News from the Databricks ecosystem.
Posts from databricks.com, MLflow, and dbt Labs — three trusted sources covering the platform, the open-source projects around it, and the data tooling layer most teams pair with it. Summarized for scanning.
This week
8 articlesBuilt In, Not Bolted On: What AI-Native Actually Means in Cybersecurity
Databricks now offers a truly AI-native cybersecurity solution, architected with intelligence at its core and leveraging proprietary telemetry for a defensible advantage. This approach prioritizes defining shared outcomes for cross-functional alignment, rather than simply selecting shared tools.
Operationalizing AI for public sector fraud prevention
Databricks now offers a scalable approach to AI-powered fraud prevention for public sector agencies. Learn how clean data, automation, and real-time insights can be integrated into daily workflows to improve decision-making.
From months to minutes: Building real-time clinical data pipelines with natural language
Databricks and Redox now enable real-time clinical data pipelines from EHRs to Unity Catalog with natural language prompts, reducing integration time from months to minutes. This partnership allows AI outputs to be written back into the EHR in real time, transforming Databricks into an operational layer for point-of-care interventions.
Agentic Data Engineering with Genie Code and Lakeflow
Genie Code, an autonomous AI partner for data engineers, is now integrated directly into Lakeflow. Data engineers can leverage Genie Code within Lakeflow's Pipeline Editor and Jobs for the full data engineering lifecycle, from development and orchestration to monitoring and debugging.
Securely send first-party conversion signals with Snapchat Conversions API on Databricks Marketplace
Snapchat Conversions API is now on Databricks Marketplace, letting you send web, app, and offline events from your Lakehouse directly to Snapchat. Improve Event Match Quality and campaign measurement by deploying a pre-built notebook for governed, server-side event delivery.
See What Your AI Sees: Multimodal Tracing for Images, Audio, and Files
Databricks now supports multimodal tracing for images, audio, and files, allowing you to visualize and interact with these artifacts directly within your traces instead of opaque base64 strings. This enhancement improves debugging for GenAI agents, reduces storage costs, and speeds up trace queries by avoiding direct storage of large multimedia strings.
How leading tech companies are killing the builder’s tax with Lakebase
Databricks Lakebase is helping leading tech companies eliminate ETL and reverse ETL by unifying operational and analytical data. This enables real-time intelligence for apps and AI systems, operating directly on fresh, low-latency data.
Inside one of the first production deployments of Lakebase: LangGuard's agentic workflow governance engine
LangGuard's agentic workflow governance engine, one of the first production deployments of Lakebase, extends Unity Catalog and AI Gateway with runtime enforcement for autonomous AI agents. Lakebase provides the elastic, low-latency operational data layer for LangGuard's GRAIL™ data fabric, enabling real-time policy evaluation without impacting agent performance.
Last week
16 articlesThe next generation of Databricks Genie
Genie now answers questions beyond Genie Spaces, connecting to external knowledge stores like Google Drive and SharePoint. This next generation of Genie, previously Databricks One, is available on web and native mobile apps.
Model Risk Management in 2026: A Banker’s Guide to the Revised Interagency Guidance
The new interagency guidance for model risk management, effective April 17, 2026, shifts to a risk-based, principles-driven framework, demanding a unified, governed lifecycle for all models, including GenAI, with evidence of governance generated automatically. Databricks offers a reference architecture to meet these revised expectations, integrating model development and validation into a single, auditable process.
OpenAI GPT-5.5 + Codex, now available and fully-governed in Databricks
GPT-5.5 and Codex are now natively available in Databricks, fully governed by Unity AI Gateway for permissions, cost controls, guardrails, and observability. This enables agent building with GPT-5.5 and natural language querying of enterprise data via Genie.
How Obie cut compute costs by 30%, reclaimed engineering hours, and built stronger governance
Databricks shipped dbt Fusion, a new engine and state-aware orchestration for dbt. Learn how Obie used it to cut compute costs by 30%, reclaim engineering hours, and build stronger governance.
Operational databases: How they work and when to use them
Databricks is introducing the "Lakebase," a new open architecture combining transactional database speed with data lake flexibility and economics, designed to overcome the limitations of traditional operational databases for modern unstructured data and AI workloads. This allows for real-time processing and concurrent transactions directly on the data lake, eliminating slow ETL pipelines and supporting diverse data types.
Databricks partners with OpenAI on GPT-5.5
GPT-5.5 and Codex are coming soon to Databricks, governed by Unity AI Gateway, and cut OfficeQA Pro errors nearly in half. This partnership with OpenAI brings advanced models directly to Databricks users.
Announcing the Public Preview of Lakeflow Designer
Lakeflow Designer is now in Public Preview, offering a visual, no-code, AI-native interface for data preparation and analysis directly within Databricks. It leverages Unity Catalog for governance and generates production-ready code, providing step-by-step data previews for easier review of AI-generated transformations.
Are LLM agents good at join order optimization?
LLM agents can improve Databricks join order optimization, achieving 1.3x latency reduction in 80% of cases by reasoning through runtime statistics. This prototype demonstrates LLM agents' potential to act as data-driven DBAs, addressing cardinality misestimation challenges in complex SQL queries.
How conversational analytics removes the BI bottleneck
Databricks Genie and Lakebase are transforming BI by enabling conversational analytics with enterprise context, providing actionable insights beyond traditional dashboards. Operationalizing trusted AI-powered analytics, built on robust governance and semantic layers, is now crucial to avoid a competitive gap.
How to transform document activation workflows with Genie and Agent Bricks
Databricks shipped a solution combining AI/BI Genie, Agent Bricks, and Unity Catalog to automate document activation workflows. This enables multi-agent orchestration for extracting, processing, and activating data from diverse documents, improving efficiency and governance.
Using dbt with Databricks: Architecture decisions that determine success
Databricks users who skip dbt incur compounding costs. A solution architect explains key architecture decisions and when to act to ensure success.
Beyond the spreadsheet: how Databricks is delivering the modern CFO in Financial Services
Databricks now offers a unified architecture for Financial Services CFOs, integrating real-time data, AI modeling, and governance to eliminate data fragmentation and slow reporting. This enables a shift from reactive reporting to strategic finance, with benefits like drastically reduced regulatory reporting times and AI-powered natural language querying of complex financial data.
AI App Development: Guide To Building AI-Powered Apps
Databricks Apps and Lakebase are purpose-built platforms that streamline AI app development by eliminating infrastructure, authentication, and data synchronization overhead. A structured process covering model strategy, prompt design, agent orchestration, and data prep, combined with rigorous quality gates, ensures production-grade AI applications.
Structuring AI Evaluation and Observability with MLflow: From Development to Production
MLflow now offers enhanced tools for structuring AI evaluation and observability, including new APIs and UI features for logging LLM calls, prompts, responses, and metrics. This enables practitioners to systematically track, compare, and analyze model performance and behavior across development and production, facilitating iterative improvement and robust monitoring.
dbt Labs Wins a 2026 Google Cloud Partner of the Year Award
dbt Labs won a 2026 Google Cloud Partner of the Year Award, recognized for empowering thousands of Google BigQuery users to deliver trusted analytics and AI at scale.
Enforce Content Policies at the Gateway with AI Gateway Guardrails
MLflow AI Gateway now supports configurable guardrails, using LLM judges to block or sanitize harmful content, PII, and custom policy violations. Enforce content policies at the gateway before requests reach your users or models.
Week of Apr 13
5 articlesMeet Antigravity: Google’s agentic IDE enters the dbt orbit
Antigravity, Google's new agentic IDE, now integrates with dbt. This pairing promises to significantly improve developer productivity, potentially giving you your weekends back.
Exploring dbt and Google with AI agents
Learn how to build your first ddbt agent by plugging AI into a dbt project. This practical guide explores what happens when AI agents interact with dbt and Google.
Tableau and dbt MCPs together
Tableau and dbt MCPs can now be configured together in a single file. Learn how this pairing unlocks impact analysis, metric reconciliation, and more.
New dbt Labs Report Finds AI-driven Acceleration is Outpacing Trust and Governance
A new dbt Labs report finds AI is accelerating data workflows, but governance and trust aren't keeping pace. This press release details the findings on how AI-driven acceleration is outpacing trust and governance.
From raw data to trusted AI: What dbt is bringing to Google Cloud Next
dbt is bringing its capabilities to Google Cloud Next, showcasing how dbt + BigQuery powers trusted, AI-ready analytics. Visit Booth #6606 to learn more.
Week of Apr 6
3 articlesMistakes I made as the head of analytics (and what I’d do differently now)
A former head of analytics on the 6 mistakes he made with dbt—and what he'd do differently now.
How to Prevent Runaway Agent Costs with MLflow AI Gateway
MLflow AI Gateway now helps prevent runaway agent costs by providing visibility into which part of your agent is driving up costs. This allows you to identify and address cost drivers before investing in the wrong optimizations.
Tired of Reviewing Traces? Meet Automatic Issue Detection for Your Agent
Automatic issue detection for your AI agent is now available, eliminating the need for manual trace reviews. This new feature helps you act on your observability data, improving the user experience beyond just recording logs, metrics, and traces.
Week of Mar 30
2 articlesOperationalize analytics agents: dbt AI updates + Mammoth’s AE agent in action
Databricks now supports operationalizing analytics agents with dbt AI updates and Mammoth’s AE agent. Learn how to build context for LLM models using dbt and MCP servers.
How AI is reshaping the way data practitioners work
AI is reshaping data practitioner workflows, and The View on Data podcast hosts share what's shifting and what isn't. Tune in to understand the concrete impacts of AI on your daily data work.
Week of Mar 23
3 articlesIntroducing the dbt Community Champions Program
Building the future of analytics engineering, together.
Harness Your OpenHands Agent with AI Observability and Governance
MLflow now supports tracing, evaluating, and governing OpenHands agents, capturing every step of their autonomous operations. This enables practitioners to monitor agent actions, assess output quality, and manage LLM costs effectively.
Testing and Refining Claude Code Skills with MLflow
MLflow tracing and LLM judges can now test Claude Code skills. This enables a self-improvement loop where Claude Code refines its own abilities.
Week of Mar 16
8 articlesTracking and Debugging AI Safety Evaluations with Inspect AI and MLflow
Inspect AI evaluations now integrate with MLflow for experiment tracking and execution tracing via the inspect-mlflow package. This enables practitioners to track and debug AI safety evaluations using familiar MLflow tools.
MLflow Workspaces: Shared Deployment Without Separate Servers
MLflow Workspaces are now available, enabling shared MLflow deployments across multiple teams by adding a logical organization and permission layer. This allows teams to scope experiments, models, traces, prompts, AI Gateway resources, and artifacts within their own workspace.
Types of data transformations for machine learning
Databricks practitioners can explore key data transformation types for machine learning, including cleaning, scaling, feature engineering, and validation. This Pulse post details these transformations to help optimize ML workflows.
What are the most common data pipeline architecture patterns?
Databricks practitioners can explore common data pipeline architecture patterns, including ETL, ELT, batch, streaming, and semantic layers. This post details the most prevalent patterns to help you understand their applications and differences.
Control LLM Spend with AI Gateway Budget Alerts and Limits
AI Gateway now supports budget policies to control LLM spend with alerts and request limits. Set spending thresholds, receive webhook alerts, and automatically reject requests when budgets are exceeded.
Your Agents Need an AI Platform
MLflow 2.12 ships with new features for building and managing AI agents, including enhanced logging for agent traces, evaluation tools, and versioning capabilities. Leverage MLflow as your unified platform for developing, deploying, and governing reliable AI agents in production.
How a semantic layer prevents AI hallucinations in analytics
Learn how a semantic layer gives AI systems the consistent, governed foundation they need.
How ETL tools fit into modern data pipeline architecture
Explore ETL vs ELT and how modern transformation tools power scalable data pipelines.
Week of Mar 9
7 articlesThe Iceberg ecosystem today
Iceberg is production-ready, and this post details what Databricks practitioners can realistically expect when running on top of it today. Anders Swanson explains the current state of the Iceberg ecosystem.
Why metadata management is critical for modern data teams
Metadata management improves discovery, governance, performance, and trust in modern data systems.
Why ETL is still essential for modern data pipelines
ETL remains essential for modern data pipelines, consolidating fragmented data, enforcing quality, and satisfying compliance requirements. These core benefits are why ETL is still a critical component for modern organizations.
How a global investment firm reduced runtimes by 30–40% with the dbt Fusion engine
The dbt Fusion engine and State-Aware Orchestration helped a global investment firm reduce runtimes by 30-40% in 3 months. Learn how NBIM achieved these gains without heavy optimization efforts.
Effective strategies to enhance data quality management
Improve data quality with testing, metrics, automation, and a scalable governance framework.
Data movement patterns explained (ETL, ELT, CDC & more)
ETL, ELT, batch, CDC, reverse ETL—learn the key data movement patterns and when to use each.
How data transformation improves data quality and analysis
Learn how transformation methods improve data quality, consistency, and analysis at scale with dbt.
Week of Mar 2
4 articlesEffective strategies to improve data quality across your organization
Databricks practitioners can improve data quality with proven strategies for testing, governance, and scalable analytics workflows. Learn how to implement these effective strategies across your organization.
How AI improves data lineage at scale
Discover how AI accelerates data lineage with automated docs, testing, and scalable governance.
Agent Trace Evaluation with TruLens Scorers in MLflow
Evaluate agent traces with TruLens GPA framework through mlflow.genai.evaluate(). Score agent plans, tool calls, and reasoning directly within MLflow.
Benchmark Your Way to Better RAG and Agents:Tuning Vector Search with MLflow
High-level summary: problems, approaches, and takeways for better RAG with MLflow
Week of Feb 23
6 articlesDeterministic Safety Checks in MLflow with Guardrails AI
MLflow evaluation pipelines now support fast, deterministic safety validation with Guardrails AI scorers. This enables adding safety checks without requiring an LLM.
Ship LLM Agents Faster with Coding Assistants and MLflow Skills
MLflow now provides coding assistants with the required feedback loop to build better LLM agents. Trace, analyze, fix, validate, and repeat to ship LLM agents faster.
Enterprise-Scale MLflow Operations and Security Practices at LY Corporation
How LY Corporation Uses MLflow: An Overview
Deploy MLflow Models to Serverless GPUs with Modal
MLflow models can now be deployed to Modal's serverless GPU infrastructure. This enables auto-scaling and streaming predictions for your MLflow models.
Introducing MLflow AI Gateway: Governed, Observable Access to LLMs
MLflow AI Gateway provides a single, secure endpoint for all LLM providers, complete with usage tracking and native tracing. This new feature offers governed, observable access to LLMs for Databricks practitioners.
Multi-turn Evaluation & Simulation: Enhancing AI Observability with MLflow for Chatbots
MLflow 3.10 now supports multi-turn evaluation and conversation simulation, enabling scoring of full conversations and reproducible testing of agent changes. This helps catch failures that only emerge across multiple turns, improving chatbot observability.