Databricks
Data Platform
Data lakehouse platform, DBRX model, Mosaic ML acquisition.
Recent activity
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Lakebase's copy-on-write branching and branch expiration (TTL) features enable fast and cheap CI/CD testing by creating isolated, writable copies of production data in seconds, scaling to zero when idle, and automatically cleaning up abandoned branches. This approach allows for 100 concurrent PR branches on a ~2.5 TB production dataset, with provisioning times under 60 seconds and storage costs comparable to traditional CI/CD methods. By leveraging Lakebase's features, teams can achieve fast and secure CI/CD testing, with security and compliance requirements met through private networking and identity management. AI summary
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Databricks and SAP are expanding their partnership to provide end-to-end data and AI capabilities, allowing customers to accelerate time to value for advanced analytics and AI by leveraging SAP's deep understanding of business processes and Databricks' industry-leading data platform. The new SAP Application Data Foundation makes SAP application data available for consumption in SAP Business Data Cloud, including SAP Databricks, and provides a cloud-native way to access SAP application data, helping to lower total cost of ownership and reduce time to value. AI summary
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Wealth management client onboarding requires a platform that connects case context across systems, coordinates work as conditions change, and preserves visibility and governance. Governed case context, which integrates evidence, case state, completed controls, open exceptions, agent activity, and decision history, enables supervised AI agents to coordinate the onboarding journey with governance, traceability, and human oversight. AI summary
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Senior data and AI leaders are making deliberate, evidence-based decisions to balance momentum, accountability, and flexibility in order to turn AI ambition into sustained enterprise value, as the pace of AI progress continues to accelerate and expectations rise. AI summary
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Real-Time Mode (RTM) on Spark Declarative Pipelines (SDP) is now Generally Available, enabling end-to-end processing latencies in the millisecond range for operational streaming workloads. RTM eliminates the need for separate specialized engines like Apache Flink, unifying batch, ETL, and low-latency streaming under a single framework. AI summary
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Serverless cost controls now incorporate three objectives: attribute costs by tagging serverless compute objects, control access through entitlements, and limit spend with rate limits. These controls are now available in Databricks, allowing teams to monitor and manage their serverless spend more effectively. AI summary
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Procore has implemented OpenSharing on Databricks to power its construction analytics solution, doubling the catalog of extractable data for its customers across all projects and regions. This allows customers to pull data for their downstream systems and business processes hourly, rather than daily. AI summary
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Procore, a construction management software provider, replaced its previous analytics product with OpenSharing on Databricks, doubling the catalog of extractable data for its customers. This change lifted the once-a-day refresh ceiling, enabling customers to pull data hourly, and doubled data availability, accelerating new product experiences and giving real estate facility owners, general contractors, and specialty contractors a single trusted view of every job. AI summary
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Databricks has developed a platform that enables businesses to build voice agents that can understand customer context, adapt to individual needs, and make fast AI decisions in real-time, creating value through personalized customer interactions. AI summary
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Vital Skills, an open-source Agent Skills catalog, equips Genie Code with healthcare and life sciences (HLS) domain expertise, enabling it to apply specialized tools and best practices. This integration improves Genie Code's performance on in-scope tasks, resulting in increased consistency, accuracy, and rigor, with fewer steps and checks missed. AI summary
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Improving document extraction accuracy with ai_extract and MLflow Prompt Registry involves using schema descriptions for field-level rules, global instructions for document-level logic, precision mode for structural complexity, and citations to diagnose errors. A repeatable workflow can be built using Genie Code, which automates the core of this workflow, scoring extraction field by field against labeled data, proposing schema and instruction fixes as reviewable diffs, and re-running the eval to show before-and-after results. AI summary
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Databricks Lakehouse Federation allows querying on-premises databases directly from Databricks with standard SQL, without moving data, and enables subscription to third-party data from the Databricks Marketplace, also without data movement. This approach lets users create federated connections to external databases, query them directly, and combine the data with third-party data in a single SQL query, without persisted intermediate objects. AI summary
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Migrating from Google BigQuery to Databricks Lakehouse requires a strategic framework, encompassing a phased approach with governance, technology, and people elements. This framework enables organizations to unify BI, ETL, and multi-model AI workloads into one environment, reducing operational overhead and improving predictability. AI summary
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Lakeflow Connect unifies productivity and AI data from various sources, including Atlassian, Microsoft, Google Workspace, GitHub, and major AI providers, into a single governed foundation in Databricks. This allows for seamless ingestion of unstructured and structured data, enabling AI agents to provide grounded answers and powering analytics and BI dashboards. AI summary
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Databricks Free Edition is a perpetually free version of the Databricks Data + AI Platform, designed specifically for learning and non-commercial use, allowing students and educators to use the same tools professionals use in production. It runs in the browser with no cloud contract or credit card required, supporting an entire data + AI curriculum, from introductory data science and statistics through data engineering, BI, machine learning, AI, and capstone projects. AI summary
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Databricks AI Search now natively supports Streaming Tables, Materialized Views, and Managed tables using Iceberg v3+, enabling real-time intelligence, personalized experiences, and cross-engine AI applications without the need for separate synchronization pipelines. AI summary
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The media industry is shifting from a focus on attention to predicting and proving outcomes, with performance becoming a leading indicator. To achieve this, a unified, governed data and AI foundation is necessary, built on four core elements: context, control, cost, and choice. This foundation enables agents to act intelligently, providing personalized engagement and measuring impact. AI summary
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Real-time energy trading and analytics on Databricks combines Spark Structured Streaming Real-Time Mode, Lakebase, Databricks Apps, and Unity Catalog to provide a unified platform for data, analytics, and AI, streamlining the process of generating trade signals and monitoring risk in real-time. AI summary
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Declarative pipelines can be validated before they run, catching structural errors early and reducing compute time by 34X compared to procedural pipelines. This validation is made possible by Spark's ability to check declarative pipelines against their schema, dependencies, and ordering before execution. AI summary
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To effectively manage AI costs, enterprises must shift from "tokenmaxxing" to "valuemaxxing," measuring the impact generated per dollar spent on AI, and adopt a centralized system for cost management, attribution, and monitoring. This requires granular visibility into AI spend, cost attribution to individual users or teams, and intelligent routing of tasks to lower-cost models or harnesses when possible. AI summary
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