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The modern data stack is built on key layers—data access, compliance, orchestration, observability, integration, storage, and analytics. In this report, we unpack how these components fit together to enable secure, scalable, and intelligent data-driven organizations. Explore why high-quality, well-governed data is the foundation for unlocking the full potential of AI.

Data Fabric vs. Data Mesh: Choosing the Right Path for Data Management In today’s data-driven economy, organisations need more than just storage—they need smart strategies to manage, integrate, and govern data effectively. Two modern approaches are leading the way: Data Fabric and Data Mesh. 🔹 Data Fabric Data Fabric is a centralised architecture that unifies diverse data sources—whether cloud or on-premises—into a single accessible layer. It leverages AI, metadata, and automation to simplify data integration, strengthen governance, and ensure seamless access across the enterprise. This model is particularly effective for organisations aiming for consistency, visibility, and control in complex environments. 🔹 Data Mesh On the other hand, Data Mesh focuses on decentralisation. Treating data as a product empowers domain-specific teams to manage and share their own datasets. With self-service infrastructure and federated governance, Data Mesh is ideal for large, distributed enterprises where agility, autonomy, and scalability are critical. 🔸 Key Distinctions Governance: Centralised in Fabric vs. Federated in Mesh Ownership: IT-driven in Fabric vs. Domain-led in Mesh Flexibility: Mesh promotes autonomy; Fabric ensures simplicity Implementation: Fabric is straightforward, while Mesh fosters innovation at scale Ultimately, the choice between Data Fabric and Data Mesh depends on your business size, complexity, and culture. #DataFabric #DataMesh #DataArchitecture #DataManagement #DataGovernance #BigData #DigitalTransformation #DataStrategy #EnterpriseIT #Innovation #ITServices #TechniHire

Data catalogs are the foundation for discovery — the place where employees go to understand what data exists. But discovery alone isn’t enough. Without a way to quickly and securely provision access, users still end up waiting weeks, and governance teams drown in manual approvals. That’s why leading organizations are pairing catalogs with intelligent data provisioning — uniting discovery with automated, governed access. The result: catalogs evolve into true hubs for data products, where finding and using data is seamless, safe, and scalable. Read more → https://ow.ly/9hNf50WUzv8 #Immuta #DataProvisioning #DataAccess #Governance

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

Precisely has partnered with Opendatasoft to launch a new integrated data marketplace, making it easier for teams to access trusted, AI-ready data! The offering combines Opendatasoft’s data sharing platform with the Precisely Data Integrity Suite to improve data discovery, sharing, and use across the organization. 👉 Read the full announcement here: https://okt.to/2AJRlZ #DataIntegrity #AIReadyData #DataMarketplace

Attention: Do you fully trust your enterprise data? And let’s be clear: true, high-quality data is no longer optional — it’s existential. The competition already understands this: enterprises will only survive and thrive in the future if they are truly data-driven. Those who fail to establish trust in their data risk being left behind. With Actian Data Observability, enterprises gain AI-powered, 100% visibility across their data estate, ensuring reliability, lowering costs, and empowering confident decision-making. Why it matters for the C-Suite: • CFOs slash hidden costs from bad data and optimize cloud spend. • CIOs & CTOs accelerate digital and AI initiatives with reliable pipelines. • CEOs & Business Leaders unlock faster, trusted insights that drive growth. Powered by real-time ML anomaly detection, root-cause diagnostics, and seamless integration with modern data architectures (Iceberg, Delta Lake, Hudi, warehouses, streams), Actian transforms data chaos into trusted, cost-efficient intelligence. In today’s economy, observability isn’t a “nice-to-have” — it’s a boardroom-level requirement. The question is no longer if you need data observability, but how fast you can put it in place.

I recently read a fantastic data modernization guide from Snowflake's Federico Zoufaly, Matt Kvancz, and Josh Klahr and EY's Patrick Siconolfi and Margherita Braga designed to help improve data migration analysis and implementation. Key Takeaways: 1. Don’t treat migration as a lift-and-shift. Embed automation, quality, and business alignment. 2. Prioritize platforms that combine scalability with governance like Snowflake’s AI-Data Cloud. 3. Approach modernization as an incremental, strategic process that is aligned to value, not just infrastructure. 4. The right data foundation unlocks your path to AI and advanced analytics. Click the below to read the EY–Snowflake report: 

EY and Snowflake have collaborated to produce this insightful report that explores how organizations can modernize their data landscapes, enabling scalability, resilience, and AI-powered insights for the future. Read more and download the full report today. #ShapeTheFutureWithConfidence

EY and Snowflake have collaborated to produce this insightful report that explores how organizations can modernize their data landscapes, enabling scalability, resilience, and AI-powered insights for the future. Read more and download the full report today. #ShapeTheFutureWithConfidence

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