"Building the Ultimate Data Platform: A Competitive Race"

🚀 Big news from #FabConVienna! Microsoft Fabric is redefining how we build data-rich AI agents on an enterprise-ready foundation. Full blog post and snappy video demos here: https://lnkd.in/eX94bH7N Key highlights: ✅ Graph in Fabric (Preview) – Unlock hidden relationships across customer, partners and supply chains for smarter decisions. ✅ Maps in Fabric (Preview) – extract real-time location-based insights for your existing business processes to drive better awareness and outcomes ✅ OneLake upgrades – Zero-copy data access, Oracle & BigQuery mirroring, and stronger governance. ✅ Developer boost – Extensibility Toolkit + MCP for seamless automation and AI-assisted coding. Why it matters: ✔ Faster AI adoption ✔ Real-time intelligence ✔ Enterprise-grade security The future of AI isn’t just more data—it’s connected, contextualised data that drives action. 👉 How will you use these innovations to transform your business? #MicrosoftFabric #AI #DataStrategy #FabConVienna

Everyone wants an AI strategy. But if your data architecture wasn’t built for AI, and most weren’t, you don’t have a strategy. You have a wish list.    The reality is, AI can’t fix fragmented data. It can’t guess context. And it can’t deliver real-time decisions if your systems are syncing overnight.    Enterprise data is messy by nature. It lives on the mainframe, in the cloud, and across everything in between. Most of it wasn’t built to support the speed or complexity AI demands.    Getting that data ready is a structural shift (not an IT task). One that requires trust, synchronization, metadata, and resilience. Without that foundation, even the smartest models will fail.    Rocket Software’s latest blog lays out what it actually takes to get enterprise data AI-ready, without tearing everything down.  https://lnkd.in/e48wjgFt

Snowflake sits at the intersection of cloud data and AI — growth is real, but valuation and execution risk are front and center. Macro trends - AI-driven analytics and increased enterprise data consumption are expanding the TAM (InvestingPro projects ~$170bn → ~$355bn by 2029). Cloud-native platforms that simplify ML/AI workloads are winning budget dollars. Key factors (problem → solution) - Problem: Enterprises need scalable, integrated data+AI platforms without heavy ops overhead. - Solution: Snowflake’s product velocity (400+ features y/y), Snowpark/Dynamic Tables and Microsoft/OpenAI integration address that demand. Evidence: Q2 FY26 product revenue +32% YoY; TTM revenue ~$4.12bn; ~6,100 customers using AI features (up from 5,200). Management raised FY26 guidance by $70m. Strategic bolt-on: Crunchy Data acquisition (~$250m) strengthens Postgres support and cross-sell potential. Risks - Consumption-based model amplifies macro sensitivity: ~40% of surveyed customers may limit spend. CFO transition (Mike Scarpelli retiring) creates short-term governance risk. Competitive pressure from Databricks and rapid tech shifts (e.g., Iceberg) require continuous execution. FCF has occasionally lagged expectations. Actionable insights - Investors: assess trajectory of $1m+ customer cohorts, AI-driven usage (50% of new-logo wins were AI-influenced), and guidance trends before adding exposure. Given stretched multiples versus some fair-value estimates, consider phased entry or options strategies to manage valuation risk. - Operators/Career: deepen Snowflake + ML/AI integration skills; roles bridging data engineering and applied AI will be in demand. - Executives: prioritize predictable consumption metrics and customer ROI stories to protect renewals under tighter budgets. Key takeaways / forecast Snowflake is well-positioned to capture secular AI/data growth but remains vulnerable to macro-driven consumption swings and execution risk. If AI adoption and cross-sell from recent initiatives accelerate, expect continued revenue acceleration; otherwise, growth could moderate until macro clarity returns. What do you think? Share your experience with Snowflake deployments or investment approach. — Viktor Kopylov, PhD, CFA.

Fall is almost here which means it’s time to start thinking about your 2026 strategy. In this month’s Datavail Data Cache Newsletter, we explore the latest in data & AI leadership: 🔹 The AI adoption gap – Enterprises struggling with AI adoption are losing $87M annually (Couchbase). 🔹 95% of GenAI pilots are failing – MIT reveals what separates success from failure. 🔹 Emerging risks – Gartner identifies tariffs & trade wars among the top 5 risks for 2H 2025. 🔹 AI that delivers ROI – Microsoft highlights 1,000+ real-world Copilot, Azure OpenAI, and Fabric use cases. 🔹 Data leaders agree – 84% say data quality & reliability are their top focus. 🔹 Agentic AI in action – Microsoft Power Automate now includes AI Builder actions to transform workflows. Blog posts from our team this month: – Enhancing RMAN Performance: Key Concepts and Practical Tips – 7 Considerations for Microsoft Fabric Adoption – How to Solve the Oracle Error ORA-12154: TNS: could not resolve the connect identifier specified – AWS Cloud Database Migrations – Migrating Cron/Scheduled Jobs to AWS Lambda for Amazon RDS for Oracle – Architecting the Future of Enterprise Workflows with Agentic AI – What Do Business Analysts Do During the Requirements Gathering and Design & Planning Phases of a Digital Transformation Project? – The End of an Era: Why Smart MySQL Teams Are Moving from MEM to OEM for Database Monitoring And a Webinar – The Impact of AI on the Database Administrator Where you can find us in September: Sept 4 – AWS Summit Toronto Sept 15–16 – PASS Summit on Tour, Dallas Sept 29–Oct 1 – PGConf NYC #AI #Cloud #Data #Databases #DigitalTransformation #Datavail

Lakehouse platforms are becoming a critical enabler for data-driven decision-making. 🤔 But which one best supports the unique needs of manufacturing and supply chain organizations? As I dive into this topic and explore available insights, I’d love to hear your recommendations. 🥇Dremio - best if you want open formats, federation, and cost-efficient analytics, with a growing AI/agent angle. - Core strength: Open lakehouse + federation 🥇Databricks - best for end-to-end AI/ML + data engineering, but heavier and more complex. - Core strength: AI/ML + data engineering 🥇Snowflake - best for BI, SQL analytics, and ecosystem/data sharing, but more closed. - Core strength: Cloud warehouse + sharing ecosystem

Choose Data Lakehouse or Data Warehouse? 🤔 In today’s AI/ML-driven business landscape, data strategy is no longer just about storage—it’s about unlocking insights at scale. The debate often comes down to Data Lakehouse vs. Data Warehouse. Here’s how they stack up: 🔹 Data Warehouse – Best for Traditional Reporting ✅ Optimized for structured data and BI dashboards ✅ Mature ecosystem with strong SQL support 💰 Higher cost as data volumes grow ❌ Limited flexibility for unstructured/semi-structured data ❌ Not ideal for AI/ML training workloads 🔹 Data Lakehouse – Best for AI/ML & Modern Analytics ✅ Handles structured + unstructured data seamlessly ✅ Supports machine learning, streaming, and advanced analytics ✅ More cost-effective at scale (separates compute from storage) ✅ Enables real-time insights + scalable experimentation ❌ Requires modern cloud infrastructure and newer skill sets 📊 Cost Perspective: Warehouses charge premium pricing for storage + compute, making them expensive for big data. Lakehouses leverage cheap cloud object storage, scaling compute separately = pay only for what you use. 💡 Key Takeaway: If your organization is still reporting-centric, a Data Warehouse remains the safest bet. But if you’re aiming to be AI/ML-first, adaptive, and future-ready, a Data Lakehouse is the foundation you’ll need. 🚀 The real question isn’t which is better overall, but rather: Is your business ready to evolve beyond reporting into AI-driven intelligence?

The future of data and AI isn’t coming—it’s here. At #FabConVienna, Microsoft Fabric made it clear: the era of fragmented data is ending. With OneLake’s zero-copy mirroring, graph and geospatial capabilities, and developer-first extensibility, we’re moving toward a world where data is not just stored—it’s activated for AI at scale. This isn’t about features; it’s about strategy. Organizations that unify their data estate today will lead in building intelligent applications tomorrow. Question for you: How ready is your company data platform to power the next generation of AI-driven experiences? Read about all of the announcements made at FabCon Vienna here: https://lnkd.in/dmF_T5Uy #MicrosoftFabric #DataStrategy #AIInnovation

SDI Presence names Garrick Schermer as AI Data Strategy & Governance Lead to help enterprises drive secure, responsible data and AI initiatives. Read the Latest Full News - https://lnkd.in/dQEmeTWY #SDIPresence #AIDataStrategy #DataGovernance #EnterpriseAI #Analytics #CloudModernization #ResponsibleAI #Snowflake #Azure #Databricks #TechEdgeAI #TechEdge

BonData’s AI-powered data platform, now on AWS Marketplace, simplifies data management for the AI era. “At BonData, we believe the future belongs to organizations that empower their employees to leverage and act on their data in a fast and simple way,” said Caroline (Sakkal) Meidan, CEO and Co-Founder of BonData. Read the full news: https://lnkd.in/dKz6UNkp #DataManagement #AIPlatform #AWSMarketplace #IntelliBond #DataGovernance #AIDataEngineer #TechIntelPro

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 EDBPostgres 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://bit.ly/45ZjbT2 #EDBPostgresAI #SovereignAI #DataSovereignty #AgenticAI #PostgreSQL #DataSecurity #AIFactory