Data-Driven Scaling Methods

Scaling data pipelines is not about bigger servers, it is about smarter architecture. As volume, velocity, and variety grow, pipelines break for the same reasons: full-table processing, tight coupling, poor formats, weak quality checks, and zero observability. This breakdown highlights 8 strategies every data team must master to scale reliably in 2026 and beyond: 1. Make Pipelines Incremental Stop reprocessing everything. A scalable pipeline should only handle new, changed, or affected data - reducing load and speeding up every run. 2. Partition Everything (Smartly) Partitioning is the hidden booster of performance. With the right keys, pipelines scan less, query faster, and stay efficient as datasets grow. 3. Use Parallelism (But Control It) Parallelism increases throughput, but uncontrolled parallelism melts systems. The goal is to run tasks concurrently while respecting limits so the pipeline accelerates instead of collapsing. 4. Decouple With Queues / Streams Direct dependencies kill scalability. Queues and streams isolate failures, smooth out bursts, and allow each pipeline to process at its own pace without blocking others. 5. Design for Retries + Idempotency At scale, failures are normal. Pipelines must retry safely, re-run cleanly, and avoid duplicates - allowing the entire system to self-heal without manual cleanup. 6. Optimize File Formats + Table Layout Bad formats create slow pipelines forever. Using efficient file types and clean table layouts keeps reads and writes fast, even when datasets hit billions of rows. 7. Track Data Quality at Scale More data means more bad data. Automated checks for nulls, duplicates, schemas, and freshness ensure that your outputs stay trustworthy, not just operational. 8. Add Observability (Metrics > Logs) Logs aren't enough at scale. Metrics like latency, throughput, failure rate, freshness, and queue lag help you catch issues before customers or dashboards break. Scaling isn’t something you “buy.” It’s something you design - intentionally, repeatedly, and with guardrails that keep performance stable as data explodes.

🚀 DD-FEM: Train Small, Model Big (Local Training → Global Assembly) What if we could learn physics locally—on small patches—then assemble those learned “data-driven elements” to solve much larger PDE problems without retraining? That’s the idea behind DD-FEM (Data-Driven Finite Element Method): ✅ Locally trained, data-driven basis functions ✅ Finite-element-style local-to-global assembly ✅ Governing equations still enforced (not a black box) Why it matters (results we’re excited about): + >1000× speedup with <1% relative error on lattice-type elasticity—showing globally accurate solutions assembled from small, locally trained components. + 23.7× speedup with <4% error for scaled-up steady Navier–Stokes porous-media flow using DG-style coupling.  + 662× speedup with ~1% error for time-dependent Burgers dynamics, while generalizing across space/time from local training.  + A single learned manifold can represent both Poisson and Burgers, trained on local 2×2 subdomains (4,000 Poisson + 101,000 Burgers snapshots). The big picture: DD-FEM keeps the numerical-method rigor and modularity we trust—while gaining the reuse and scalability we want from data-driven models. 📄 If you’re curious about the framework and results, see our paper “Defining Foundation Models for Computational Science: A Call for Clarity and Rigor.” + Link to the paper: https://lnkd.in/gWSHPAqj #SciML #Numerical #Methods #Finite #Elements #Model #Reduction #Domain #Decomposition #HPC #PDE #Foundation #Models #libROM

As we've seen recently with the release of DeepSeek, there is substantial room for improvement in large scale foundation models, both in terms of architectural efficiency and unsupervised training techniques. While the discussion has been mostly about LLMs, there is also a strong need for improvement to the scalability of generative AI in other domains such as video and multi-sensor world models. In the last several months we have released multiple foundation models for video and multi-sensor generative simulation for the autonomous driving space: VidGen-1 and 2, WorldGen-1 and GenSim-2. These models were developed fully in-house (and not fine-tuned from any open-source models) using only ~100 H100 GPUs (inclusive of all the R&D and final training runs), which is a tiny percentage of the typical compute budgets associated with video foundation model development (thousands to tens of thousands of H100 GPUs). How did we achieve industry leading foundation models with much less compute? We combined DNN architecture innovation with advanced unsupervised learning techniques. By leveraging our Deep Teaching technology and improvements to generative AI DNN architectures, we were able to use smaller parameter/more efficient models and to simultaneously accelerate the unsupervised learning process, leading to superior scaling laws compared to industry-typical methods, which means higher accuracy per compute dollar spent, both during training and inference. We have verified that these scaling law advantages persist at larger scales of compute/data, and look forward to keep pushing the frontier of world models for autonomous driving and robotics by scaling up. In essence, combining Deep Teaching with generative AI architecture innovation, leads to a highly scalable form of generative AI for simulation.

🗄️ “How Would You Scale the Database?” (An interview story most candidates get wrong) I’ve asked this question in dozens of system design interviews. “Our database is becoming slow as traffic grows. How would you scale it?” The most common answer? 👉 “We’ll shard the database.” And that’s usually where the answer goes wrong. ❌ What weak answers sound like Candidates jump straight to: 🔺 Sharding 🔺 Distributed databases 🔺 Complex architectures Without asking: ✅ Why is it slow? ✅ Is it read-heavy or write-heavy? ✅ What queries are actually expensive? This tells me they know tools, but not thinking. ✅ What a strong answer sounds like Strong candidates layer their response—just like this diagram. They say: “Before jumping to sharding, I’d first understand the bottleneck.” Then they walk step by step 👇 🧠 Step 1: Fix the obvious 💠 Indexing 💠 Check query patterns 💠 Add or fix missing indexes 💠 Measure improvement 👉 Many production issues end here. ⚡ Step 2: Reduce database load 💠 Caching 💠 Cache hot reads (Redis) 💠 Avoid hitting DB for repeated queries 👉 This alone can cut DB traffic by 70–80%. 🧱 Step 3: Optimize data shape 💠 Denormalization / Materialized Views 💠 Reduce joins 💠 Precompute expensive queries 👉 Especially useful for dashboards & analytics. 📈 Step 4: Scale reads safely 💠 Replication 💠 Primary for writes 💠 Replicas for reads 👉 Introduces eventual consistency, so trade-offs must be discussed. 🧩 Step 5: Scale vertically (if possible) 💠 Vertical Scaling 💠 Add CPU/RAM 💠 Quick but limited 👉 Good short-term fix. 🔥 Step 6: Shard only when needed 💠 Sharding 💠 Used when data or writes can’t fit on a single node 💠 Requires shard key design 💠 Adds operational complexity 👉 Powerful, but last resort, not first. 🎯 What interviewers are really testing Not whether you know sharding. They’re testing: ✅ Can you reason from symptoms → solutions? ✅ Do you understand trade-offs? ✅ Can you keep systems simple until forced otherwise? 💡 The winning mindset “Great engineers don’t reach for the biggest hammer. They reach for the right tool at the right time.” If you answer database scaling like this, you’re no longer “just a candidate” — you’re thinking like someone who has run systems in production. Ankit Pangasa

42.1% error reduction with 85% less data. At Ento, we use a lot of traditional black-box Machine Learning to model building energy consumption, and they're great for many use cases. But they have their limits. When we're dealing with: - Plenty of indoor sensor data - Limited historical data - The need to actively control a building’s HVAC system ... plain black-box approaches often fall short. That’s why I’ve been following key trends around blending data-driven methods with physical modeling: 🔹 Transfer Learning: Use data from similar buildings to improve models. 🔹 Digital Twins: Blend data-driven methods and physical simulations. 🔹 Physics-Informed AI: Embed physical laws into the learning process to improve results. Just last month, three papers in these fields came out from leading researchers: - GenTL: A universal model, pretrained on 450 building archetypes, achieved a 42.1% average error reduction when fine-tuned with 85% less data. From Fabian Raisch et al. - An Open Digital Twin Platform: Han Li and Tianzhen Hong from LBNL built a modular platform that fuses live sensor data, weather feeds, and physics-based EnergyPlus models. - Physics-informed modeling: A new study proved that Kolmogorov–Arnold Networks (KANs) can rediscover fundamental heat transfer equations. From Xia Chen et al. Which of these 3 trends do you see having the biggest real-world impact in the next 2-3 years?

Scaling Laws for Robotics Our latest research at #waymo marks a distinguished milestone—a rigorous exploration of scaling laws in autonomous driving at unprecedented scale. This work represents the pinnacle of my time at Waymo and fulfills the vision I set out to achieve during my tenure. We've demonstrated that autonomous vehicle (AV) performance scales predictably with increased training compute, model size, and data. Our key findings: 1) Motion forecasting and planning in autonomous vehicles adhere to power-law scaling similar to large language models (LLMs). 2) Optimal model size grows 1.5x faster than dataset size with increasing compute. 3) Improved model performance significantly enhances closed-loop simulation outcomes, crucial for real-world AV safety and reliability. 4) Increasing inference-time compute substantially boosts trajectory prediction accuracy and coverage, optimizing smaller models until a crossover point favoring larger models. 5) Training on observed trajectories of other agents effectively transfers skills to the AV ego-agent, highlighting data efficiency opportunities. Observed driving miles significantly complement human-demonstrated miles in training. These insights not only advance AV technology but also broadly inform data collection strategies, model scalability, and computational efficiency in general robotics and embodied AI. I am deeply grateful to Waymo for their support, enabling us to conduct this principled study and openly share our findings with the global research community. This would not have been possible without the dedication and brilliance of all of my collaborators: Kratarth Goel, Mustafa Baniodeh, Benjamin Sapp, Dragomir Anguelov, Scott E., Ari Seff, Carlos Fuertes, Cole Gulino, Ghassen Jerfel, Chenjie Yang, Dokook Choe, Rui Wang, Vinutha Kallem, Sergio Casas Full details available here - Paper: https://lnkd.in/gWXiGrVW - Blog Post: https://lnkd.in/g6w_9iWF

Design for Scale - 2: The Power of Events in Distributed System Events are fundamental building blocks in modern distributed systems, yet their importance is often underappreciated. To understand their power, we must first distinguish events from commands and queries. Events represent immutable facts - things that have already occurred. In contrast, commands express intentions that may or may not succeed. While this distinction can be subtle, it's crucial for system design. Interestingly, we can also treat commands and queries themselves as event streams in different contexts, representing the historical record of customer interactions with our system. This event-centric thinking unlocks elegant solutions to traditionally complex problems. The most common type of event is Change Data Capture. I worked on a quota enforcement tracking resource usage system for millions of customers. The initial approach using scheduled batch queries placed enormous stress on the database. However, by recognizing that data volume was high but change velocity was relatively low, we pivoted to an event-driven approach: establish baseline counts and track mutations through events. This transformation converted a challenging scaling problem into simple in-memory counting. The durability of events provided built-in reliability - if processing failed, we could replay the event stream. We further optimized by buffering rapid add/delete operations in memory, allowing them to cancel out before writing to the quota system, dramatically reducing write pressure. Events can elegantly address the notorious distributed transaction problem through the Saga pattern. Instead of struggling with complex transaction coordination across heterogeneous datastores, we can listen to committed events from the primary system and reliably propagate changes. This approach transforms a difficult distributed transaction problem into a more manageable event-based synchronization challenge. This pattern isn't new - many database systems internally use similar approaches like write-ahead logs or commit logs for replication and synchronization. Events also provide a powerful foundation for system validation and auditing. Independent systems can cross-check correctness and completeness by consuming the same event streams. This pattern has proven successful even in language models for improving result accuracy. But events encompass more than just data changes. Metrics, application logs, audit trails, and user interactions all represent valuable event streams. This broader perspective enables creative solutions to seemingly intractable problems. Treating events as first-class citizens in distributed system design leads to more scalable, reliable, and maintainable architectures. Whether handling data mutations, system operations, or user interactions, event-driven approaches often simplify complex problems while providing built-in reliability and auditability. Befriend with your events!

This one metric separates thriving businesses from failures. Most entrepreneurs overlook it until it's too late. It’s not hard to create a great product or service. The real challenge is producing it for less than people are willing to pay. This is where businesses thrive or die. At Quest Nutrition, our mission was clear: make a protein bar with the flavor of a candy bar but the protein profile of a protein powder. It was crazy expensive at first. (We joked about having the most costly protein bars on planet Earth.) We knew to scale, we had to drive costs down. Here’s how we did it: Model It Out. Build a detailed business model. Know your costs at different volumes. Break down your costs for ingredients and employees, and align them with your revenue. Scale Smartly. Initial costs will be high. As you grow, buy ingredients in larger quantities to reduce costs. Validate Your Assumptions. If your product needs to be priced higher than what customers are willing to pay, you don’t have a business. Run thought experiments to test this before sinking years and money into it. Stay Objective. Don’t fall in love with your idea. Base your decisions on data. The worst time to realize you can’t be profitable is after launch. Now let’s apply this to hiring. Model It Out: Calculate the cost of hiring help at different levels of your business. Break down the costs of each hire, including salaries, benefits, and overheads. Align these costs with the revenue they are expected to generate. For each volume of business, determine how many employees you can afford and what their impact on revenue will be. Scale Smartly: Hire in phases. Initially, take on more roles yourself or hire part-time help. As your business grows and revenue increases, you can hire more full-time employees. Focus on efficiency before increasing headcount. Validate Your Assumptions: Ensure that hiring additional help will directly contribute to increased revenue or significantly reduce costs. If it doesn’t, rethink your strategy. Run the numbers and see if you can maintain your profit margins with the new hires. Stay Objective: Don’t hire based on gut feeling or desperation. Use data to make hiring decisions. Track the performance and ROI of each new hire. If they aren’t contributing to profitability, reassess their role or your hiring strategy. Key takeaways: → Model your costs meticulously and align them with expected revenue.  → Scale your hiring and production smartly, focusing on efficiency. → Always validate your assumptions with data and thought experiments. → Stay objective and use data to guide your hiring and business decisions.

Adaptive Data Optimization (ADO): A New Algorithm for Dynamic Data Distribution in Machine Learning, Reducing Complexity and Improving Model Accuracy Researchers from Carnegie Mellon University, Stanford University, and Princeton University introduced Adaptive Data Optimization (ADO), a novel method that dynamically adjusts data distributions during training. ADO is an online algorithm that does not require smaller proxy models or additional external data. It uses scaling laws to assess the learning potential of each data domain in real time and adjusts the data mixture accordingly. This makes ADO significantly more scalable and easier to integrate into existing workflows without requiring complex modifications. The research team demonstrated that ADO can achieve comparable or even better performance than prior methods while maintaining computational efficiency. The core of ADO lies in its ability to apply scaling laws to predict how much value a particular dataset or domain will bring to the model as training progresses. These scaling laws estimate the potential improvement in learning from each domain and allow ADO to adjust the data distribution on the fly. Instead of relying on static data policies, ADO refines the data mixture based on real-time feedback from the training model. The system tracks two main metrics: the domain’s learning potential, which shows how much the model can still gain from further optimization in a given domain, and a credit assignment score, which measures the domain’s contribution to reducing the training loss. This dynamic adjustment makes ADO a more efficient tool compared to traditional static data policies... Read the full article here: https://lnkd.in/g9CZ_W-H Paper: https://lnkd.in/gsPN2tkZ GitHub: https://lnkd.in/g79fm-zC YiDing Jiang Allan Zhou Zhili Feng Sadhika Malladi Zico Kolter Machine Learning Department at CMU Stanford University

Startups often begin with a vision, a strong belief in an idea, and a gut feeling about the market. But scaling a startup requires more than intuition—it demands data-driven decisions that guide product development, customer retention, and revenue growth. 1. Finding Product-Market Fit with Data Instead of guessing what customers want, successful startups: ✅ Analyze user behavior—Which features get the most engagement? Where do users drop off? ✅ Use A/B testing—Test different versions of features, landing pages, or pricing models to see what resonates. ✅ Leverage surveys & feedback loops—Direct customer insights can validate assumptions and refine offerings. 2. Boosting Customer Retention with Data Analytics Acquiring new customers is expensive, but retaining them is key to sustainable growth. Data helps startups: 🔹 Segment customers—Identify high-value users and personalize their experiences. 🔹 Predict churn—Spot patterns that indicate when a customer is about to leave and intervene proactively. 🔹 Optimize onboarding—Track friction points in the user journey and improve the first-time experience. 3. Optimizing Revenue and Monetization Strategies Startups must experiment with revenue models to maximize profitability. Data helps by: 📊 Identifying profitable pricing strategies—Analyzing purchase behavior to adjust pricing tiers. 📈 Tracking customer lifetime value (LTV)—Ensuring the cost of acquiring a customer (CAC) is justified. 💡 Experimenting with revenue streams—Using insights to explore upsells, subscriptions, or partnerships. The Bottom Line? Data Wins. Relying solely on intuition can be risky. Combining gut instinct with real-world analytics creates a powerful engine for scalable, smart growth. 𝑾𝒉𝒂𝒕’𝒔 𝒐𝒏𝒆 𝒘𝒂𝒚 𝒚𝒐𝒖𝒓 𝒔𝒕𝒂𝒓𝒕𝒖𝒑 𝒉𝒂𝒔 𝒖𝒔𝒆𝒅 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒎𝒂𝒌𝒆 𝒔𝒎𝒂𝒓𝒕𝒆𝒓 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏𝒔? 𝑫𝒓𝒐𝒑 𝒚𝒐𝒖𝒓 𝒕𝒉𝒐𝒖𝒈𝒉𝒕𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #DataDrivenDecisionMaking #StartupEcosystem #Startups #StartupScaling