uses the kubectl command to scale pods → API Server processes the command → The number of pods in the backend Kubernetes system is adjusted accordingly. Predictive Scaling: Function: Uses machine learning models to predict future workloads and scales resources proactively. Workflow: ML Forecast generates predictions → KEDA (Kubernetes-based Event Driven Autoscaling) acts on these predictions → Cluster Controller ensures resource balance by scaling resources. Custom Metrics Based Scaling: Function: Scales pods based on custom application-specific metrics. Workflow: Custom Metrics Server collects and provides metrics → HPA controller retrieves these metrics → The HPA controller scales the deployment based on custom metrics. These strategies ensure that Kubernetes environments can efficiently manage varying loads, maintain performance, and optimize resource usage. Each method offers different benefits depending on the specific needs of the application and infrastructure.

People who’ve used Airflow long enough are familiar with these common pain points: rigid DAGs, unclear observability, and frustrating scaling. Debugging a failing workflow at scale feels like archaeology, digging through logs, piecing together what went wrong. I’m guessing this is why I’m hearing about data operators moving to Prefect. The reason? It’s Python-native, flexible, and removes the operational overhead of managing an Airflow cluster. More importantly, it treats orchestration as a first-class concern, not an afterthought. I checked out a few of their blogs, and here’s what stands out: ✅ Their event-driven workflows: You can now move beyond static schedules. Prefect reacts to real-time events, making it ideal for dynamic pipelines. ✅ They enabled decoupled scheduling: No more Airflow’s single scheduler bottleneck. Prefect lets teams deploy and scale workflows independently. ✅ They have built-in observability: No more wondering where a task failed. Prefect provides full visibility without extra plugins. ✅ The dynamic infrastructure model: scale your infrastructure based on the specific workflow’s need with work pools. If Airflow feels like overhead, Prefect might be your next move. I would love to know who made the switch, as I am curious to hear your experience. Check out the blog posts below! #data #workflows #datapipelines

At Zomato, we often discussed service interactions. During my time there, a lot of new microservices were developed to handle the increasing load and demand on the platform. I recall my discussions with the in-house tech-architect team about implementing saga patterns for new services in the system. Many workflows at Zomato are long-lived, sometimes taking up to 60 minutes (or even more) to complete. Processes such as customer order placement, restaurant acceptance, rider search, rider assignment, order pickup, and delivery occur at different points in time and are managed by different services. Any state change in one service often requires updates in others. For example, if a restaurant declines an order, the system has to cancel the order and initiate a refund. To handle such scenarios, two widely used patterns emerge: Orchestration and Choreography. In the Orchestration pattern, a central orchestrator manages the workflow, ensuring that each step executes correctly. In this setup, individual services ideally support both “do” and “undo” actions for their respective steps. The orchestrator also handles retries and service invocations. In the Choreography pattern, it's decentralised, where individual services make their own decisions such as retrying or undoing an action based on events published by other services. While this approach is harder to visualise and manage, it generally scales better and is more fault-tolerant. I’ve come across Temporal.io, which provides centralised orchestration with built-in durability, fault tolerance, and automatic retries for workflow management. I’d love to hear from anyone who has hands-on experience using Temporal at scale. #saga #orchestrator #temporal

🚀 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬: 6 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 𝐄𝐯𝐞𝐫𝐲 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐌𝐚𝐬𝐭𝐞𝐫 In the world of #Kubernetes, scaling isn't just a "nice-to-have"—it’s the difference between a seamless user experience and a 3:00 AM production outage. 📉 But "scaling" isn't a one-size-fits-all solution. Depending on your workload, the "how" matters just as much as the "why." Here are 6 core strategies to keep your clusters resilient and your cloud bill optimized: 1️⃣ Manual Intervention (The Tactical Fix) 🛠️ The Workflow: Manually adjusting pod counts using kubectl scale. Best For: Debugging, one-off load tests, or small internal apps where automation overhead isn't worth it. The Vibe: Direct control, but zero elasticity. 2️⃣ Horizontal Pod Autoscaling (HPA) ↔️ The Workflow: Automatically adding or removing Pods based on CPU/RAM usage. Best For: Stateless applications like APIs and Web Servers. The Vibe: The "Industry Standard" for handling traffic spikes. 3️⃣ Vertical Pod Autoscaling (VPA) ↕️ The Workflow: Adjusting the Size (CPU/Memory requests) of existing pods. Best For: Long-running jobs or stateful workloads where more instances won't help, but more "muscle" will. The Vibe: Perfect for "right-sizing" and avoiding resource waste. 4️⃣ Cluster Autoscaling (The Infrastructure Layer) ☁️ The Workflow: Adding or removing the actual Nodes (VMs) when pods can't find a home. Best For: Dynamic cloud environments. The Vibe: If HPA is adding more passengers to the bus, Cluster Autoscaling is buying more buses. 5️⃣ Custom Metrics Scaling 📊 The Workflow: Scaling based on app-specific data (like Queue Length or Latency) rather than just hardware metrics. Best For: Event-driven architectures (e.g., processing a massive backlog of messages). The Vibe: Precision scaling for complex business logic. 6️⃣ Predictive Scaling (The Future-Seer) 🔮 The Workflow: Using ML models to forecast traffic and scaling before the surge hits. Best For: High-stakes events like Black Friday or scheduled morning peaks. The Vibe: Proactive readiness instead of reactive scrambling. Which strategy is your "go-to" for production? Let’s discuss in the comments! 👇 #Cloud #DevOps #CloudNative #PlatformEngineering #SRE #TechTips #Consulting #Architect