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
Unified context: The missing layer for enterprise AI coworkers
Enterprise AI assistants often produce fluent answers yet fail to improve forecast calls, deal reviews, and operational standups because decision context is scattered across systems, teams, and competing definitions. Genie One addresses this by using a shared context layer spanning Databricks data, documents, SaaS applications, and operational systems, allowing questions and follow-up work to retain business meaning. Genie Ontology organizes terms, metrics, entities, and relationships into a living knowledge graph, learning from data, dashboards, queries, documents, and connected applications while ranking definitions and signals using usage and certified-asset links. Together with Unity Catalog, it applies permissions, certified data, shared definitions, and governance controls to answers, actions, and agents. The stated outcome is faster movement from decision preparation to action, with less manual reconciliation while preserving accuracy and control.
What happens in the milliseconds after you tap pay
At checkout, fraud scoring must combine transaction inference with customer-specific rules while keeping latency low enough for an interactive payment. The retail-app sample pairs a FastAPI backend and React frontend in a Databricks App with Model Serving route optimization and Lakebase Postgres, where the model retrieves historical features and the backend reads profile controls. A transaction is scored first, then checked against daily spending, international-transaction, and country rules; pooled OAuth-authenticated connections and token-rotation handling avoid repeated handshakes and stale credentials. In the supplied benchmark, route-optimized calls reached 27.2 ms p50 and 37.3 ms p95 end-to-end, with feature lookup at 8.9 ms p50, CatBoost inference at 0.4 ms, and network overhead at 17.4 ms p50; actual performance varies by model.
How Unity Catalog managed tables bring interoperability, performance, and unified governance to the Lakehouse
Unity Catalog external access to Unity Catalog managed Delta tables is now in Public Preview, allowing external engines to create, read, and write while governance remains centralized. The announcement addresses the previous multi-engine trade-off: external tables enabled access but lacked managed-table performance optimizations and governance guarantees. Catalog commits coordinate writes through Unity Catalog, making it the source of truth for table state and enabling safe external writes, multi-statement transactions, and auditing of external operations. Predictive Optimization cleans storage, collects query statistics, and selects Liquid clustering columns as query patterns change; the post says these capabilities can deliver up to 50% storage cost savings and 20x faster queries. Support includes Spark, Flink, Starburst, DuckDB, and StreamNative, with open APIs and Delta Kernel extending integrations across Databricks UC and UC OSS.
Introducing Apache Spark 4.2
Apache Spark 4.2 extends the engine’s role in modern data and AI workloads with governed metrics, vector and top-K primitives, Arrow-first Python execution, native change data capture, and stronger streaming foundations. Metric views provide shared business definitions, while Spark Connect uses gRPC and Arrow to let remote clients submit logical plans without a full Spark runtime. Spark SQL adds vector similarity functions, NEAREST BY, geospatial types, sketches, and time-series features; Python interoperability includes Arrow UDFs and can move Spark DataFrames into supported Arrow-native tools without copying or serializing underlying data. Spark Declarative Pipelines adds Auto CDC for SCD Type 1 targets, while Data Source V2 standardizes change streams through CHANGES and expands row-level operations and schema evolution. The release also includes operational updates such as Web UI modernization, Kubernetes improvements, JDK 25 support, and dependency upgrades.
Inkling model from Thinking Machines Lab now on Databricks
Databricks announces that Inkling, Thinking Machines Lab’s first open-weights model, is available to enterprise customers through the Unity AI Gateway. The model is positioned for coding and agentic reasoning workflows, supports multi-modal inputs, and can be applied to enterprise data, including proprietary codebases, internal documentation, and domain-specific data. Unity AI Gateway provides centralized security, permissions, audit logging, policy enforcement, cost controls, budgets, and observability, while data remains within the governed environment; Inkling is invoked through a REST API, with SQL query support planned. Teams can try it in AI Playground, deploy a governed endpoint, connect coding agents such as Cursor, OpenCode, or Pi, and build agents with Agent Bricks, with open weights enabling customization and inference-cost optimization without per-token API pricing.
AI-Enabled Advisory Services for Higher Education
Higher-education call centers face costly, limited-coverage monitoring of advisor conversations and brittle, slow methods for identifying student concerns from transcripts. The proposed workflow deploys OpenAI Whisper on Databricks Model Serving, applies AI Functions for sentiment, topics, intent, and rubric scoring, and uses Unity Catalog to govern the resulting data. For advisor quality, an LLM-as-a-judge evaluates every transcript against a reference-table rubric, returns a weighted 1–5 overall score and per-criterion scores, and routes flagged calls for targeted QA review instead of random sampling. For student insights, quarterly transcript enrichment feeds an Agent Bricks Knowledge Assistant for cited reasoning over raw calls and a Genie Space for structured trend queries, while LangGraph orchestrates UC SQL functions as tools. Together, these components let non-technical advisors, mentors, and QA managers query student interactions without reaching out to a data SME.
Data-Native AI Agents: Why Agents Must Move to Your Data
Enterprise AI pilots often move data into separate vector databases, SaaS LLMs, or serving layers, creating governance gaps, compounded latency, fragmented costs and observability, and duplicated lifecycle work. The post advocates data-native agents: models, agents, tools, retrieval, and memory run inside the governed data platform, with policy enforced during query planning and computation rather than after responses are produced. It argues that post-hoc controls cannot undo sensitive information encoded in aggregations and can trigger token-burning retry loops. For state and memory, it presents Lakebase, managed PostgreSQL within Databricks, as transactional storage and a shared source of truth for multi-agent swarms. The described platform pattern combines Unity Catalog, Unity AI Gateway, Model Serving, MLflow 3, AI Search, Lakebase, and business-context services, and recommends inventorying workloads already outside the perimeter before closing seams incrementally.
Take insights anywhere with Genie One on mobile
Genie One mobile apps for iOS and Android let business users ask questions of company data, view dashboards, access Databricks Apps, and use conversational agent capabilities away from a desk. Answers draw on Genie Ontology, Genie Agents, enterprise governance, and connectors to Google Drive, Microsoft 365, and Atlassian, while respecting source permissions. AI/BI Dashboards reflow widgets into a single column in portrait mode and preserve their designed layout in landscape. Authentication uses the existing OAuth flow and identity provider, with MFA, conditional access, device posture, network controls, regional data handling, and workspace permissions carried over from the browser; there is no separate mobile backend or mobile-only endpoint. The app is available in Public Preview, with dark mode, push notifications, voice mode, and account-level access listed as planned capabilities.
How Retail Finance teams are using Agentic AI to protect omni-channel margins
Omni-channel retail has spread margin, cash, and markdown decisions across more channels, fulfillment paths, and return routes, while agentic systems increase the speed and complexity of change. The post presents ontology as a way to preserve the meaning and business context behind finance figures, keeping definitions, channels, and cost drivers current. Databricks Genie is described as a data-smart AI coworker that answers finance questions in plain language, grounding responses in an evolving ontology, source traces, permissions, and governed AI costs. It focuses on margin after fulfillment and returns, inventory cash tied up in the wrong place, and full-price revenue at risk from markdowns and returns, then prepares actions for a person to approve. Unilever deployed Genie to more than 1,200 finance and business users; analysis that took days now takes minutes, with expected multi-million-euro annual cost avoidance.
Foundational context: Cross-industry & function-specific accelerators for Lakebase
Databricks presents Lakebase as a fully managed, serverless, standard Postgres database for combining operational and analytical workloads on its Data + AI Platform. The platform separates compute from storage, integrates with the lakehouse through Synced Tables and Lakebase CDF, and uses Unity Catalog for governance; copy-on-write branching and autoscaling to zero are described as core infrastructure primitives. The post showcases partner-built, ready-to-deploy accelerators spanning technology, finance, marketing, sales, supply chain, human resources, customer service, and operations. Examples include PostgreSQL migration assessment, multi-agent Genie orchestration, stateful enterprise agents, autonomous data reliability, governed contact-center intelligence, and project operations management. These offerings package Lakebase patterns into migration controls, domain-specific solutions, and agent frameworks intended to accelerate modernization and reduce transformation complexity.
Blocking Slow-Burn Attacks: Contextual Policies in Omnigent
Omnigent’s post examines how a vendor-review assistant can leak confidential pricing terms when an attacker hides an indirect prompt injection in a shared runbook. Because the malicious workflow is divided into ordinary actions, stateless checks approve each step even though the session as a whole is unsafe. The demonstration compares an unprotected run, which sends the summary externally, with a contextual policy that stores a running risk score, adds 30 for each document read, and denies email after the score exceeds 50. It also shows that agents cannot remove or disable policies, new policies require human approval, and any denial prevails when policies are combined. Runtime enforcement therefore preserves the block even when the agent has been misled.
Ultra-Fast Anomaly Detection using Apache Spark Real-Time Mode
The post presents a reusable real-time guardrail pattern for flagging suspicious Ethereum blockchain transactions and routing them for downstream action. Its rules identify impossible blocks where gas_used exceeds gas_limit and scan extra_data for email addresses, JWT tokens, or AWS access-key patterns, producing ALLOW or QUARANTINE decisions with reasons. The implementation uses Apache Spark Structured Streaming Real-Time Mode, whose continuous data flow, pipeline scheduling, streaming shuffle, pre-allocated execution pipelines, and asynchronous checkpointing target millisecond latency without a separate streaming engine. On a four-worker DBR 16.4 LTS cluster, the stateless Kafka-to-Kafka test processed about 23 million records at 69,713 rows per second, with P95 below 0.5 milliseconds and P99 at 1 millisecond. The post notes that RTM with a Kafka sink provides at-least-once delivery and that more complex stateful workloads may incur higher latency.
Practical Guide To Python App Hosting
Python app hosting is presented as both an infrastructure choice and, for data-intensive or AI applications, a data-architecture choice. The guide defines a hosting stack comprising application code, runtime, dependency manager, application server and reverse proxy, and contrasts it with traditional web hosting. It compares shared hosting, VPSs and cloud VMs, PaaS, container platforms and serverless functions by control, operational effort, scalability and workload fit. It notes that long-running data and AI tasks such as model training, vector indexes, cached datasets, ETL jobs and lengthy inference pipelines can exceed practical serverless limits. The conclusion is that public APIs and lightweight web apps allow broad platform choice, while apps accessing governed data, model endpoints or AI agents should be evaluated by where data lives and how access is governed, with Databricks Apps presented as an option when data already resides in a lakehouse.
The agentic marketing stack starts with the data layer
The piece argues that agentic marketing depends on modernizing the data and infrastructure foundation first, rather than starting with isolated AI use cases. At Acxiom, moving core products and client solutions from on-premises Hadoop to Databricks reportedly improved workload runtimes by 80 to 90 percent, reducing jobs that took 50+ hours or as long as 90+ hours to 2–3 hours. The migration also reduced manual pipeline and infrastructure work, freeing engineering capacity for products and client outcomes. Those capabilities now support agentic audience planning, media buying, campaign activation, performance analytics, code generation, testing, and ad variation, with a goal of connecting the full marketing value chain. Because workflows handle PII, generated content passes through legal approval, defined controls, and human oversight; Acxiom is also embedding its data in client platforms and privacy-safe clean rooms to make decisions more transparent and native to customer environments.
Introducing Feature Views
Feature Views are presented as a framework for creating managed feature pipelines in Databricks, addressing duplicated logic, training/serving skew, backfills, online-store plumbing, and governance for real-time ML. A Feature View defines the source, entity, time-series column, and computation once; Databricks uses it to generate point-in-time-accurate historical data, then materializes pipelines for batch or streaming production use. Registered in Unity Catalog, materialized features receive lineage and access controls, while MLflow records model dependencies and Model Serving retrieves required features without custom lookup code. For Kafka-sourced streaming features, Spark Realtime Mode, Lakebase, and Model Serving support rolling-window updates with reported end-to-end p99 latency of 200ms from event to online availability. The announcement says Feature Views are in Public Preview and can be developed through the Feature Engineering Client SDK and Genie Code, with streaming materialization requiring an Enterprise-tier workspace in a region supporting Lakebase.
The ambulatory intelligence gap
Health systems face an ambulatory intelligence gap: access, provider capacity, referral retention, panel management, and financial performance are interconnected, while information remains scattered across disconnected systems. Health Catalyst’s Ambulatory Intelligence combines AI with nearly two decades of healthcare improvement expertise, deploying directly in a customer’s Databricks workspace so sensitive data stays within the health system’s environment. It uses a medallion-based semantic layer, Unity Catalog for governance, Lakebase for low-latency serving, and Genie alongside dashboards to help leaders investigate why metrics change. The solution ships with prebuilt metrics across Access Optimization, Revenue Intelligence, Panel Management, and Referral Insights, plus cross-domain scorecards and configurable terminology and workflows. Reported outcomes from supported improvement work include increased revenue and encounters at Thibodaux Regional, 55,000 closed care gaps at INTEGRIS Health, and higher outpatient visits with fewer cancellations without reschedules at WakeMed; future plans include models based on prior outcomes and agentic capabilities.
Ask, build, compose: What our 5th Genie Hackathon taught us about Databricks Genie
The fifth Databricks hackathon used Databricks Genie to test three ways of working with governed data: asking, building, and composing. Genie Agents give business users domain-specific natural-language access to curated data, while Genie Code helps analysts create metric views, functions, pipelines, and dashboards inside Databricks. The composition track uses Conversation APIs and a managed MCP server to make Genie an agent tool alongside custom logic, Model Serving, and other MCP servers, with Unity Catalog governing access. Projects included supervisory routing across 190 tables, governance and analytics products built in days, and multi-agent systems such as ShipBob’s overnight operations brief. Across the tracks, the stated lesson is that shared governance and semantic context let business users, builders, and engineers use Genie at different levels without abandoning grounded, permissioned data access.
Navigating a Synapse Migration to Databricks
The post presents migration from Azure Synapse to Databricks as a way to reduce platform complexity while supporting data engineering, analytics, machine learning, real-time pipelines, and AI applications. It distinguishes migration paths for Dedicated SQL Pools, serverless SQL, Spark Pools, orchestration, governance, and downstream BI connectivity, noting that accumulated T-SQL logic and dependencies require the most redesign, testing, and validation. The proposed programme uses discovery and assessment with Lakebridge Profiler and Analyzer, then selects an architecture, sequences workloads, pilots a real use case, and scales migration in waves. It recommends automating code conversion, reconciling row counts, aggregations, hashes, and tolerances, avoiding direct Synapse distribution-to-Delta partition mappings, and planning compaction, enablement, and rollback. Reported examples include Casey’s halving operational data delivery times from eight hours to four and Italgas cutting workload costs by 73%.
Benchmarking Coding Agents on Databricks’ Multi-Million Line Codebase
Databricks built a coding-agent benchmark from reviewed tasks drawn from its multi-million-line codebase, spanning languages including Scala, Go, Rust, Python, and TypeScript. It uses recent human-written pull requests, held-out tests, standard model and harness setups, and manual validation rather than an LLM judge; Git history was sealed after traces showed agents could recover solutions. Results clustered models and harnesses into three capability tiers and showed that quality, cost per task, and token price can diverge: GLM 5.2 statistically tied Opus 4.8 on quality while costing $1.28 versus $1.94 per task, whereas Sonnet 5 cost $2.09 versus Opus’s $1.94. The same model through different harnesses produced cost differences of more than 2x in some cases while quality remained the same; Pi sent about three times less context per turn. Databricks plans to shift routine work toward cheaper models, deploy open models such as GLM as daily drivers, and improve model-and-harness selection.
How to Evaluate an Enterprise Analytics Platform
Enterprise analytics platform evaluations often overemphasize dashboard interfaces, although the larger decision concerns whether analytics, AI and agents share data, semantics and governance. The post distinguishes point solutions from a unified platform and proposes seven evaluation criteria: workload fit, architecture and openness, governance and compliance, performance and scalability, adoption and usability, AI and ML readiness, and total cost of ownership. It recommends mapping current and three-year workloads, testing production-scale data with realistic concurrency, measuring p95 latency, and examining governance, usability, contracts and operational complexity in a proof of concept. Lakehouse architecture, open formats such as Delta Lake and Apache Iceberg, and shared controls are presented as ways to reduce context gaps; Databricks is offered as a practical example using Unity Catalog, Genie and Agent Bricks. The conclusion favors a weighted, three-year assessment over a feature comparison.
Topics they write about
All topics →Weekly digest
The best engineering reads, once a week.