Sagar Kshirsagar’s Post

This image is a fantastic analogy for anyone grappling with Data Lakes vs. Data Warehouses! On the left, we have the Data Lake. It's a beautiful, natural, and expansive body of water where everything flows in – raw, unprocessed, and in its native format. You can collect logs, sensor data, social media feeds, and traditional structured data without needing to define its schema upfront. It's fantastic for discovery, machine learning, and when you're not entirely sure what insights you're looking for yet. On the right, the Data Warehouse. This is a meticulously organized factory. Data has been carefully collected, cleaned, transformed, and bottled (or structured) for specific purposes. It's optimized for fast querying, reporting, and delivering consistent business insights. Think of it as the refined product ready for consumption. The key takeaway? They're not mutually exclusive! Often, data flows from the lake (raw exploration) to the warehouse (refined analysis). Understanding when and where to use each is key to a robust data architecture. What are your thoughts on this comparison? #DataArchitecture #DataManagement #DataAnalyst #DataEngineer #CloudData #AnalyticsStrategy

From Partitioning to Liquid Clustering: The Next Leap in Data Optimization Imagine this: You’re running analytics on a 10 TB Delta table. Your queries crawl. Your partitions are uneven. Some have millions of rows, others barely a few hundred. You Z-ORDER once, twice… it helps, but only for a while. Next week, data grows, clusters drift, and your performance tanks again. Sounds familiar? That’s exactly the problem Liquid Clustering was built to solve. So, What Is Liquid Clustering? Think of it as partitioning reimagined as a smarter, more flexible way to organize data in Delta tables. Traditional partitioning: ▪️ Static, predefined columns (date, region) ▪️ Risk of data skew and small file problems Liquid Clustering: ▪️ Dynamic — adapts as data evolves ▪️ Automatically maintains balanced data ranges ▪️ Greatly improves query performance and compaction ▪️ No more hard-coded partitions. No more manual re-clustering. ▪️ Delta tables literally self-optimize as you write new data. Why It Matters: 1️⃣ Adaptive Performance — Queries stay fast even as data volume grows. 2️⃣ Simplified Design — No need to guess partition keys up front. 3️⃣ Smaller Footprint — Reduced file fragmentation and shuffle overhead. 4️⃣ Future-Proof — Works seamlessly with Photon, Unity Catalog, and Delta Lake. Real-World Example At scale, teams use Liquid Clustering to optimize large fact tables (like sales_fact or events_log) that evolve daily. Instead of rebuilding tables or manually clustering weekly, you simply define clustering keys — and Delta handles the rest. ALTER TABLE sales_fact SET TBLPROPERTIES ( 'delta.liquidClusteredColumns' = 'customer_id, region' ); Liquid Clustering is the bridge between traditional partitioning and autonomous data optimization. You don’t just design for today’s data, you design for tomorrow’s growth. Because in data engineering, stability isn’t about being static it’s about staying adaptive. #Databricks #DeltaLake #LiquidClustering #DataEngineering #PerformanceOptimization #Azure #BigData #ModernDataStack #DeltaTables #Analytics #PySpark #DatabricksCommunity

📊 Every enterprise has data. But not every enterprise knows what to do with it. We’ve seen businesses struggle with scattered systems, siloed teams, and decisions made on gut instinct instead of insight. That’s where data engineering services come in not just as a technical fix, but as a strategic transformation. At Prescience Decision Solutions, a Movate company, we help organizations turn raw data into real impact. From building scalable architectures to enabling a data-driven culture, optimizing operations, mitigating risks, and even unlocking new business models, data engineering is changing the game. 💬 This blog dives into 5 powerful ways data engineering is reshaping modern enterprises. If your business is ready to move from data chaos to clarity, this is a must-read - https://lnkd.in/d3ev8gEC #DataEngineering #DigitalTransformation #PrescienceDS #AIEnablement #BusinessIntelligence #EnterpriseGrowth #ETL #Movate

Enabling User-Facing Analytics Directly on the Data Lake 🚀 Data lakes have rapidly evolved into the single source of truth for many organizations as the volume and variety of data continue to explode 📈. Beyond traditional analytics, AI data pipelines for interactive analysis, feature engineering, and model training also depend heavily on the data lake as their foundation. But as more users and systems rely on the data lake directly for insights, one challenge becomes increasingly apparent—delivering high concurrency and low latency for user-facing analytics ⚡. These workloads are no longer limited to batch jobs or scheduled pipelines. They demand real-time or near real-time access with fast response times—a difficult problem when operating directly on massive, cloud-based object stores ☁️. Chinmay Soman and I explored how to address these challenges and enable user-facing analytics directly on the data lake 🔍 . We at StarTree benchmarked our approach on a 1TB dataset to evaluate performance under realistic query loads, and the results were promising 🎯. In our new blog (link in comment), we deep dive into: ✅ The architectural choices behind enabling interactive analytics on the data lake 🏗️ ✅ Key optimizations that make low-latency queries possible at scale ⚙️ ✅ Benchmark results that validate the approach 📊 Would love to hear your thoughts on bringing real-time capabilities to data lake architectures! 💭💡 #DataEngineering #DataLake #ApacheIceberg #RealTimeAnalytics #CloudComputing #BigData

The discussions following my recent post “Do We Still Need Traditional Data Modeling Tools?” have been some of the most insightful I’ve seen in a long time. From experienced practitioners to modern tool builders, the consensus is clear: data modeling is more important than ever — but the way we do it must evolve. Leaders like Serge Gershkovich (SqlDBM) and Johannes Hovi (Ellie.ai) shared great perspectives — showing that the future isn’t about abandoning tools, but about making them open, automated, and collaborative. The visual medium will always matter — but it now needs to be powered by metadata, integration, and AI-assisted automation so that our models stay alive, consistent, and connected to real delivery. Thanks to everyone who contributed — Werner, Robert, Anke, Joerie, Harmen, Guido, Steve, Thierry, Reeves, and others — for making this such a rich conversation. The fact that both vendors and practitioners are now aligned on this evolution is a sign that we’re moving toward a shared goal: 👉 Modeling as a living system — not a static diagram. https://lnkd.in/e_vDkPt2

🚀 𝗧𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗺𝘆𝘁𝗵 𝗮𝗯𝗼𝘂𝘁 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: “𝗠𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 = 𝗯𝗲𝘁𝘁𝗲𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀.” When I started in data engineering, I believed that having 𝘵𝘰𝘯𝘴 of data was the key. But over time, I’ve learned this: 👉 It’s not the 𝘃𝗼𝗹𝘂𝗺𝗲 of data that matters — it’s the 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 and 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆. I’ve seen teams store terabytes of data... ...but when business users ask a simple question, it still takes hours to get an answer. The real challenge? • Duplicated datasets 🌀 • Poorly defined data ownership • Missing documentation • No single source of truth That’s why modern architectures like 𝗠𝗲𝗱𝗮𝗹𝗹𝗶𝗼𝗻 or 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 aren’t just buzzwords — they’re ways to bring 𝘁𝗿𝘂𝘀𝘁, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝘀𝗽𝗲𝗲𝗱 to your data. ✨ My key takeaway: “𝘈 𝘴𝘮𝘢𝘭𝘭𝘦𝘳, 𝘸𝘦𝘭𝘭-𝘮𝘰𝘥𝘦𝘭𝘦𝘥 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 𝘤𝘢𝘯 𝘥𝘳𝘪𝘷𝘦 10𝘹 𝘮𝘰𝘳𝘦 𝘷𝘢𝘭𝘶𝘦 𝘵𝘩𝘢𝘯 𝘢 𝘮𝘢𝘴𝘴𝘪𝘷𝘦, 𝘮𝘦𝘴𝘴𝘺 𝘰𝘯𝘦.” What’s your take — do you think organizations over-focus on 𝘃𝗼𝗹𝘂𝗺𝗲 instead of 𝘃𝗮𝗹𝘂𝗲? #DataEngineering #Databricks #DataQuality #DataMesh #MedallionArchitecture

Data Strategy: Data Mesh vs. Data Fabric "Data Mesh" and "Data Fabric" are two of the most discussed concepts in data architecture today. They're often confused, but they solve fundamentally different problems. 1. Data Mesh -What it is: A strategy for decentralizing data ownership and management. -Core Principle: It treats "data as a product." Instead of a central data team handling all requests (a bottleneck), individual business domains (e.g., Sales, Marketing, Finance) are given the tools and responsibility to own, build, and serve their own data products. -It solves: Organizational bottlenecks and scalability issues. 2. Data Fabric -What it is: An architectural layer that unifies disparate data sources. -Core Principle: It uses technology like AI, active metadata, and a knowledge graph to create an intelligent, integrated, and unified view of all data, regardless of where it's stored. -It solves: Technical data silos and integration complexity. #DataEngineering #DataArchitecture #DataGovernance

Somewhere along the way, we started confusing complexity with capability.  I’ve seen data platforms so over-engineered that even who built them can’t explain how they work anymore. Everything looks impressive, but nothing moves the business forward. Real capability isn’t how many layers or tools you have. It’s how fast you can go from data → decision → action. Simplicity is a competitive advantage. It keeps systems scalable, people aligned, and decisions fast. Every extra component you add has a cost, not just in money, but in clarity. So before adding one more tool, ask: “Is this making us smarter, or just busier?” In my experience, the strongest architectures aren’t the most complex. They’re the ones everyone can understand and use. What’s one thing in your data or tech stack you could remove today, without losing real business impact? Follow Anouar Znagui Hassani MSc for more insights, If you find it useful Repost. #DataStrategy #BusinessSimplicity #DecisionIntelligence #DataDriven #DigitalTransformation #BusinessExecution #AIForBusiness #DataArchitecture #ScalableSystems #TechLeadership #BusinessImpact #DataClarity #SmartDecisions

From Raw to Refined: The Journey of a Dataset Every dataset tells a story — but only if it’s cleaned, transformed, and contextualized. Our engineering teams specialize in: Ingesting structured and unstructured data Cleansing and validation Building semantic layers for BI Whether it’s retail transactions or clinical data, we build pipelines that respect the data and enhance its value. #ETL #DataTransformation #Databricks #SQLServer #DataLineage

74% of execs say outdated or disconnected data is holding them back. Let’s be honest, it’s worse than that. Disconnected data isn’t just a delay. It’s a drag on everything: forecasting, resourcing, uptime, and ultimately, trust. In logistics and manufacturing, the margin for error is razor-thin. And yet, most operations are still held together by a patchwork of legacy systems, siloed reporting, and paper-based processes. That’s not digital transformation. That’s really more like digital denial. At Dexory, we’ve seen this play out firsthand: 👉 Warehouses with thousands of SKUs, dozens of systems, and zero live context. 👉 Teams are firefighting because they’re working with data that’s either stale or scattered. And when that’s your operating reality, you don’t get to be proactive. You react. Slowly. Inefficiently ... in the dark. This is why we built DexoryView as a real-time operations layer. Not some random dashboard. A system that collects, connects and feeds high-density, structured data into a digital twin that evolves as your warehouse changes, not months, weeks or days later. The goal here is to have the right insight at the right time. Seamlessly flowing into systems designed to act, not be admired. But to get there, you can’t pile on yet ANOTHER visualisation tool and hope for the best. You’ve got to overhaul the infrastructure that your decisions depend on and make it work for you. Until then, the real cost isn’t the tech spend. It’s the compounding drag of decisions made days or weeks too late.