How Data Fabric simplifies data management across multi-cloud environments

Enterprise buyers are rethinking their data foundations as AI adoption accelerates. The right data platform isn’t just about storage — it’s about scale, governance, and enabling advanced analytics to unlock business value. ISG’s 2025 Buyers Guide for Data Platforms provides a clear view of leading providers, key differentiators, and the innovations shaping tomorrow’s enterprise data strategies. Whether you’re modernizing legacy systems or seeking a cloud-native solution, this guide helps decision-makers cut through the noise and identify the best fit for their digital roadmap. 📖 Explore the guide here: https://bit.ly/48lRG7r #DataPlatforms #EnterpriseAI #DigitalTransformation

Broadcom announces the availability of  VMware Tanzu Data Intelligence. Tanzu Data Intelligence goes beyond big data, data warehousing and data lakes to provide a full stack solution for using all the traditional and new-age data sets for both analytics and to power next generation applications driven by data and AI:  https://lnkd.in/gtpkZMWE

Managing data at scale is becoming more challenging than ever! Without the right database platform, organizations struggle with fragmented data silos, poor scalability, compliance risks, and limited insights—especially in hybrid and AI-driven environments. Challenges organizations face without EDB Postgres AI: ❌ Complex and costly database management ❌ Limited scalability for enterprise workloads ❌ Inability to unlock deep insights with advanced analytics ❌ Lack of seamless hybrid and multi-cloud deployment options ❌ Data security and compliance gaps that slow innovation With the exponential growth of data and AI adoption, businesses need a smarter, scalable, and secure database foundation to turn raw data into actionable intelligence. Why EDB Postgres AI with TechnoBind: ✅ AI-Powered Database – Drive smarter decisions with intelligent automation ✅ Enterprise-Grade Scalability – Handle complex workloads with ease ✅ Advanced Analytics – Unlock powerful insights from your data ✅ Seamless Cloud & Hybrid Deployment – Flexible and future-ready architecture ✅ Security & Compliance First – Protect sensitive data and meet global standards Empower your business with smarter data and smarter decisions—anytime, anywhere, with EDB Postgres AI, enabled by TechnoBind. . . . #EDB #PostgresAI #Database #DataAnalytics #Cloud #HybridCloud #TechnoBind #ScalableData #AI

🚀 Automating Data Governance: What’s Next? From AI-driven policy enforcement to real-time, cloud-native governance—data governance is evolving fast. In this latest series, Fred Krimmelbein breaks down emerging trends like self-service platforms, metadata-driven automation, and Gen AI use cases that are reshaping how we manage data. 👉 https://lnkd.in/gVsuiy8d #DataGovernance #Automation #GenAI #DataOps #CloudData #Compliance

Understanding the Big Data Life Cycle In today’s data-driven world, managing the Big Data Life Cycle is crucial for driving value and insights across organizations. This cycle highlights the end-to-end journey of data, ensuring it’s collected, processed, stored, learned from, and visualized to deliver actionable business value. Fusion – Combining diverse datasets with algorithmic flexibility, scalability, and uncertainty modeling. Storage – Ensuring integrability, scalability, and computational efficiency for massive volumes of data. Processing – Driving adaptability and performance through uncertainty modeling, scalability, and efficiency. Learning – Empowering self-learning systems, adaptability, and predictive capabilities. Visualization – Transforming complex datasets into clear, interactive, and versatile insights for decision-making. At the center lies Data Security & Governance, ensuring compliance and reliability across every phase. The ultimate goal? Turn DATA into VALUE through strategic handling of each lifecycle phase. #BigData #DataEngineering #MachineLearning #DataGovernance #Analytics #DataScience #Cloud #C2C #SeniorDataEngineering #BigDataEngineer

𝗙𝗿𝗼𝗺 𝗠𝗮𝗻𝘂𝗮𝗹 𝗣𝗿𝗼𝘃𝗶𝘀𝗶𝗼𝗻𝗶𝗻𝗴 𝘁𝗼 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀   Hej! 👋   For a long time, managing users and groups on Databricks meant navigating complex SCIM workflows, manual provisioning, and a lot of administrative overhead. Onboarding new users and ensuring consistent access controls could be a real bottleneck.   That's all changing now! Databricks has announced that Automatic Identity Management for Entra ID is now Generally Available.   𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻?   This new native feature provides a direct and automated link between your Entra ID directory and your Databricks account. It automatically syncs:   • Users   • Groups   • Service Principals   You no longer have to manually provision identities or manage complex sync jobs. It’s on by default for new accounts and a simple opt-in for existing ones.   𝗪𝗵𝘆 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗔𝗱𝗺𝗶𝗻𝘀?     1. Faster Onboarding and Offboarding: When a new team member is added to a group in Entra ID, they automatically appear in Databricks. When they leave, their access is revoked instantly. This ensures a secure and seamless user lifecycle.     2. Centralized Governance: All identity management is now centralized in Entra ID. This means the single source of truth for your organization’s users and groups is automatically enforced in Databricks.     3. Effortless Collaboration: This is a big one. You can now easily share dashboards and data with anyone in your company—even if they've never logged into Databricks. They will automatically be provisioned to view the content, reducing friction for business users and encouraging broader data adoption.   𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻:   This feature is more than a technical upgrade; it's a strategic move to simplify platform governance and accelerate productivity. It tightly integrates Databricks with the tools your company already uses, making your data platform more secure, scalable, and user-friendly.   What are your thoughts? Will this new feature change how you manage user access in your organization?   ___________________   I’m Mengyu Shi, a passionate Data Engineer.   My mission is to help companies transform complex, historically grown data landscapes into modern, scalable, and automated platforms.   If you’d like to get more practical tips on data topics, feel free to subscribe to my newsletter. Mengyu   #Databricks #Azure #IdentityManagement #DataGovernance #DataInsightConsulting  

Thursday – Key decision factors vs. typical organization needs 🗝 The right architecture depends on your context. Here’s what to weigh when choosing: * Organizational Structure: Central IT-managed, efficient for centralized models Domain-aligned teams with contextual understanding * Governance: Unified policies and oversight Federated governance with autonomy and flexibility * Scalability Model: Adding storage scales vertically Domain scaling grows horizontally with autonomy * Data Accessibility: Central control, less self-serve Enables self-serve by domain users * Analytics Use Cases: Best for full-scale analytics and ML Great for domain-specific, real-time, agile use cases These patterns help determine the best path for your org. 👉 Which axis (structure, governance, scale) presents the biggest tension for your org? Pro Tips: * Sketch your domains and data use cases then align the model. * Consider hybrid: lakes for storage, mesh for consumption. * Invest early in self-serve governance tooling. 📖 🔗 https://lnkd.in/gCHvr-rn 🔗 https://lnkd.in/gtxvKZPe 🔗 https://lnkd.in/gMFHTS6k 🔗https://lnkd.in/ghQwZdWu #AI #MachineLearning #DataOps #OrganizationalAlignment #HybridStrategy

AI might be the engine behind today’s biggest breakthroughs, but we know that without the right data in the right place, that engine stalls. Most organizations are sitting on massive volumes of data buried in legacy file systems, scattered across cloud storage, or trapped inside platforms like SharePoint and Salesforce. This data sprawl makes it hard to move fast. VAST Data calls this the “last mile” problem. Even with advanced models and plenty of compute, teams struggle to get their data into a pipeline where AI can actually use it. VAST has introduced SyncEngine as a possible solution to this problem. It is designed to act as a universal data router, automatically discovering, cataloging, and moving unstructured data across fragmented systems and SaaS platforms. By collapsing migration, indexing, and transformation into a single workflow, SyncEngine helps organizations feed their AI pipelines without relying on brittle scripts or a patchwork of third-party tools. Please follow Hardial Singh  for such content. #linkedIn #Cybersecurity #informationsecurity #cloudsecurity #datasecurity #cybersecurityawareness #Data #Bigdata #Hadoop #Enterprisedata #Hybridcloud #Cloud #Cloudgovernance #Devops #Devsecops #Secops #cyber #infosec #riskassessment #informationsecurity #auditmanagement #informationprotection #securityaudit #cyberrisks #cybersecurity #security #cloudsecurity #trends #AWS #EC2 #AWSStorage #Cloudstorage https://lnkd.in/gfkrFUFJ

𝐇𝐨𝐰 𝐂𝐨𝐧𝐟𝐥𝐢𝐜𝐭-𝐅𝐫𝐞𝐞 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬 (𝐂𝐑𝐃𝐓𝐬) 𝐏𝐨𝐰𝐞𝐫 𝐑𝐞𝐝𝐢𝐬 𝐟𝐨𝐫 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞, 𝐀𝐥𝐰𝐚𝐲𝐬-𝐀𝐯𝐚𝐢𝐥𝐚𝐛𝐥𝐞 𝐃𝐚𝐭𝐚 🧠 In today’s distributed systems, ensuring eventual consistency without coordination bottlenecks is essential. That’s where CRDTs shine—and Redis has embraced them to deliver highly available, low-latency data replication. 𝑾𝒉𝒂𝒕 𝑨𝒓𝒆 𝑪𝑹𝑫𝑻𝒔? 🤔 Conflict-Free Replicated Data Types are specially designed data structures that support: ➡️ Concurrent updates across multiple replicas ➡️ Automatic conflict resolution without locks or leader election ➡️ Deterministic merges ensuring every replica converges to the same state At their core, CRDTs track causality so operations commute—whether you’re adding, removing, or incrementing, updates can be applied in any order and still yield the correct final result. 𝑯𝒐𝒘 𝑪𝑹𝑫𝑻𝒔 𝑾𝒐𝒓𝒌? 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧-𝐛𝐚𝐬𝐞𝐝 𝐂𝐑𝐃𝐓𝐬: Each change (e.g., “add 5”) is broadcast to all replicas. Operations carry enough metadata to be applied in any order safely. State-based CRDTs: Entire state snapshots are merged. Each replica periodically exchanges its state and uses a merge function that’s associative, commutative, and idempotent. 𝐇𝐲𝐛𝐫𝐢𝐝 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬: Combine both to optimize bandwidth and convergence speed. 𝐖𝐡𝐲 𝐑𝐞𝐝𝐢𝐬? Redis Enterprise integrates CRDTs via its Redis Conflict-Free Replicated Data Types module (Redis CRDT), enabling: Geo-distributed clusters with multi-master writes Zero downtime under network partitions Automatic reconciliation when partitions heal 𝑪𝑹𝑫𝑻𝒔 𝒊𝒏 𝑹𝒆𝒅𝒊𝒔 𝑬𝒏𝒂𝒃𝒍𝒆: Scalable counters (G-Counters, PNCounters) for real-time analytics Grow-only sets (G-Sets) for distributed feature flags Observed-Remove Sets (OR-Sets) for collaborative applications Flags and registers for coordination-free feature toggles and leaderless locking Real-World Use Cases Gaming leaderboards: Consistent ranking updates from players around the globe without locking IoT telemetry: Edge devices record sensor readings locally and sync seamlessly when back online Collaborative editing: Multiple users update shared documents or whiteboards concurrently 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 𝑩𝒚 𝒄𝒐𝒎𝒃𝒊𝒏𝒊𝒏𝒈 𝒕𝒉𝒆 𝒔𝒊𝒎𝒑𝒍𝒊𝒄𝒊𝒕𝒚 𝒂𝒏𝒅 𝒓𝒊𝒄𝒉 𝒆𝒄𝒐𝒔𝒚𝒔𝒕𝒆𝒎 𝒐𝒇 𝑹𝒆𝒅𝒊𝒔 𝒘𝒊𝒕𝒉 𝒕𝒉𝒆 𝒓𝒐𝒃𝒖𝒔𝒕 𝒄𝒐𝒏𝒇𝒍𝒊𝒄𝒕 𝒓𝒆𝒔𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝒑𝒓𝒐𝒑𝒆𝒓𝒕𝒊𝒆𝒔 𝒐𝒇 𝑪𝑹𝑫𝑻𝒔, 𝒕𝒆𝒂𝒎𝒔 𝒄𝒂𝒏 𝒃𝒖𝒊𝒍𝒅 𝒕𝒓𝒖𝒍𝒚 𝒂𝒍𝒘𝒂𝒚𝒔-𝒐𝒏, 𝒉𝒊𝒈𝒉𝒍𝒚 𝒄𝒐𝒏𝒄𝒖𝒓𝒓𝒆𝒏𝒕 𝒅𝒊𝒔𝒕𝒓𝒊𝒃𝒖𝒕𝒆𝒅 𝒂𝒑𝒑𝒍𝒊𝒄𝒂𝒕𝒊𝒐𝒏𝒔—𝒘𝒊𝒕𝒉𝒐𝒖𝒕 𝒕𝒉𝒆 𝒄𝒐𝒎𝒑𝒍𝒆𝒙𝒊𝒕𝒚 𝒐𝒇 𝒎𝒂𝒏𝒖𝒂𝒍 𝒄𝒐𝒏𝒇𝒍𝒊𝒄𝒕 𝒉𝒂𝒏𝒅𝒍𝒊𝒏𝒈 𝒐𝒓 𝒄𝒆𝒏𝒕𝒓𝒂𝒍 𝒄𝒐𝒐𝒓𝒅𝒊𝒏𝒂𝒕𝒊𝒐𝒏. — Empower your next project with Redis CRDTs and see consistency and availability coexist. #Redis #CRDT #DistributedSystems #EventualConsistency #RealTimeData