Data Evolution: From ETL to AI-Driven Pipelines

Day 03 : Data!! 📊 AI is only as good as the data it learns from. Real world data reflects the underlying processes that generated it. In practice data may be inconsistent as it moves across subsystems and processes, may be incomplete, and in the age of big data may in unstructured (scanned images/PDF/Audio files). In addition, real world data is fragmented across data silos. To unlock value from incomplete, inconsistent and fragmented data investment in foundational data practices is critical. 🔹 1. Data Governance: Setting the Rules of the Game by defining Ownership and decisions rights, Standards to drive consistency, and Permissible use cases. 👉 Strong Data Governance builds trust, transparency and forms ethical baseline for AI applications 🔹 2. Data Curation: The Art and Craft of moving from Raw to Refined data which involves Cleaning (pre and post processing), tagging/enrichment (adding metadata) so data is searchable and contextual, historical alignment. 👉 Curated data is what turns datasets into decision assets 🔹 3. Automated Data Pipelines: Horizontal and vertical scaling flows moving from Manual ETL (Extract-Transform-Load) to Automated operations, real-time ingestion and data streams. Automated anomaly detection, validation and monitoring. 👉 Automated pipelines allow data and ideas from POC to industrial grade solutions #AI #Finance #DataEngineering #DataGovernance #Analytics #Automation #ScalingAI

After spending over a decade in the data industry, consulting with C-level executives and being part of multiple architecture boards across banks, telcos, and global enterprises, I’ve noticed something fascinating — and slightly frustrating. No matter how far we’ve come — - From data lakes to lakehouses - From BI dashboards to Gen AI copilots - From ETL pipelines to Agentic AI and RAG …the underlying problems haven’t really changed. We’re still fighting the same battles around data quality, trust, and alignment between business and tech. Even as we talk about Cognitive Agents, A2A orchestration, and self-healing data pipelines, the truth is: …All of it collapses if your data isn’t reliable. So why do these issues keep resurfacing — even after 10+ years of “modernization”? 1. Organizational Incentives Are Misaligned: Most data programs are measured by delivery, not trust. Engineering teams are rewarded for speed, not accuracy. Business teams care about outcomes, not lineage. The result? Quality becomes everyone’s responsibility — and no one’s priority. 2. Tooling Evolves Faster Than Culture: We keep reinventing the stack — Databricks today, Snowflake tomorrow, Agentic AI next year — but the mindset around ownership, validation, and accountability hasn’t evolved at the same pace. Tech can’t fix what people and process don’t reinforce. 3. Context Gets Lost in Translation: Data moves faster than understanding. Every handoff — from source systems to pipelines to dashboards — strips away business context. By the time the AI agent or model consumes it, it’s technically perfect but semantically meaningless. ⸻ My takeaway: Before building the next “AI-powered data assistant,” maybe we need a data assistant that can explain our data quality issues back to us — in plain English. Because after a decade of shiny tools and buzzwords, data quality remains the quiet bottleneck behind every AI promise. ⸻ Curious — what’s the one recurring data challenge you’ve seen that just won’t go away?

💧 Data Lake: The Foundation of Modern Data A centralized repository that stores all types of data—structured, semi-structured, and unstructured—for analytics and AI. In today’s digital world, data is the new gold—but only if you know how to store, manage, and use it efficiently. Traditional databases struggle when data grows too large, unstructured, or complex. That’s where Data Lakes step in: 💾 Store Everything → Structured, semi-structured, and unstructured data in one place ⚡ Process Anything → Run analytics, AI, or machine learning directly on the raw data 🔗 Flexible & Scalable → Grow effortlessly as your business and data needs expand Why it’s game-changing: A Data Lake is not just storage—it’s the starting point for insights, intelligence, and innovation. Companies that leverage Data Lakes can unlock hidden patterns, accelerate decision-making, and stay ahead in a data-driven world. Pro Tip for Beginners: Understanding Data Lakes now gives you a huge edge in data engineering, AI, and analytics careers. #DataLake #BigData #DataEngineering #Analytics #AIandBigData #CloudComputing #DataScience #TechTrends #FutureOfData #LearningSeries #CareerGrowth #DataArchitecture

🌍 Building Scalable Data Foundations for the AI Era 💡 TL;DR The foundation of AI success isn’t just algorithms—it’s scalable, reliable, and well-governed data infrastructure. 📌 What I’ve Learned 🔹 Start with Architecture, Not Models AI projects fail most often because the underlying data layer can’t scale or adapt fast enough. 🔹 Observability = Trust Strong data observability ensures that every model is trained and validated on reliable, up-to-date information. 🔹 Governance That Enables Innovation A well-structured data governance framework doesn’t slow teams down—it accelerates safe, compliant experimentation. ⚙️ Practical Focus Areas • Modular data pipelines designed for rapid AI/ML adoption • Monitoring data drift and schema changes in real time • Metadata-driven lineage for full transparency and reproducibility 📈 Why It Matters As AI becomes embedded across business processes, the true leverage point is no longer model tuning—it’s the reliability, transparency, and scalability of your data foundation. 🧠 Takeaway Future-ready Data Engineers bridge the gap between infrastructure and intelligence. Build once, scale everywhere. #DataEngineering #AIInfrastructure #DataReliability #DataGovernance #DataOps #Analytics

Why 'Fixing' Data with AI is Not a Substitute for Data Architecture and Governance I recently had an amazing discussion with some true experts that sent me down a rabbit hole. The comment that keeps waking me up at night? The position that AI eliminates the need for data interoperability standards. It sounds appealing—the ultimate technological shortcut—but at its core, it is fundamentally flawed. As leaders in P-20W data, we need to move past the hype and truly understand the cost of this "simplification." AI is a powerful accelerator, but we must recognize that its speed can be a Trojan horse for fragility if not constrained by robust, systemic data architecture. The Core Flaws: Oversimplification and Tunnel Vision When proponents argue that AI removes "data barriers," they are often targeting complexities that are, in fact, critical distinctions that ensure equity and quality. 1. The Risk of Oversimplification: AI, in the pursuit of efficiency, can commit feature reduction, eliminating nuanced variables it deems inefficient. Think about the difference between chronic tardiness and excused absences. If an AI model simplifies this for processing speed, it removes the very signal needed for timely, equitable support. The decision process gets easier for the machine, but the outcome for the student becomes less targeted and less impactful. 2. The Risk of Tunnel Vision: AI, whether integrating data or generating code, focuses on a local objective. It can map one field brilliantly but lacks the necessary systemic coherence—the "big picture" view of the entire organization's data contract. A strong data standard is the architectural blueprint: it forces the machine to account for the downstream impact of a change in System A on reporting, transcripts, and predictive modeling systems B and C. AI operating outside of this contract creates an untraceable accountability gap. Standards are the Guardrails for Trust Data standards and strong governance are not obstacles to innovation; they are the essential guardrails that allow for responsible, large-scale AI adoption. They force the machine to honor the integrity of the data ecosystem. We must insist on a standards-first, AI-assisted framework. I'd love to hear your thoughts. What vital data nuance have you seen AI attempt to eliminate in favor of simplicity? #EducationData #DataGovernance #AIinEducation #P20W #SystemIntegrity

🚨 Data Governance & Data Quality are NOT optional 🚨 Every decision maker and data expert needs to know this truth: If your organization is aiming for AI, Data Science, or even reliable Business Intelligence you need to know this: The journey doesn’t start with fancy algorithms or dashboards… it starts with the foundations. Decision makers and data expert must sponsor and support these critical practices from the top down: 👇🏻 ✅ Build Your Core Data Architecture: The backbone of scalable analytics. ✅ Governance First: Define data owners and data stewards to ensure accountability and transparency. ✅ Quality Control: Implement clear processes, frameworks, and source-system changes to keep your data clean and trustworthy. ✅ Good History Matters: Accurate historical data is the backbone for predictive analytics and ML models. Garbage in = Garbage out. ✅ Problem First, AI Second: Don’t chase AI for the hype. Start with clear business problems that AI can solve—never the other way around. 🎯 Without these fundamentals, AI and Analytics become just buzzwords. But with them, you unlock real business value, accuracy, and innovation. 👉 The message is simple: No Foundation, No Data Governance & No Data Quality = No Sustainable AI or Data practices #DataGovernance #DataQuality #AI #DataScience #BusinessIntelligence #DataFoundation #Leadership

From raw data to real-time predictions, this is the seemingly forgotten truth behind the machine learning model successfully launched in production… The Machine Learning Lifecycle represents a continuous feedback driven ecosystem where every stage fuels the next. Each phase, from data collection to model monitoring, forms a loop of constant improvement. This ensures that models perform well at launch and continue to learn and adapt as new data flows in. Here’s how the architecture works. Data scientists, ML engineers, and AI engineers will find themselves spending time more or less within the different stages listed👇: 1.🔹Process Data: The journey begins with data collection and preprocessing. Data is cleaned, transformed, and engineered into features that become the foundation of every model. 2.🔹Develop Model: With prepared data in place, models are trained, tuned, and evaluated for accuracy and efficiency before being registered for deployment. 3.🔹Store Features: Features are stored in Online and Offline Feature Stores to enable consistent access for real time and batch inference. This ensures reliable data availability for both deployment and retraining. 4.🔹Deploy: Models are deployed through automated pipelines and integrated into production environments where they power intelligent applications and perform inference in real time. 5.🔹Monitor: Continuous monitoring tracks performance, detects drift, and triggers retraining workflows when accuracy drops. 6.🔹Feedback Loops: Performance and Active Learning feedback loops keep models updated with new insights and data, ensuring continuous evolution. 💡 In essence: A strong ML lifecycle should be cyclical. Data fuels models. Models power applications. Applications generate new data and the loop continues. 🧠 Building such an architecture enables scalability, adaptability, and governance across the entire machine learning ecosystem, but it doesn’t come without challenges. What obstacles have you encountered in your patch? How have surmounted them? #MachineLearning

𝐄𝐓𝐋 𝐯𝐬. 𝐄𝐋𝐓 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: 𝐖𝐡𝐚𝐭'𝐬 𝐭𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞? Think of data pipelines like working with lemons: 𝐄𝐓𝐋 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭 → 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 → 𝐋𝐨𝐚𝐝): - Extract: Pick lemons from the tree - Transform: Juice the lemons (process the data) - Load: Bottle and store the juice ➡ Transformation happens before storage. ➡ Ideal for structured, regulated environments like finance, healthcare, and traditional ML models. 𝐄𝐋𝐓 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭 → 𝐋𝐨𝐚𝐝 → 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦): - Extract: Pick lemons - Load: Store whole lemons in a cart (raw data into warehouse) - Transform: Juice them later as needed ➡ Transformation happens after loading. ➡ Best for modern use cases like LLMs, GenAl, real-time analytics, and cloud-scale systems. In reality, most teams today use a hybrid approach — some data is juiced right away, while other data is stored raw and processed only when required. From my own experience, the art lies in choosing the right recipe at the right time rather than sticking to one approach. In your projects, do you lean more towards ETL, ELT, or a hybrid? Share in the comments :) I’ll be sharing more practical resources on Data Science, AI/ML, Gen-AI & LLMs — so stay tuned! 📣 Join my Data & AI Community → https://lnkd.in/gb_NjbRV ♻️ Repost to help your network learn about AI ➕ Follow me Piku Maity for daily AI content Graphic Credit - Alex Wang #Data #DataProcessing #DataPipelines #DataEngineering #ETL #ELT #AI

GenAI Is Only as Smart as Your Data Contracts 🤖 Everyone wants GenAI, but few have the data discipline to make it useful. I’ve seen teams deploy LLMs on top of BI systems without realizing — if your data contracts are weak, your model inherits your chaos. Here’s how we started preparing our analytics environment for GenAI: 🧠 5 Practical Ground Rules 1️⃣ Schema stability first — version your tables and views; don’t surprise your model with renamed columns. 2️⃣ Document semantics — make every metric human-readable and machine-readable. 3️⃣ Govern access — classify what’s safe to expose to model prompts. 4️⃣ Track drift — watch for data that changes meaning (e.g., “customer_active_flag”). 5️⃣ Retrain lightly, not blindly — prioritize stable domains over hype. 💡 AI doesn’t fix broken data governance — it exposes it. Question: What’s one data readiness step you’ve taken before testing LLMs or AI tools in your analytics stack? #GenAI #AI #Analytics #DataOps #MLOps #Snowflake #PowerBI @OpenAI @Microsoft @SnowflakeInc

💡 Data Platforms Are Becoming Operating Systems for AI For years, data platforms were built for one purpose: analytics. We designed them to answer questions. Now, we’re designing them to make decisions. The game has changed. Here’s what’s happening: We’re quietly evolving from pipelines and dashboards → to feature stores, model registries, and real-time feedback loops. The modern data platform is no longer a passive data warehouse — it’s the nervous system of an AI organization. It doesn’t just serve data anymore. It coordinates intelligence. The new “AI Operating System” stack looks something like this: Data Layer: Streaming + batch + unstructured Knowledge Layer: Vector stores, semantic catalogs, embeddings Model Layer: Training, registry, and feature management Application Layer: AI agents, copilots, and decision automation These layers work together like an OS kernel — where LLMs are the “user interface” that humans interact with. 🚀 What this means for data engineers: Your pipelines are no longer just feeding dashboards. They’re feeding agents that act, reason, and learn. Data platforms aren’t just infrastructure anymore — they’re becoming intelligence platforms. “Data Platform as AI OS” is the next big architecture shift! #DataEngineering #AI #DataPlatforms #MachineLearning #DataStrategy #AIInfrastructure #MLOps #DataArchitecture #Analytics #GenerativeAI