Loading…
Databricks
Data and AI platform for data engineering, analytics, machine learning, and generative AI.
- 542 posts summarised
- latest
The Good Stuff
Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.
From their blog
Latest articles
Introducing Arrow UDFs in PySpark: A Faster, Leaner Replacement for Pandas UDFs
Apache Spark’s Pandas UDFs improve Python UDF throughput through Arrow-based serialization and batching, but Pandas/Arrow conversion can add copies, limit zero-copy behavior, and weaken support for complex datatypes. Native Arrow UDFs, introduced in Databricks Runtime 18.0, operate directly on Arrow data without converting inputs to Pandas or NumPy objects, preserving columnar layout and enabling vectorized processing through Arrow’s compute and memory model. The new @arrow_udf decorator supports scalar, aggregate, and table functions, while type-hinted @udf definitions, mapInArrow, and applyInArrow extend the interface to DataFrame and grouped operations; iterator-based modes can amortize initialization costs. In a comparison, an Arrow UDF ran about 10% faster than a Pandas UDF and used about 40% less memory, with better complex-datatype support.
Automate Data & KPI Monitoring with SQL Alerts
Databricks SQL Alerts is generally available as an automated alternative to manually checking dashboards and rerunning monitoring queries for business KPIs, pipeline health, and data quality. Teams define a SQL query, evaluation condition, schedule, and notification destinations, then receive alerts when results cross configured guardrails; standalone alerts can also run within Lakeflow Jobs. GA supports authoring in the SQL editor, notifications through email, Slack, PagerDuty, Microsoft Teams, or webhooks, and production management through Git, Declarative Automation Bundles, APIs, Terraform, and SDKs. The post’s revenue example compares yesterday’s result with a seven-day average and triggers when revenue_pct_change is below -5%, while the SQL Alert task can expose OK, TRIGGERED, or ERROR to downstream pipeline logic.
How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase
Card fraud operates in seconds, while organizations need to score and block suspicious transactions before settlement without adding a separate streaming stack. This Solution Accelerator presents an open-source, end-to-end implementation on Databricks, covering transaction ingestion, Spark Real-Time Mode processing, fraud scoring, Lakebase feature serving, and a Databricks Apps monitoring dashboard. RTM processes streaming workloads in milliseconds and targets sub-300ms latency, while Lakebase supplies a managed, serverless Postgres serving layer with sub-millisecond reads. The design keeps batch ETL, training, streaming, and governance within the same platform. Deployment progresses from a synthetic rate-source quick start to Kafka-based parsing, stateful per-card velocity tracking with TTL, enrichment, weighted scoring, routing, and optional MLflow-trained RandomForest scoring, followed by a Streamlit app for live decisions.
How Databricks Genie improves supply chain visibility with real-time AI analytics
Supply chain leaders often have extensive operational data yet still respond reactively because predictive signals such as supplier lead-time trends, inventory velocity, weather, and commodity prices remain siloed and difficult to synthesize. Databricks Genie is presented as a plain-language intelligence layer that lets leaders interrogate operational and external data in the flow of work rather than relying on analyst-led BI sessions. Users can ask questions combining supplier tiers, lead-time changes, inventory coverage, contract terms, and production schedules, then follow with what-if queries about financial exposure if conditions worsen. The post says Genie returns answers in seconds, supports near-real-time monitoring and proactive alerting, and enables shared, governed answers for procurement, operations, and finance, shifting decisions from reactive reporting toward earlier evidence-based action.
A CFO’s guide to managing value-based care financial performance
Value-based care changes healthcare finance by tying payment to outcomes, cost efficiency, and equity rather than service volume, while shifting greater financial risk to providers under ACO, bundled, and capitated contracts. A CFO must therefore track attributed populations, PMPM cost trends, utilization, clinical outliers, quality thresholds, and contract performance against benchmarks. Databricks Genie is presented as a conversational interface over integrated attribution, claims, clinical, and benchmark data, allowing questions such as PMPM divergence among high utilizers and DRG drivers to be answered in real time. The source also identifies incorrect attribution and risk-adjustment coding errors as major threats, and describes predictive risk stratification and quality-gap forecasting as ways AI can support earlier intervention.
Stop rogue AI: How Unity Catalog secures your agent actions
Agentic AI systems connected to external tools can take destructive production actions while operating within delegated authority, yet without restrictions on tool selection or records of their calls. Unity Catalog now governs MCP tools with fine-grained access control, service policies, and payload logging, while Unity AI Gateway enforces decisions in real time. Policies are SQL functions receiving actor and context information; they can allow, deny, or require user consent, inspect arguments and caller properties, and block calls before execution. Every tool call is recorded in a Unity Catalog-managed Delta table with the tool name, arguments, result, user identity, and allow-or-deny status, making the activity queryable with SQL. The service policies and payload logging are available as a Gated Beta, with early access offered through Databricks account teams.
Databricks context engineer associate: the industry’s first certification for reliable AI agent systems
Databricks announces the Databricks Certified Context Engineer Associate, described as the industry’s first certification purpose-built for context-aware AI systems. It assesses whether practitioners can design, assemble, and govern the information AI agents receive at inference time, including system prompts, retrieval, memory, external tools, context-window management, and governance. Candidates work with Databricks technologies such as AI Search, Lakebase, MLflow, MCP, and Unity Catalog, while advanced coverage includes multi-agent systems, long-horizon workflows, and evaluation of context decisions. The beta exam will be available free to Data + AI Summit attendees, who may take it once; beta results are expected in 6–8 weeks.
How to safeguard AI workloads with Unity AI Gateway Guardrails
Unity AI Gateway’s beta LLM Guardrails extend earlier Gateway guardrails with LLM-powered evaluation, pre-built protections, and tunable custom rules for AI security, compliance, and sensitive-data protection. An Acme marketing scenario maps policies to PII redaction on inputs, jailbreak and prompt-injection blocking, unsafe-content blocking on outputs, and a custom rule that blocks competitor references. Teams configure these guardrails on an endpoint, optionally use Log mode, select evaluator endpoints, and test representative requests while tracking activity in inference tables. Built-in controls behave as expected in the examples, while the custom rule initially performs unreliably until a more specific prompt and gpt-5-4-mini evaluator improve triggering without degrading other tests. Inference tables record request outcomes, evaluator calls, latency, statuses, token counts, and shared request_id values for auditing and refinement.
What’s new in Unity AI Gateway: service policies, guardrails, observability, and cost controls for AI agents and MCPs
Unity AI Gateway is expanding runtime governance for production AI agents, model calls, and MCP tool interactions as teams face rising costs, unclear behavior, and limited control. The Beta adds LLM-based guardrails, token-level cost attribution with per-user alerts and hard budget limits, payload logging, and MCP service policies. Guardrails use a model and prompt to evaluate inputs, outputs, or both in real time, while inference tables and Unity Catalog system tables centralize governed records of usage and interactions. For MCPs, administrators can define SQL policies as Unity Catalog functions using agent identity, user context, and request parameters to constrain tool access and sensitive actions. The capabilities are available in Beta, with payload logging and service policies offered through gated enrollment, and are intended to improve observability, compliance, and cost control.
MemEx: A Programmable Scratchpad for LLM Agents
MemEx addresses the context-window bottleneck in LLM agents, where large tool outputs persist in the shared prompt even when later steps need only a small portion. It gives the agent a persistent, typed Python kernel: tool results remain as objects in scope, code can filter, summarize, and compose them, and only printed values are materialized as context tokens. MemEx preserves existing Tool Calling schemas while adding typed submit(), persistent state, backend-agnostic execution, and asynchronous spawn_agent() sub-agents, enabling multi-step workflows and parallel trajectory analysis. Across nine-model evaluations on OfficeQA Pro and Enterprise Structured Retrieval, it was reported as better and cheaper than parallel structured Tool Calling; examples include roughly 10-point OfficeQA gains for some models and Qwen structured-retrieval accuracy rising from 18% to 36% and 20% to 38% at lower token cost.
How Deutsche Börse built a generative AI tool to tackle the large-scale migration of Zeppelin notebooks to Databricks
Deutsche Börse Group is migrating a high volume of Zeppelin notebooks used by more than 2,000 users from Cloudera to Databricks, after Cloudera’s 2027 Zeppelin decommissioning and the group’s move to cloud analytics. Because the notebooks combine SQL, Python, custom interpreters, data-source references, visualizations, widgets, and scheduling logic, the team rejected fully rule-based rewriting. Its Zeppelin to Databricks Notebook Converter, delivered as a Databricks App, deterministically maps paragraphs to cells, translates interpreter syntax, reformats metadata as .ipynb JSON, and preserves original content. A generated, environment-specific prompt then gives Genie context about interpreters, data sources, and configuration patterns so it can reconstruct logic, ask clarifying questions, and handle the variable work. The hybrid workflow reduced redevelopment to 15–20 minutes per notebook, while keeping SQL, Python, visualizations, widgets, Oracle and HDFS references, scheduling, and custom business logic untouched; large-scale testing is next.
Announcing the Databricks analytics engineer learning pathway
Databricks is launching the Analytics Engineer Learning Pathway, a curriculum for SQL practitioners that covers data modeling, pipelines, metrics, and Genie spaces on the lakehouse. The pathway teaches learners to transform raw data into governed, AI-ready semantic models and metric views, the foundation for analytics, dashboards, and AI agents. Courses cover Analytics Fundamentals, production SQL ETL with Materialized Views, Streaming Tables, AUTO CDC, and Lakeflow Jobs, plus data modeling with Delta Lake and Unity Catalog. The curriculum also addresses metric views, Genie spaces, governance with Unity Catalog permissions and ABAC policies, and Spark Declarative Pipelines with expectations, event logs, and metrics. Courses are available in self-paced and instructor-led formats through Databricks Academy, with the full pathway included in active learning subscriptions.
The question your commercial data should already be able to answer
In ATTR-CM, a progressive and often fatal form of heart failure, 70 to 80 percent of patients do not know they have the disease, creating a need for better commercial prioritization than static call lists provide. Databricks and Veeva position embedded Genie agents and AI/BI dashboards in Veeva Vault CRM as a way to connect commercial data bidirectionally and answer role-specific questions inside existing workflows. Sales reps can see geographic HCP views with suspected patient counts, formulary scores, office availability, NRx-weighted priorities, and talking points, then rebuild a day after a cancellation. MSLs can generate cited pre-call briefs from approved sources such as Veeva Link, PubMed, ClinicalTrials.gov, and ASNC guidelines. Territory managers receive personalized views of call patterns, unworked signals, and dormant HCPs, while Unity Catalog provides shared access, lineage, and compliance governance.
PipelineIQ: Forward‑Looking Sales Intelligence That Drives Action
PipelineIQ addresses the administrative drain and unreliable revenue predictability caused by incomplete, inconsistent, and backward-looking CRM data in B2B sales. Rather than build another forecasting system, it applies prescriptive analytics to identify forward signals and produce immediate actions for reps and managers. Built on Databricks, it uses Foundation Model APIs, Unity Catalog, Delta Lake, and AI/BI Dashboards; its confidence scorer sends CRM fields to ai_query() with a Gemma 3 12B model, scores eight MEDDPICC dimensions from 0–10, and limits missing fields to scores of 3 or below. Weighted confidence is refreshed daily, with a fail-safe override to Low when a use case has more than three active blockers. Dashboards and Genie queries connect evidence-based risk explanations, remediation steps, and portfolio views to sales execution.
Backstage with Lakebase, part 2
Part 2 shows how moving Backstage’s operational Postgres database to Databricks Lakebase brings security, auditing, masking, and cost controls under Unity Catalog. The proof of concept used Lakehouse Federation to expose the catalog as the lakebase_bs foreign catalog, replacing cross-service RDS investigations with Unity Catalog grants and system.access.audit records. Audit events identify OAuth users and source IPs, while billing attributed 31.6130 DBU to production and 0.0107 DBU to a dropped test branch. Branch-level attribute masking propagates automatically to feature, CI, and QA copies, while the post cites a Perforce report stating that 60% of organizations experienced breaches or theft in non-production environments. It also introduces LakebaseOps, with three agents and seven scheduled jobs, and Lakebase MCP, exposing 46 governed tools across four profiles, so DBAs can design policies and workflows instead of handling repetitive provisioning.
Expanded interoperability with Unity Catalog Open APIs
Unity Catalog’s expanded Open APIs address data silos by letting organizations use multiple compute engines while retaining centralized governance and a single copy of data. In beta, Apache Spark, Apache Flink, and DuckDB can create, read, write, and stream to or from UC managed Delta tables, with catalog commits providing serialized commits, transactional safety, and auditability. Delta Kernel, an open source Java and Rust library, abstracts low-level protocol details, helping connectors integrate external writes with catalog-managed commits while Predictive Optimization continues to run on accessed tables. Credential vending, now GA for tables, issues short-lived, scoped cloud credentials and supports M2M OAuth plus automatic refresh; volume credential vending is in Public Preview for unstructured data. The roadmap includes functionality for fine-grained row- and column-level ABAC on external reads, while external managed-table access remains in beta.
From manual to autonomous: how AI agents are transforming electric grid operations
Electric utilities are facing rising demand, retiring generation, extreme weather, aging infrastructure, and fragmented operational data that manual processes cannot manage at scale. AI agents are presented as a human-centered alternative that synthesizes heterogeneous data, learns from outcomes, and progresses from human-approved recommendations to exception-based control and eventually autonomous operations within defined parameters. Hawaiian Electric used a Retrieval Augmented Generation proof-of-concept with Databricks AI Search, Unity Catalog, and Lakeflow Declarative Pipelines to query regulatory documents and provide page-specific citations. The system reduced response times from five minutes to five seconds and was implemented in two weeks, while the article describes broader potential for predictive maintenance, outage response, load forecasting, and customer service.
Data quality is the AI strategy
NYU Langone Health’s AI strategy starts with data quality, arguing that healthcare AI cannot be reliable when source data is fragmented or inconsistent. The institution standardized on common transactional platforms, including one electronic health record and one ERP system, established authoritative data sources, and fixes data at the source rather than mapping it in the warehouse layer. Its Databricks-based unified data and AI platform, with Unity Catalog, supports clinicians, analysts, scientists, and corporate users across care, operations, and research, while real-time feeds power emergency-room decision-support models. Mherabi also describes a three-layer analytics model: structured visualizations, conversational tools such as Genie, and answers delivered in formats suited to the user. The stated conclusion is that upstream data discipline, governance, literacy, and adaptable platforms provide the foundation for trustworthy AI and timely clinical insight.
The Rosetta stone of CPS: Claroty’s AI-powered library
CPS asset identification is difficult because 88% of CPS assets do not transmit an exact product code and 76% use product codes that differ from the vendor's official records. Claroty's AI-Powered CPS Library addresses this entity-resolution problem with a hybrid architecture that combines classic matching, statistical inference, generative AI, NLP, reasoning agents, and human-in-the-loop review to reconcile protocol data, vendor records, manuals, and vulnerability information. Its Databricks-based lakehouse uses Delta Lake, Unity Catalog, Spark pipelines, Model Serving, MLflow evaluations, Lakebase, and Databricks Apps to maintain governed, auditable mappings across a catalog of more than 17 million assets. The post reports 25% improved vulnerability-attribution accuracy and says early tests gave 56% of analyzed devices new or updated recommendations for outdated firmware, while low-confidence mappings remain subject to expert review.
Clinical operations intelligence belongs on the Lakehouse
The Site Feasibility Workbench addresses fragmented clinical operations data by placing a site-selection decision-support application, its models, and data on Databricks. Released as a fully open-source Databricks App, it combines a FastAPI backend, React frontend, Unity Catalog-governed data, Lakebase operational state, and AI/BI Genie within the workspace. TA-segmented LightGBM models use the organization’s CTMS, EDC, and IRT history alongside real-world evidence, patient access, qualification, Open Payments, and protocol-execution signals to rank sites. Each prediction stores SHAP attributions in a governed Unity Catalog Delta table, while MLflow tracks training runs and model versions; saved shortlists persist to Lakebase and Genie answers governed-data questions. The six-step workflow supports protocol selection through final shortlisting, including diversity considerations; the release is a decision-support layer rather than a source of record, and deployment reportedly takes about 30 minutes before security review and validation.
Topics they write about
All topics →Weekly digest
The best engineering reads, once a week.