Promethium’s Post

In today’s complex, hybrid data environments, trust in data is everything. Too often, organizations struggle with fragmented metadata, siloed systems, and a lack of trust in their data. That’s why automated data lineage is no longer a “nice to have” — it’s the foundation for governance, compliance, and AI success. The new Cloudera Octopai integrations show how powerful it can be to map data end-to-end across 60+ systems, down to the column level. The result? Clarity, trust, and speed in decision-making. If your teams are navigating complex hybrid environments, this post by Varun Jaitly is worth a read 👉 https://lnkd.in/dq_Riy9g #DataLineage #DataGovernance #AI #Cloudera #Octopai #MetadataManagement

Why #Data Infrastructure Must Evolve for Practical AI #AI isn’t failing because of the models—it’s failing because of the data foundation. Too many enterprises are discovering that: • Traditional data pipelines are passively killing AI ROI. They’re rigid, over-engineered, and designed for static reporting, not dynamic, AI-driven insights. • AI success requires real-time, adaptive, and governed data flows—not brittle batch processes. • Distributed Semantic Intelligence—where meaning, lineage, and governance are built into the data fabric—is the key to making AI practical and scalable. That’s why data infrastructure must evolve. We need to move beyond yesterday’s pipelines into architectures that are: ✔ Business-first – aligned with outcomes, not just technical output ✔ Pattern-driven – reusable, consistent, and adaptable across domains ✔ Semantically intelligent – embedding context, governance, and discoverability Over my career as a Data Architect & Modeler (25+ years), I’ve led this evolution: • Built scalable data ecosystems with Snowflake, Databricks, and Data Vault 2.0 • Implemented governance and semantic frameworks using Collibra, Unity Catalog, and UDMs • Modernized legacy pipelines into cloud-native, AI-ready architectures across finance, healthcare, and tech • Championed Customer360, advanced analytics, and AI-driven decision-making at the enterprise level From IBM to Microsoft, Accenture, EY, Northern Trust, and beyond—I’ve helped organizations unlock true AI value by transforming their data architecture into a strategic business enabler. 👉 If your organization is serious about maximizing AI ROI through smarter data architecture, let’s connect. I’m ready to help bridge the gap between data strategy and AI execution. #AI #DataArchitecture #DataModeling #SemanticIntelligence #DataROI #Cloud #Governance #opentowork https://lnkd.in/gEYFGxMv

Our client, Promethium CEO Prat Moghe, shares in DATAVERSITY how to approach a new #dataarchitecture for #AI at Scale and highlights the issues with the current architecture mismatch. "To prepare for AI at scale, organizations need an architecture that's more than just models or co-pilots. A strong #datafabric foundation requires real-time access to all data sources, dynamic business context, and collaborative self-service." https://lnkd.in/es8Daz74

Prat Moghe from our client Promethium explains in DATAVERSITY why to prepare for #AI at scale, organizations need an architecture that is more than just #models or #copilots. "A strong #datafabric foundation requires real-time access across all #data sources, dynamic business context that can ground models to ensure accuracy, and trust and collaborative self-service that delivers insights at high velocity." https://lnkd.in/eDKPYD4V

Something that I’ve put a lot of thought into, and have seen as a trend lately in mfg. circles is around data standardization. While I love the increased focus on data architecture, I think the hyperfocus on standardization as the ultimate goal is the wrong answer. Don't get me wrong data standardization is appropriate and is crucial in some areas, but it is often pitched as "The Solution" to increase interoperability, but I’m not so sure. I think of interoperability in three layers, and I think as an industry we often get stuck focusing on the first two: - Technical Interoperability: Can System A's data be read by System B? This covers the basic plumbing, APIs, connectors, schema, etc. It's a foundational step. - Semantic Interoperability: Do we use the same vocabulary? Do we both call a "Work Order" a "Work Order"? Common data models are great at this. They give us a shared dictionary. But here's the trap and where standard data models that are trying to address multiple domains break down. The third layer is where the real value, and the real difficulty lies imho: - Conceptual Interoperability: When your system says "Work Order" and my system says "Work Order," are we actually talking about the same underlying concept? Does it have the same lifecycle, processes, and relationships? This is the "physical reality" of how a business operates. When I worked as an Enterprise Architect at Northrop, I saw this firsthand trying to align dozens of factories. Each factory had its own culture and operational nuances, and getting them to agree on a single conceptual reality for their data was not realistic or appropriate, oftentimes that nuance in the domain has real business value, and you don’t want to flatten it. This is where generic, top down data models can break down. In an effort to apply to everyone, they become bloated. They try to model every possible variation and end up not being a perfect fit for anyone. We spend more time trying to cram our real world processes to fit a standardized model than we do creating value. In a Future with AI and Agents, models that represent reality is a req. An agent operating on a generic model that doesn't reflect the deep conceptual physical reality of your domain is going to deliver suboptimal results. Knowledge Models are going to be what sets companies apart with the adoption of AI/Agents. Don't get me wrong, I'm not against the idea of standardization, and we shouldn't outright reject it, but rather we must apply standardization intelligently. We should standardize at the interface, treating data from each domain as a well defined 'data product.' This gives us the best of both worlds: enterprise level governance for shared concepts, with the freedom for domains to maintain the accurate/relevant conceptual models that reflect their unique reality. That flexibility requires a different way of thinking, but are the ideals of modern data architecture. Textbooks on Data Architecture in the comments.

For AI to succeed, organizations must fundamentally reimagine data architectures. Learn more about architecture requirements for building agentic AI via Mohan Varthakavi #Couchbase

For AI to succeed, organizations must fundamentally reimagine data architectures. Learn more about architecture requirements for building agentic AI via Mohan Varthakavi #Couchbase

🚀 The Future of Data Engineering = Real-Time + AI-Driven Pipelines Most companies still run batch-heavy pipelines. But the shift is clear: 🔄 Real-time streaming + 🤖 AI integration are becoming the new standard. Here’s why this matters for Data Engineers: 💡 1. Streaming is no longer optional Businesses need instant insights (fraud detection, recommendations, IoT). Tools like Apache Kafka, Spark Structured Streaming, Flink are now mainstream. 💡 2. AI inside the pipeline Data pipelines aren’t just moving data anymore → they’re powering LLMs, vector search, and predictive models. Example: pushing embeddings to a vector database directly from ETL. 💡 3. Cost + Performance Balance Cloud-native engines like Databricks Photon, Snowflake’s Query Optimizer are rewriting what “efficient pipelines” mean. Smart partitioning, caching, and auto-scaling = fewer $$ spent, more insights delivered. 💡 4. Skills that stand out in 2025 ✅ Strong SQL (execution order, window functions, optimizations) ✅ Streaming-first mindset (Kafka, Delta Live Tables, Flink) ✅ Cloud + Cost Optimization skills ✅ GenAI + Vector DB integration know-how For those curious to dive deeper: https://lnkd.in/dbkppgha Why this matters: 👉 AI is no longer optional—it’s embedded in pipelines to automate scaling, governance, and quality control. 👉 Photon represents the next-gen execution layer, bridging traditional Spark APIs with ultra-efficient C++ performance. 👉 Together, streaming + AI = fewer delays, lower costs, and developer productivity you can measure. 👉 Data Engineering is no longer just pipelines. It’s about building scalable, intelligent, real-time data products. 🔥 Takeaway: If you’re a Data Engineer, the next 2–3 years will redefine your role. Stay ahead by blending streaming + AI + optimization skills. #DataEngineering #BigData #Streaming #Databricks #Kafka #AI #GenerativeAI #SQL #CloudComputing #CareerGrowth

Data Engineering is the backbone of modern data and AI. Here are 20 foundational terms every professional should know Part 1: 1️⃣ Data Pipeline: Automates data flow from sources to destinations like warehouses 2️⃣ ETL: Extract, clean, and load data for analysis 3️⃣ Data Lake: Stores raw, unstructured data at scale 4️⃣ Data Warehouse: Optimized for structured data and BI 5️⃣ Data Governance: Ensures data accuracy, security, and compliance 6️⃣ Data Quality: Accuracy, consistency, and reliability of data 7️⃣ Data Cleansing: Fixes errors for trustworthy datasets 8️⃣ Data Modeling: Organizes data into structured formats 9️⃣ Data Integration: Combines data from multiple sources 🔟 Data Orchestration: Automates workflows across pipelines 1️⃣1️⃣ Data Transformation: Prepares data for analysis or integration 1️⃣2️⃣ Real-Time Processing: Analyzes data as it’s generated 1️⃣3️⃣ Batch Processing: Processes data in scheduled chunks 1️⃣4️⃣ Cloud Data Platform: Scalable data storage and analytics in the cloud 1️⃣5️⃣ Data Sharding: Splits databases for better performance 1️⃣6️⃣ Data Partitioning: Divides datasets for parallel processing 1️⃣7️⃣ Data Source: Origin of raw data (APIs, files, etc.) 1️⃣8️⃣ Data Schema: Blueprint for database structure 1️⃣9️⃣ DWA: Automates warehouse creation and management 2️⃣0️⃣ Metadata: Context about data (e.g., types, relationships) Which of these terms do you use most often? Let me know in the comments! Join The Ravit Show Newsletter — https://lnkd.in/dCpqgbSN #data #ai #dataengineering #theravitshow

Every Leader Needs Data Engineering Literacy. Data Engineering is often the “invisible” part of Data & AI projects… until timelines and estimations are challenged. When leaders ask: “Why does it take so long?”, this post is a great indicator/reminder. Because before data becomes actionable, it must first become simply usable. Collecting raw data, ensuring governance, building pipelines, cleansing, modeling… these are not “extras,” they are the foundation. I’ve seen too many projects underestimated because the effort behind making data reliable was overlooked. Understanding these core concepts helps set the right expectations and build trust between C-level, business & data teams. Leaders: if your next project requires building from scratch, take a moment to read this. It will help you better evaluate estimations and see the value in the process. Thank you Ravit Jain for the great document. #DataEngineering #AI #DataStrategy #Leadership #BusinessImpact