Data-Driven Load Optimization

I took this report’s load time from 10-15 seconds to less than 1 second.. and reduced its model size from 192 MB to just 20 MB, approximately 90% reduction! For the Fabric User Group Nigeria September Challenge. The business problem was to optimize a slow-loading executive dashboard for Van Arsdel that was causing significant productivity and confidence issues. Leveraging Semantic Link Labs, my core actions were: 📍Streamlined Data Model & Query Steps: I used Power Query to disable unused tables and eliminate unreferenced columns, which was a key factor in reducing memory footprint. 📍Optimized Relationships: I replaced a problematic many-to-many relationship with an efficient one-to-many setup using a bridge table and switched to single-directional filters to improve query performance. 📍Disabled Auto Date/Time: This feature adds hidden, resource-intensive calendar tables. Turning it off immediately made the model leaner. 📍Refactored DAX: I replaced inefficient DAX measures that were forcing multiple table scans with streamlined, standard time intelligence functions like DATEADD, resulting in significant performance gains. Business Impact? The improvements I made directly addressed the business's pain points: ✅Increased Productivity: Executives now save 2-3 hours per week with a fast, responsive dashboard, allowing them to focus on strategic tasks rather than waiting for data to load. ✅Faster Decision-Making: The dashboard is now a reliable tool for quarterly planning, eliminating the delays that were affecting the business. ✅Restored Stakeholder Confidence: The dashboard now loads instantly, ensuring smooth, professional board presentations and reinforcing confidence in the data and the team behind it. For more detail, read repo: https://lnkd.in/dGBc4gCy

Cloud computing infrastructure costs represent a significant portion of expenditure for many tech companies, making it crucial to optimize efficiency to enhance the bottom line. This blog, written by the Data Team from HelloFresh, shares their journey toward optimizing their cloud computing services through a data-driven approach. The journey can be broken down into the following steps: -- Problem Identification: The team noticed a significant cost disparity, with one cluster incurring more than five times the expenses compared to the second-largest cost contributor. This discrepancy raised concerns about cost efficiency. -- In-Depth Analysis: The team delved deeper and pinpointed a specific service in Grafana (an operational dashboard) as the primary culprit. This service required frequent refreshes around the clock to support operational needs. Upon closer inspection, it became apparent that most of these queries were relatively small in size. -- Proposed Resolution: Recognizing the need to strike a balance between reducing warehouse size and minimizing the impact on business operations, the team developed a testing package in Python to simulate real-world scenarios to evaluate the business impact of varying warehouse sizes -- Outcome: Ultimately, insights suggested a clear action: downsizing the warehouse from "medium" to "small." This led to a 30% reduction in costs for the outlier warehouse, with minimal disruption to business operations. Quick Takeaway: In today's business landscape, decision-making often involves trade-offs.  By embracing a data-driven approach, organizations can navigate these trade-offs with greater efficiency and efficacy, ultimately fostering improved business outcomes. #analytics #insights #datadriven #decisionmaking #datascience #infrastructure #optimization https://lnkd.in/gubswv8k

🚀 Before you launch your LLM into production, it’s essential to understand how your inference endpoints perform under load. In this latest blog post, Gary Stafford explores load testing Amazon Web Services (AWS) SageMaker real-time inference endpoints using Locust, an open-source tool for simulating user demand at scale. Discover how model size, instance type, hosting framework, deployment configuration, and inference parameters impact peak requests-per-second (RPS) and latency—key metrics for delivering reliable and performant AI applications. 🔍 Learn how to: • Benchmark your SageMaker endpoints under load • Identify performance bottlenecks before they impact users • Optimize your deployment for scalability and responsiveness Whether you’re deploying new LLM features or scaling existing production workloads, this guide will show you how to optimize the performance of your inference endpoints and make data-driven infrastructure decisions. All open-source code is available on GitHub. #AWS #SageMaker #LLM #LoadTesting #Locust #MachineLearning #AI #PerformanceTesting

Not all shipments are created equal. Some deliveries are urgent, with customers counting the minutes until they arrive (... me with Amazon deliveries). Others have more flexibility. So, how do you decide what gets priority? Well, the final piece of the route planning and optimization puzzle this week are customer requirements and prioritization. It's about balancing what's urgent with what's efficient, ensuring that every shipment gets where it needs to be, exactly when it needs to be there. But here's the catch: prioritization doesn't work in isolation. It only works when paired with data-driven decision-making, dynamic routing, and optimized loads & vehicle utilization. Here's how it all connects: 📦 Customer requirements are the starting point. By understanding delivery windows and shipment urgency, planners can categorize shipments into priorities. Critical shipments can then be routed through the most time efficient paths while less-urgent shipments can balance time and cost. 📦 Data-driven decision making backs these priorities by insights. Real-time data helps planners adapt to disruptions that can impact high priority shipments, while historical data informs strategies to prevent delays in the first place. 📦 Dynamic routing allows flexibility when things don't go as planned. Need to reroute a time-sensitive shipment? No problem! Dynamic plans help keep high-priority deliveries on track without throwing hte rest of the schedule into chaos. 📦 Load optimization & vehicle utilization make sure that every truck is working efficiently, even when prioritization shifts the focus to critical shipments. Grouping similar loads and assigning the right vehicles create the flexibility to adjust without wasting resources. By aligning customer needs with operational strategies, companies can do more than just meet delivery windows. At its core, prioritization is about creating value by aligning customer needs with broader supply chain goals. Think about how meeting delivery windows impacts production, warehousing, inventory, other shipments... Every shipment is another step toward an interconnected, responsive, and resilient supply chain. #supplychain #logistics #transportation #routeplanning