Databricks' new features for declarative pipelines and data governance

SQLv2 Early Benchmarks: 60% Faster ML Pipeline Execution Our first benchmarks show SQLv2 reduces end-to-end ML pipeline latency by 60% compared to traditional architectures that run inference outside the database. No data movement. No API calls. No orchestration overhead. Embedding, inference, and vector operations execute natively inside SQLv2.   This is the real advantage of an AI-native database: less plumbing, more performance.   👉 See the live benchmark breakdown at https://lnkd.in/djASucSy #SQLv2 #Database #AI #MachineLearning #MLOps #DataEngineering

📢 Big News from Databricks! 📢 Just dove into their latest presentation on the new Agent Framework and Model Composition Protocol (MCP) – and wow, this is a serious game-changer for anyone working with tool-calling AI agents! 🤯 Forget the spaghetti code of integrating tools; Databricks is bringing order to the agent universe. Here’s why I’m hyped: * Standardization FTW! 🌐 MCP is like the universal translator for AI agents. It gives them a common language to discover and use tools, making development smoother than a freshly paved highway. No more "Does this tool even speak my agent's language?" moments! * Governance & Security? ✅ In the world of AI, trust is everything. Databricks delivers robust governance and granular security controls. Your agents will know their boundaries – and stay within them. Think of it as having a super-smart, perfectly obedient assistant. 😇 * From Idea to Deployment, FAST! 🚀 The framework makes prototyping in the AI playground a breeze (yes, even no-code!). Then, deploying your agent is so straightforward, you'll be wondering if you missed a step. It's like having a fast-forward button for your agent lifecycle. * Data Connectivity, Simplified! 🔗 With managed MCP servers for Unity Catalog, your agents can access data securely and efficiently. No more wrestling with data access policies – it's all governed and ready. Your agents will be data-ninjas! 🥋 * Endless Extensibility! 🛠️ Want to connect your agents to custom tools or APIs? Build and deploy your own MCP servers! The possibilities are truly limitless, turning your agents into super-powered Swiss Army knives. 🔪 This is a massive leap forward, making AI agents more powerful, secure, and genuinely easier to build. If you're in the AI space, you NEED to watch this! 👇 Check out the full presentation here: https://lnkd.in/eDDaZGvV #Databricks #AI #Agents #MCP #MachineLearning #Innovation #DataScience #BigDatapedia

🔧 Designing Observability That Powers Real Reliability A recent academic study argues that true observability in cloud-native systems requires more than just logs. You need distributed tracing, application metrics, and infrastructure metrics working together. At Recursive Loop, we bring those same patterns into your infrastructure: ✅ Tracing to uncover cross-service latency ✅ Metrics to highlight performance and anomalies ✅ Infrastructure visibility to monitor health and scalability Because when your systems aren’t just seen—but understood—your business can be trusted. 🔁 Recursive Loop — Observability Engineered for Reliability #RecursiveLoop #Observability #Tracing #Metrics #CloudNative #InfrastructureHealth

If you're building RAG systems at scale, you know the real challenge isn't the LLM—it's the data pipeline feeding it. I just came across Datalab's Chandra, an open-source toolkit that actually solves this problem. It handles the boring-but-critical stuff: intelligent data orchestration, workflow automation, and modular pipelines that don't fall apart in production. What caught my attention: it's built for real enterprise scenarios. Multi-source ingestion, smart chunking, consistent vectorization, error handling that doesn't require you to panic at 2 AM. The kind of infrastructure that separates working prototypes from systems that actually scale. Been working on RAG architectures for a while now, and most teams are either building fragile custom solutions or cobbling together incompatible tools. Chandra changes that—it gives you a solid foundation without reinventing everything. If you're dealing with enterprise data silos, document fusion, or just tired of brittle pipelines, worth checking out. GitHub link in comments. Sample by Datalab: https://lnkd.in/eCvSxXTF What's your biggest pain point with RAG ingestion? #RAG #DataEngineering #AI #OpenSource

Unlock blazing speed in your data pipelines. Most pipelines crawl not because of hardware limits, but because of how they’re designed. Hard-coded logic. Spaghetti workflows. Endless patches to fix what shouldn’t have been brittle in the first place. Table-driven architecture fixes that. It centralizes pipeline logic in metadata, one source of truth for schema, rules, and transformations. No more rewriting the same logic across notebooks and jobs. Change it once, and every downstream process updates automatically. On Databricks, this means fewer failed runs, simpler debugging, and pipelines that actually scale. Your data engineers stop firefighting and start engineering. The result? Predictable pipelines, faster delivery, and measurable gains in both data quality and developer sanity. If your team is still hand-coding workflows one column at a time, you’re building technical debt instead of value. The future is table-driven. Build once, reuse everywhere, and move faster than the data you process.

Schema evolution isn’t just about adapting to change — it’s about governing it.   If every schema change puts your data pipelines at risk, the issue isn’t evolution — it’s governance. In dynamic data ecosystems, schema drift is inevitable.  Without control, it leads to pipeline failures, compliance gaps, and lost productivity. At xponent.ai, we built a Schema Handling Framework on Databricks — a modular, registry-driven solution that turns schema drift into governed evolution.  It enforces integrity, tracks every version, and ensures only approved, auditable changes reach production. Business impact:  • Reliability — pipelines adapt automatically to change   • Efficiency — fewer false schema alerts and rework   • Scalability — 95% code reusability across frameworks   • Transparency — 100% auditability and lineage tracking By combining schema enforcement, controlled evolution, and governance, the framework transforms reactive data management into proactive control — reducing operational risk and accelerating business outcomes. To turn every schema change into an advantage, write to us at hello@xponent.ai #Databricks #SchemaEvolution #DataGovernance #DataEngineering #XponentAI

🚀 Build Data Pipelines with Lakeflow Declarative Pipelines by Databricks! 🧱 How Lakeflow is transforming the way we build and manage data pipelines on the Databricks Data Intelligence Platform. Here's what I learned: 🔹 Declarative Pipelines: No more managing complex orchestration logic! Lakeflow lets you define what you want, and it figures out how to get there. 🔹 Unified Batch & Streaming: Lakeflow supports both batch and streaming data seamlessly, making real-time analytics more accessible. 🔹 Built-in Orchestration: With native support for dependencies, retries, and scheduling, Lakeflow simplifies pipeline management without needing external tools. 🔹 Data Quality & Governance: Integration with Unity Catalog ensures secure, governed, and discoverable data pipelines. 🔹 Productivity Boost: The intuitive UI and YAML-based pipeline definitions make it easy to collaborate across teams. #Databricks #Lakeflow #DataEngineering #DeclarativePipelines #Lakehouse #LearningJourney #DataOps

The Elementary MCP Server changes how data teams work. No more tab-hopping between tools. Your reliability context now moves with you, into your IDE, AI copilot, or BI workflow. In his latest post, Or Avidov shows how the MCP brings reliability into the flow, with real use cases like preventing breaking changes, improving test coverage, and resolving issues faster, all without switching tools. Read the full story 👉 https://lnkd.in/dr8eM3CG

Migrating to Unity Catalog? Real-World Tips That Actually Work 💡 From legacy chaos to governed clarity — here’s how pros do it. 1️⃣ – Visibility First Inventory before impact. - Map all tables, jobs, and ACLs. - Use UCX pre-check or system tables. - Spot cross-workspace dependencies early. 🛑 Avoid surprises. Visibility = control. 2️⃣ – Coexist with LakeBridge Don’t rush. Phase it. - LakeBridge lets Hive and Unity Catalog talk. - Ideal for business-critical pipelines. - Enables smooth, low-risk migration. 🧩 Think of it as a bridge, not a leap. 3️⃣ – Automate with UCX Migrate metadata, ACLs, notebooks. - Use UCX for bulk migration. - Run dry-runs to catch unsupported objects. - Save hours of manual effort. ⚙️ Automation = sanity. 4️⃣ – Fix Storage First DBFS mounts ≠ Unity Catalog. - Convert to external locations. - Register storage credentials. - 90% of blockers stem from this step. 🔐 Storage is the foundation. Nail it early. 5️⃣ – Validate Everything Post-migration ≠ parity. - Test queries, permissions, lineage. - Use system tables and audit logs. - Prioritize business-critical workloads. 🔍 Trust but verify. 6️⃣ – The Winning Combo UCX + LakeBridge = ✅ Minimal downtime ✅ Maximum governance ✅ Real-world success 🎯 Migration isn’t just technical — it’s strategic. --- Planning your Unity Catalog migration? Let’s talk strategy, governance, and ROI. Databricks #UnityCatalog #LakeBridge #UCX #DataEngineering #Migration #AzureData #QueryFederation ---

🎉Databricks October 2025 👉 Lakeflow Pipelines Editor is now in Public Preview The Lakeflow Pipelines Editor offers a unified workspace designed specifically for building Lakeflow Declarative Pipelines. It streamlines every stage of pipeline development—from writing code and organizing files to previewing data and visualizing pipeline flows—all in one intuitive interface. Built into the Databricks ecosystem, it also supports version control, collaborative code reviews, and automated scheduling, making it a powerful tool for modern data engineering. #LakeflowPipelinesEditor  #Databricks  #DataEngineering #DeclarativePipelines  #PipelineDevelopment  #VersionControl #CodeFirstWorkflows  #DataVisualization  #Automation  #TechInnovation #DatabricksMVP