Databricks raises $1B, reports $4B revenue run rate

Databricks showcased an incredible lineup of innovations at the Data + AI World Tour, and several stood out for me: Lakebase: A fully managed, PostgreSQL-compatible service with sub-10 millisecond latency and native lakehouse integration, enabling seamless synchronization between operational and analytical data. FinOps: An iterative approach to cost optimization where engineering teams continuously analyze usage and optimize workloads for efficiency. Data performance: Smarter file sizing, clustering strategies, and continuous authorization are becoming critical to scaling modern data platforms. With Databricks pushing boundaries, WorldLink US is helping enterprises operationalize these capabilities, simplifying complexity while unlocking value from their data and AI investments.

Still juggling manual scripts and siloed pipelines? You’re not alone. Many enterprise data teams hit a wall when scaling analytics: delayed insights, compliance risks, and stalled AI initiatives become the norm. That’s exactly where Databricks Workflows changes the game. As a trusted Databricks Partner, Optimum helps enterprises orchestrate and automate analytics pipelines across the Databricks Lakehouse Platform to unify data operations, enforce governance, and accelerate decision-making at scale. If your team is ready to move beyond fragmented processes and build an automated, governed analytics environment, our latest blog breaks down how Databricks Workflows can help you get there. Read the full post to see how your analytics operations can scale with Databricks Workflows: https://lnkd.in/g9VQNWsz #DatabricksPartner #DataGovernance #DatabricksWorkflows #EnterpriseAnalytics

Databricks 𝗠𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲 + 𝗗𝗲𝗹𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 = 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻𝗹𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀 The Databricks Marketplace isn’t just a catalog of datasets and AI models; it’s powered by Delta Sharing, an open protocol that enables seamless data exchange. ⚡ 𝗪𝗵𝗮𝘁 𝗗𝗲𝗹𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗱𝗼𝗲𝘀:  • Let's you share live data securely across clouds & platforms.  • Removes the need for copying or moving files.  • Works with any tool that supports open standards. 📈 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲:  • Providers can deliver up-to-date, ready-to-use data products.  • Consumers get instant access to their existing tools.  • Teams collaborate without worrying about format lock-in. In practice, this means faster time to insights, lower integration overhead, and a smoother path to building data-driven products. If you’re exploring the Databricks Marketplace, think of Delta Sharing as the engine making the ecosystem actually work. 𝙍𝙚𝙖𝙙 𝙢𝙤𝙧𝙚 𝙝𝙚𝙧𝙚: https://lnkd.in/d5Tsz8Rw #Databricks #DeltaSharing #Lakehouse #Marketplace #DataEngineering

Databricks hits $4B revenue run-rate and raises $1B in Series K at $100B+ valuation. “Our teams are putting up these results by building the data and AI infrastructure enterprises will rely on for decades,” says CEO Ali Ghodsi. Read more: https://lnkd.in/eJmRBpQF #Databricks #SeriesK #AIInfrastructure #DataAnalytics #TechIntelPro

💻 Learn More in My Latest Blog! I recently explored the power of AI Foundry to build multi-agent data orchestration using Semantic Kernel over Databricks genie agent and Fabric data agent. Check it out to see how these ideas lay the groundwork for innovations. 🔗 https://lnkd.in/gQw9cgP7 💬 What’s Your Take? How are you leveraging AI in de-federated or distributed data architectures? Does the Genie Shield align with your vision for the future of data systems? #DataArchitecture #Lakehouse #DataGovernance #Databricks #DataSecurity #MultiAgentSystems #MicrosoftFabric #FabricDataAgent #Innovation #BigData

Fabric or Databricks? Wrong question. These platforms aren’t interchangeable. They solve different problems, and understanding the distinction is where real strategy starts. Here’s the breakdown: Databricks is your engine for big data engineering, machine learning, and advanced analytics. It’s built for scale, flexibility, and complex compute especially when AI is in the mix. Microsoft Fabric is your unifier. It connects Power BI, Data Factory, Synapse, and OneLake into a streamlined analytics experience. It’s the glue between data, governance, and business users. Where they overlap: • Lakehouse architecture • Direct Lake performance • Support for semantic modeling and governed access • Shared potential for Copilot + GenAI Where they diverge: • Databricks leads with data science and engineering workflows • Fabric leads with reporting, governance, and business enablement In reality, we’re seeing clients use both and when architected well, they complement each other beautifully. It’s not a debate. It’s a decision: how do you align the right platform to the right outcome? #Databricks #MicrosoftFabric #DataArchitecture #Lakehouse #AIEnablement #DataStrategy #SemanticModeling #Collectiv #BIPlatform

Databricks Surpasses $4B Revenue Run-Rate, Exceeding $1B AI Revenue Run-Rate : Databricks’ Recent Performance This new investment comes on the heels of strong momentum for Databricks, which includes: -Surpassing $4 billion revenue run-rate, growing >50% year over year. -Recently exceeding $1 billion revenue run-rate for its AI products. -Achieving positive free cash flow over the last 12 months. -Net retention rate sustaining >140%. -650+ customers consuming at over $1 million annual revenue run-rate.

Why settle for one when you can harness the best of both? With 20 years as a hands-on architect in solutions and data, I’ve applied the patterns that define modern software architecture - Microservices, Event-Driven, Distributed Systems, DDD, Low-Code/No-Code, and more. The same principle powers advanced data solutions: a hybrid approach, blending the strongest capabilities of diverse platforms, delivers results faster, cleaner, and more elegantly. Across today’s data landscape, real value emerges when you combine: 1. Enterprise-grade governance and compliance 2. Event-driven automation and actionable alerts 3. Seamless, shortcut-enabled access that eliminates silos 4. Always-fresh reporting designed for real business users 5. High-performance analytics and advanced ML/AI at scale By mixing these strengths, one can erase handoffs, reduce complexity, and empower both technical and business teams to focus on what matters most: insights, automation, and impact. The Conductor: Microsoft Fabric Fabric orchestrates the flow - building pipelines, transforming data, and automating actions through Data Activator. Shortcuts unify data access across AWS, Azure, GCP, SaaS apps, and more, while semantic refresh ensures always-current insights in Power BI. As the platform hub, Fabric integrates natively with Microsoft 365 and Azure AI, while keeping external engines like Databricks modular and pluggable - all without unnecessary complexity in the data core. The Soloist: Databricks Databricks brings computational muscle. Apache Spark and Photon power distributed processing at any scale. MLflow and Feature Store streamline the machine learning lifecycle from training to production. Streaming and deep learning frameworks unlock real-time inference and predictive analytics. The Unified Data Plane: OneLake The Unified Data Plane: OneLake OneLake solves the multi-engine data problem elegantly. One Delta Lake format, ADLS Gen2 APIs, cross-platform compatibility. Databricks sees it as native Delta storage, Power BI reads via Direct Lake, external tools connect through standard APIs. The result: architectural simplicity where data lives once but serves many purposes. As a hands-on architect, I see this hybrid, best-of-both-worlds model enabling solutions that are faster, cleaner, and future-ready. Microsoft’s approach doesn’t close doors - it opens them wider . Why build silos, when we can build symphonies? #MicrosoftFabric #Databricks #DataArchitecture #DeltaLake #DataGovernance #AI #HybridArchitecture #DataPlatform

Earlier this year, the buzz was that 𝗙𝗮𝗯𝗿𝗶𝗰 𝗶𝘀 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲. Fast forward a few months, and now people are saying 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗮𝗰𝘁𝘂𝗮𝗹 𝗳𝘂𝘁𝘂𝗿𝗲! So, who’s right? Let’s break it down. 👇 💡 Databricks Think of it as building your own custom robot. You pick every component—data sources, pipelines, ML models, and outputs. The reward? Full flexibility, high compute power, and advanced analytics capabilities. But yes, it needs expertise and careful planning. 🤖 Microsoft Fabric Like using a smart all-in-one gadget .Everything is ready: data integration, warehousing, real-time analytics, and dashboards. Maintenance is automatic, so teams can focus on insights. Fast, integrated, and low-maintenance. ✅ Verdict Fabric = speed, simplicity, seamless Microsoft integration Databricks = flexibility, power, AI-ready analytics Many organizations combine both Fabric for dashboards and governance, Databricks for advanced ML and engineering. #DataAnalytics #MicrosoftFabric #Databricks #MachineLearning #DataEngineering #FutureOfData #CloudComputing

🚀 Keeping up with Databricks Terminology – 2025 Edition 🚀 Databricks is evolving into a full Data Intelligence Platform, and along with it comes a wave of new terminology that every data professional should know. Here are some of the important ones 👇 🔹 LakeFlow Declarative Pipelines → successor to Delta Live Tables (DLT). Build declarative pipelines that handle orchestration, monitoring, and optimization automatically. 🔹 LakeFlow Connect → simplifies ingestion from SaaS apps, DBs, and message buses with managed + standard connectors. 🔹 LakeBase → Databricks’ new transactional database engine built into the Lakehouse, enabling OLTP-style workloads. 🔹 Lakehouse Federation → query data across external sources and catalogs without moving it into Databricks. 🔹 Lakehouse Monitoring → observability for tables, pipelines, and ML models to ensure data quality & reliability. 🔹 Unity Catalog Volumes → governance for non-tabular datasets (files, images, audio, etc.). 🔹 Agent Bricks (Mosaic AI) → no-code/low-code framework to build and evaluate enterprise AI agents on your data. 🔹 Databricks Asset Bundles → package jobs, notebooks, and pipelines for CI/CD and version control. They’re part of Databricks’ move to unify data engineering, streaming, AI, and transactional workloads under one intelligent platform. 👉 Curious to hear: Which of these new terms do you find most impactful for your projects? #Databricks #Lakehouse #LakeFlow #LakeBase #LakehouseMonitoring #UnityCatalog #DataEngineering #DataPlatform #GenerativeAI #MosaicAI #DataIntelligence #ETL #StreamingData #ModernDataStack #AI