Obfusware AG masks 1 billion rows in 5 minutes with AWS Glue

Looking to get started with OpenMetadata? This helpful step by step guide walks you through it and leverages NetApp Instaclustr for managed OpenSearch and PostgreSQL to support the OpenMetadata solution. https://lnkd.in/eDzHXwxC #managedservices #datadiscovery #governance #AI

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Doug Flora explains how the EDB Postgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://lnkd.in/eqXCTwTG #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

Gartner warns that by 2027, 60% of organizations will fail to realize AI value due to data governance gaps. In The Register, EDB’s VP of Product Marketing Douglas Flora explains how the EDB Postgres AI Factory helps enterprises cross the agentic AI chasm with a sovereign platform that unifies data, secures it end-to-end, and accelerates production by as much as 3x faster with 6x better cost efficiency. Read Doug’s full piece here: https://lnkd.in/ewcNBncD #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory

It offers an AI agent to solve a problem that data analytics products have struggled with for decades: The people who know how to run the big data infrastructure are not the ones who actually need to use the data. https://lnkd.in/dVkcFNu7

Game-changer in big data? Arun Murthy, co-founder of Hadoop and former Scale AI CTO, just launched Isotopes—an advanced AI analytics agent aiming to tackle big data’s toughest challenges. Could this be the breakthrough enterprises need for unlocking deeper insights? Read more via TechCrunch: techcrunch #AI #BigData #Analytics https://cstu.io/ce2407 #OnlinePresence #TargetAudience #WebsiteTraffic #Conversions #BrandElevation #SudoXResults #DigitalMarketing #CostEffectiveStrategy #CommunityEngagement #CustomerFeedback #BusinessGrowth #BrandAwareness #BusinessMarketing #MarketingInsights #AdvertisingGoals

Isotopes AI, founded by former Hadoop and Scale AI CTO Arun Murthy, launches Aidnn—an LLM-powered analytics agent that queries, cleans, reasons, and drafts reports from enterprise data sources. 📌 Full story: https://lnkd.in/diQ2Ax6z Source: TechCrunch

🚀 Snowflake 2025 is here — and it’s changing how we think about Data + AI. From Cortex AISQL to Adaptive Compute and Snowflake Intelligence, the new features aren’t just upgrades — they’re redefining how teams can: ✅ Turn plain language into powerful insights ✅ Automate governance & observability for AI apps ✅ Scale compute smarter and faster with Gen-2 warehouses ✅ Bridge Postgres + Snowflake for unified workloads In my latest blog, I’ve shared real-world use cases and practical examples of how these features can be applied today. 👉 Read here: https://lnkd.in/gitgzKXA #Snowflake #TigerAnalytics #Snowflake2025 #AI #GenerativeAI #DataAI #DataScience #MachineLearning #DataAnalytics #DataEngineering #CloudComputing #DigitalTransformation #Innovation

⬅️🚀 100 Days of Shift Left - Day 8.1 We conclude our first chapter by highlighting two final market movements that underscore the need for data contracts today. Specifically: 1. Data’s Rise over ML in Creating a Competitive Advantage 2. The Rise of Shift Left Data Practices Before the world of LLMs consumed everything, the idea of having a machine learning model in production was reserved for companies with a) the ability to deploy an immense amount of capital into R&D, and b) retain specialized talent-- especially true for deep learning, which powers most LLM models. But this "business calculus" was completely upended with the release of ChatGPT to the public in November 2022. Shortly after, the once-difficult work of having an operational and useful machine learning model in production was reduced to API calls. In other words, the bottleneck that prevented data products from being financially feasible for all organizations was greatly reduced. The last major shift we saw at this magnitude was the introduction of cloud computing. Thus, technology as a moat is evaporating in many areas, and data is quickly supplanting itself as a business's key differentiator. With the proliferation of data products, the already challenging process of managing data consistently and reliably has become significantly more difficult. Yet, data isn't the first industry in tech to experience a similar pattern. IT operations teams felt a similar strain with the move towards Agile. Security teams also faced this challenge with the rise of attack vectors and surfaces as the internet grew. This pattern is a federation. It's neither good nor bad. It's simply a change in the market's incentives and constraints that requires the business to adapt. Both DevOps and DevSecOps handled this via "Shift Left" practices, empowering upstream developers to manage their domain-specific constraints at the source by making it trivial to do such work via automation. The data industry today is having its "Shift Left" moment. --- 👋🏽 Hi, I'm Mark, one of the co-authors for our upcoming book, Data Contracts-- I'm happy to welcome you to my mini book club! Want to read along? Then get your free download of the early release of the book by clicking the "view my blog" link in my header! Chapter 1: Why the industry now needs data contracts Section: Data-Centric AI and the Rise of Shift Left Data Practices Subsections: Data’s Rise over ML in Creating a Competitive Advantage The Rise of Shift Left Data Practices --- P.S. Videos will be back tomorrow (technical difficulties), but I still wanted to make a post for consistency!

🌊 My Learnings on Data Lakes – Goldmine or Swamp? As I continue my Big Data learning journey, one concept that really stood out to me is data lakes. At first glance, a data lake sounds simple—just dump all your data in one place and you’re done. But as I dug deeper, I realized it’s not that straightforward. A data lake can either be a goldmine of insights or a swamp of unusable data, depending on how it’s designed and managed. What makes a well-designed data lake powerful? It can store all types of data—structured (tables), semi-structured (JSON, logs), and unstructured (images, videos). It supports both batch ingestion (e.g., daily sales reports) and real-time streaming (e.g., IoT sensor data). It scales seamlessly to support analytics, data science, and machine learning workloads. It gives flexibility to data scientists and analysts to experiment and innovate without waiting weeks for IT to provision data. In short, a well-structured data lake can become the foundation of enterprise-wide analytics and AI. ❌ What turns a data lake into a data swamp? No governance → anyone can dump anything, leading to confusion. Lack of metadata/cataloging → nobody knows what data exists or how to use it. Poor partitioning → queries become slow and expensive. Weak security → sensitive data is exposed or misused. The result? A messy, untrustworthy system that slows down rather than speeds up decision-making. Key considerations I learned before designing a data lake: Nature & origin of data – Are we storing logs, transactions, IoT, images, or all of the above? Ingestion frequency – Do we need batch pipelines, real-time streaming, or both? Governance & security – Who has access? How do we enforce encryption and compliance? Metadata & cataloging – Can users discover, understand, and trust the data? Partitioning & optimization – How can we improve query speed and keep storage costs under control? These questions shape the architecture and ensure the lake remains useful as it grows. My Key Learning:- A data lake is not just “storage.” It’s a launchpad for insights, ML, and AI—but only if it’s built with governance, scalability, and discoverability in mind. ⚡The Analogy That Stuck With Me Think of your data lake as your wardrobe: If you organize by type, color, and season → you’ll find what you need instantly. If you dump everything in → you’ll spend forever searching and probably give up. Have you ever worked with a data swamp? What was the hardest part of fixing it—governance, metadata, or user adoption? #BigData #DataAnalytics #ArtificialIntelligence #MachineLearning #BigData #DataEngineering #DataLake #DataGovernance #MachineLearning #Analytics #CloudComputing #LearningInPublic

Scale AI’s former CTO launches AI agent that could solve big data’s biggest problem: Isotopes, co-founded by Arun Murthy, launched a sophisticated analytics agent. Arun was one of the creators of Hadoop who later joined Scale AI. https://lnkd.in/drgMMND4