llms.txt
+81 −33
https://databricks.techfabric.com/llms.txt
− Written by the engineers at TechFabric, a Databricks partner with 80 Databricks-certified engineers inside a team of 115+. We migrate warehouses onto the lakehouse, design Unity Catalog governance, and build the applications and agents that run inside the workspace. The work is at https://www.techfabric.com/databricks.− - [Databricks guide for engineers: where to start](https://databricks.techfabric.com/guide/): A free Databricks guide for engineers. Which chapters to read for the job you have, the first thing to do in each, and how the pages are laid out.− - [Databricks vs Postgres and a dashboard: when you need more](https://databricks.techfabric.com/guide/not-just-postgres/): The honest answer to "we already store data in Firebase, pull it into Postgres, and build dashboards on top, and that works fine."− - [Databricks pricing: DBU rates and what it really costs](https://databricks.techfabric.com/guide/what-it-costs/): Databricks pricing explained, with the list price per DBU for each compute type, a worked monthly bill, and the settings that decide whether an estate is cheap.− - [How to learn Databricks: a learning path and certification](https://databricks.techfabric.com/guide/how-to-learn-databricks/): A learning path for Databricks. What to build first on Free Edition, what to read in what order, which certification to sit, and where to ask a person.− - [What is Unity Catalog? Databricks governance in practice](https://databricks.techfabric.com/guide/unity-catalog/): Databricks Unity Catalog is the register of what data you hold and the gatekeeper for who may see it. How the model works, how to lay it out, what it misses.− - [Delta tables on Databricks: storage, liquid clustering, features](https://databricks.techfabric.com/guide/tables-and-storage/): What a Delta table actually is, why partitioning is usually the wrong instinct now, and the table features that solve problems teams still solve by hand.− - [Databricks Lakebase: serverless Postgres with branching](https://databricks.techfabric.com/guide/lakebase/): Postgres with branching, instant restore, and separated storage and compute, sitting inside the same governance boundary as your analytical data.− - [Databricks LTAP explained: how it differs from HTAP and Zero ETL](https://databricks.techfabric.com/guide/ltap/): Lake transactional/analytical processing: what Databricks announced, how it differs from HTAP and Zero ETL, and what it changes for agentic systems.− - [Medallion architecture on Databricks: bronze, silver and gold](https://databricks.techfabric.com/guide/medallion-architecture/): What bronze, silver, and gold each promise, how to enforce those promises in Unity Catalog, and the mechanics that make the layers cheap to run.− - [Databricks change data capture (CDC) and real-time data](https://databricks.techfabric.com/guide/real-time-and-cdc/): Choosing a latency tier honestly, managed CDC connectors, change feeds from sources without one, and applying changes with AUTO CDC.− - [Lakeflow Jobs: orchestrating Databricks jobs in production](https://databricks.techfabric.com/guide/orchestration/): How Lakeflow Jobs runs work unattended, which dependencies belong in a job graph, and the job settings that decide whether on-call is bearable.− - [Databricks metric views and Power BI: where definitions live](https://databricks.techfabric.com/guide/reporting/): Where business definitions should live, why metric views beat a semantic model locked in a BI tool, and what to do if you already run Power BI.− - [Databricks Genie: build a Genie Agent people trust](https://databricks.techfabric.com/guide/genie/): What Databricks Genie is after the June 2026 rename, how a Genie Agent answers, and how to build, test and pay for one business users will trust.− - [Databricks row-level security for multi-tenant reporting](https://databricks.techfabric.com/guide/multi-tenant-reporting/): Showing each customer only their own data, and each user only what their role allows, without trusting a WHERE clause in application code.− - [Databricks Apps: what they are, cost, and when to use them](https://databricks.techfabric.com/guide/databricks-apps/): Databricks Apps explained: running a Python or Node app inside the workspace, what it costs per hour, and the identity choice that decides its governance.− - [Delta Sharing, Databricks clean rooms and the Marketplace](https://databricks.techfabric.com/guide/sharing-and-collaboration/): Giving partners live access to governed tables, running joint analysis without either side disclosing rows, and acquiring third-party data.− - [MLflow on Databricks: tracking, model registry and serving](https://databricks.techfabric.com/guide/mlflow/): Tracking, packaging, registering and serving models, and why a model governed as a catalog object makes an audit answerable.− - [Databricks Foundation Model APIs and Unity Gateway](https://databricks.techfabric.com/guide/foundation-models/): Pay-per-token, provisioned or external models, and why routing every call through Unity Gateway, formerly Unity AI Gateway, is the decision that matters.− - [RAG on Databricks: Vector Search and context engineering](https://databricks.techfabric.com/guide/context-and-retrieval/): How retrieval-augmented generation actually works, why the context window is a budget rather than a container, and why filtering by the asker comes first.− - [Databricks AI agents: Agent Bricks, evals and governance](https://databricks.techfabric.com/guide/agents/): Building AI agents on Databricks with Agent Bricks or code, next to governed data, why evaluation is the real work, and where the agent must stop.− - [Databricks migration guide: from a warehouse to the lakehouse](https://databricks.techfabric.com/guide/migration/): Moving an established estate onto Databricks, from the three kinds of work through reconciliation to switching the old system off.− - [Databricks cost optimization and performance tuning](https://databricks.techfabric.com/guide/cost-and-performance/): Diagnosing slow Databricks work in the right order, choosing compute types, and making every dollar of spend attributable to a team, with the SQL to do it.− - [Databricks security: identity, network, keys and audit](https://databricks.techfabric.com/guide/security/): Which identity reaches your Databricks data on each path in, and what the network paths, encryption keys and audit logs add around it.− - [Databricks CI/CD with Asset Bundles and environments](https://databricks.techfabric.com/guide/ci-cd/): Databricks CI/CD with Declarative Automation Bundles (formerly Asset Bundles), Terraform, real tests and one artefact promoted from dev to production.− - [Databricks data observability: freshness, volume and quality](https://databricks.techfabric.com/guide/observability/): Catching the quiet failures that produce confidently wrong numbers, using freshness, volume, and quality signals rather than job status alone.− - [Databricks disaster recovery: what you must recover yourself](https://databricks.techfabric.com/guide/resilience-and-recovery/): Separating what the platform recovers for you from what you must recover yourself, and turning a DR document into a rehearsed capability.− - [Databricks vs AWS, Azure and Google Cloud data services](https://databricks.techfabric.com/guide/platform-comparison/): Databricks against the native data services on AWS, Azure and GCP, job by job, where the clouds win, Microsoft Fabric, and how we'd run the evaluation.− - [Databricks vs Snowflake: cost, workloads, when to move](https://databricks.techfabric.com/guide/snowflake/): Databricks vs Snowflake compared side by side on billing, storage, workloads and AI, and why moving an unchanged workload mostly moves the bill.− - [Delta Live Tables, Mosaic AI and other Databricks renames](https://databricks.techfabric.com/guide/renames/): Delta Live Tables, Asset Bundles, Vector Search and Unity AI Gateway have new names. Old and new, and where the old one survives in code and on the bill.− - [Databricks glossary: terms, old product names and meanings](https://databricks.techfabric.com/guide/glossary/): Every Databricks term this manual uses, what we mean by it, the old name where a product was renamed, and the pairs of terms teams most often mix up.− - [Databricks LTAP Direct Writes in beta: faster synced table loads](https://databricks.techfabric.com/notes/ltap-direct-writes-is-the-first-piece/) (2026-09-13): LTAP Direct Writes, in beta since 25 August 2026, loads synced tables by writing to storage instead of through compute. The first LTAP claim a team can time.− The guide is also a public, read-only MCP server at https://databricks.techfabric.com/mcp, with no sign-in. Its tools are search_guide, get_chapter (a chapter or one section, code intact), get_code_examples, whats_new and list_chapters. Answers come from the published pages and change with each deploy, and a chapter, section or code example comes with the Databricks documentation links its page cites. Add it to Claude Code with `claude mcp add --transport http databricks-field-guide https://data+ Written by the engineers at TechFabric, a Databricks partner with 80 Databricks-certified engineers inside a team of 115+. We migrate warehouses onto the lakehouse, design Unity Catalog governance, and build the applications and agents that run inside the workspace. The work is at https://www.techfabric.com/databricks. AI assists the drafting, and a TechFabric engineer reviews every page before it is published: how the guide is written, checked and corrected is at https://databricks.techfabric.c+ - [How we write this guide](https://databricks.techfabric.com/about/): who writes and reviews it, how AI assists the drafting, the fact-check and the corrections policy.+ - [Databricks guide for engineers: where to start](https://databricks.techfabric.com/guide/): Where to start with Databricks for the job you have, the chapters that answer it, the first step we'd take, and how to use this free guide from an agent.+ - [Databricks vs Postgres and a dashboard: when you need more](https://databricks.techfabric.com/guide/not-just-postgres/): "Databricks vs Postgres for reporting: when Postgres and a dashboard is enough, the three signs it has run out, and how Lakebase changes the move."+ - [Databricks pricing: what a DBU costs and what decides the bill](https://databricks.techfabric.com/guide/what-it-costs/): What a DBU costs on each Databricks compute type in October 2026, DBUs an hour by warehouse and cluster size, model serving rates, and what cuts the bill.+ - [How to learn Databricks: a learning path and certification](https://databricks.techfabric.com/guide/how-to-learn-databricks/): "A Databricks learning path: what to build first on Free Edition, which free Academy pathway to follow, which certification to sit, and where to ask a person."+ - [What is Unity Catalog? Databricks governance in practice](https://databricks.techfabric.com/guide/unity-catalog/): How Databricks Unity Catalog governs tables, files, models and agents, the catalog layout we'd use, row filters and ABAC in SQL, and where it stops.+ - [Delta tables on Databricks: storage, liquid clustering, features](https://databricks.techfabric.com/guide/tables-and-storage/): "How Databricks tables are stored: managed vs external, Delta vs Iceberg, liquid clustering, predictive optimization and deletion vectors, by runtime."+ - [Databricks Lakebase: managed Postgres with branching](https://databricks.techfabric.com/guide/lakebase/): What Databricks Lakebase is, what it costs, and how branching, restore, the Data API and synced tables behaved when we built an app on it live.+ - [Databricks LTAP explained: how it differs from HTAP and Zero ETL](https://databricks.techfabric.com/guide/ltap/): "Databricks LTAP explained: what lake transactional/analytical processing is, which Lakebase pieces ship today, how it differs from HTAP and Zero ETL."+ - [Medallion architecture on Databricks: bronze, silver and gold](https://databricks.techfabric.com/guide/medallion-architecture/): "Medallion architecture on Databricks: what bronze, silver and gold each promise, the Lakeflow pipelines code that enforces it, and when to use fewer layers."+ - [Databricks change data capture (CDC) and real-time data](https://databricks.techfabric.com/guide/real-time-and-cdc/): "Change data capture on Databricks: Lakeflow Connect CDC connectors, AUTO CDC for SCD type 1 and 2, real-time mode, Zerobus, and picking a latency tier."+ - [Lakeflow Jobs: orchestrating Databricks jobs in production](https://databricks.techfabric.com/guide/orchestration/): "Lakeflow Jobs in production: triggers, for-each and if/else tasks, retries and timeouts, and the system table queries that audit a jobs estate."+ - [Databricks metric views and Power BI: where definitions live](https://databricks.techfabric.com/guide/reporting/): How Databricks metric views work as a semantic layer, how Power BI reads them in DirectQuery, and the dashboard habits that keep reporting cheap.+ - [Databricks Genie: build a Genie Agent people trust](https://databricks.techfabric.com/guide/genie/): What Databricks Genie One, Genie Agents and Genie Code are, the warehouse a Genie Agent needs, what it costs from 2027, and how we'd build one people trust.+ - [Databricks row-level security for multi-tenant reporting](https://databricks.techfabric.com/guide/multi-tenant-reporting/): Row-level security for multi-tenant reporting on Databricks, with the SQL for row filters, ABAC, embedded dashboards and OAuth custom identity claims.+ - [Databricks Apps: what they are, cost, and when to use them](https://databricks.techfabric.com/guide/databricks-apps/): Databricks Apps for developers, covering the runtime, on-behalf-of-user auth and scopes, Lakebase and warehouse resources, deploys and the hourly cost.+ - [Delta Sharing, Databricks Clean Rooms and the Marketplace](https://databricks.techfabric.com/guide/sharing-and-collaboration/): Databricks Clean Rooms and Delta Sharing (OpenSharing) in practice, with the SQL to share a table, the clean room approval rules, and the audit queries.+ - [MLflow on Databricks: tracking, model registry and serving](https://databricks.techfabric.com/guide/mlflow/): Runnable MLflow 3 code for Databricks covering table lineage, the Unity Catalog model registry and aliases, deployment jobs, serving and GenAI tracing.+ - [Databricks Foundation Model APIs and Unity Gateway](https://databricks.techfabric.com/guide/foundation-models/): How Databricks Foundation Model APIs work, which models are pay-per-token, what they cost in DBUs, and why every call goes through Unity Gateway.+ - [RAG on Databricks: AI Search and context engineering](https://databricks.techfabric.com/guide/context-and-retrieval/): RAG and context engineering on Databricks with AI Search (formerly Vector Search), chunking, hybrid search, reranking and filters by who is asking.+ - [Databricks AI agents: Agent Bricks, evals and governance](https://databricks.techfabric.com/guide/agents/): Building AI agents on Databricks with Genie Agents or the Agent Bricks CLI, scoring them with MLflow 3, and keeping them inside the caller's permissions.+ - [Databricks migration guide: from a warehouse to the lakehouse](https://databricks.techfabric.com/guide/migration/): How to migrate a warehouse to Databricks with Lakebridge and federation, which parts overrun, and how to prove parity before switching the old system off.+ - [Databricks cost management and performance tuning](https://databricks.techfabric.com/guide/cost-and-performance/): Databricks cost management and performance tuning: attribute spend with tags and system.billing SQL, pick the right compute, then fix slow queries in order.+ - [Databricks security: identity, network, keys and audit logs](https://databricks.techfabric.com/guide/security/): Databricks security in practice: the identity on each path to the data, ABAC policies, serverless egress, customer-managed keys and audit log SQL.+ - [Databricks CI/CD with bundles, Terraform and environments](https://databricks.techfabric.com/guide/ci-cd/): Databricks CI/CD with Declarative Automation Bundles (formerly Asset Bundles) and Terraform: a runnable databricks.yml, a GitHub Actions deploy and promotion.+ - [Databricks data observability: freshness, volume and quality](https://databricks.techfabric.com/guide/observability/): What data observability means on Databricks: freshness, volume and quality checks, system table SQL for job health, anomaly detection and alerts with owners.+ - [Databricks disaster recovery and high availability](https://databricks.techfabric.com/guide/resilience-and-recovery/): Databricks high availability vs disaster recovery: what the platform recovers, managed DR and its limits, Lakebase HA and cross-region DR, and a rehearsed plan.+ - [Databricks vs AWS, Azure and Google Cloud data services](https://databricks.techfabric.com/guide/platform-comparison/): Databricks vs AWS, Azure and Google Cloud data services, service by service as of October 2026, where the native stack wins, and Databricks or Microsoft Fabric.+ - [Databricks vs Snowflake: cost, workloads, when to move](https://databricks.techfabric.com/guide/snowflake/): Databricks vs Snowflake on billing, storage, AI and Postgres, why moving an unchanged workload mostly moves the bill, and how to migrate when it's right.+ - [Delta Live Tables, Mosaic AI and other Databricks renames](https://databricks.techfabric.com/guide/renames/): Delta Live Tables is now Lakeflow pipelines. Where the DLT docs moved, and the other Databricks renames, with where each old name survives in code and billing.+ - [Databricks glossary: terms, old product names and meanings](https://databricks.techfabric.com/guide/glossary/): A Databricks glossary with one-sentence definitions of Unity Catalog, Lakeflow, Lakebase, Genie and AI terms, the old product names, and links to each chapter.+ - [TechFabric Toolkit for Databricks, the Chrome extension](https://databricks.techfabric.com/guide/databricks-toolkit/): "TechFabric's free Chrome extension for Databricks: what each feature does and where, how to install it, its permissions and privacy, and its MCP tools."+ - [Databricks Unity Gateway for coding agents: controlling the AI bill](https://databricks.techfabric.com/notes/the-ai-bill-is-coming/) (2026-10-23): Databricks says a gateway cut its AI coding bill. How we would put Claude Code and Codex behind Unity Gateway budgets, smart defaults and routing.+ - [Databricks serverless jobs from pyproject.toml, and uv.lock beside it](https://databricks.techfabric.com/notes/serverless-job-environments-from-pyproject-toml-beta/) (2026-10-23): Databricks serverless job tasks can now read their environment from pyproject.toml, in Beta. Why we would treat uv.lock as part of the job.+ - [Databricks Lakebase Insights: per-project setup and what stopped](https://databricks.techfabric.com/notes/lakebase-insights-moves-to-per-project-settings-with/) (2026-10-23): Lakebase Insights now runs per project, on a schedule or on demand. Old observability schedules stopped, so what we would turn on and who should see it.+ - [Databricks Files API 3.0: multipart uploads and conditional writes](https://databricks.techfabric.com/notes/files-api-3-0-for-unity-catalog-volumes/) (2026-10-23): Files API 3.0 in Beta adds multipart uploads and ETag conditional writes to Unity Catalog volumes. What we would use first, and the network work.+ - [Databricks row filters and column masks: policy metrics now hidden](https://databricks.techfabric.com/notes/databricks-runtime-19-october-19-2026/) (2026-10-23): Databricks Runtime 19 hides row filter and column mask internals from Spark Connect metrics. What stays visible, and how we would tune protected tables.+ - [Databricks transformWithState async processing: when we'd switch](https://databricks.techfabric.com/notes/asynchronous-processing-with-transformwithstate-is-now-generally-available/) (2026-10-23): Async processing for Python transformWithState is GA on Databricks Runtime 19. When we would convert a stateful processor, and what to check first.+ - [Databricks Lakeflow pipeline system tables are GA: cost and refreshes](https://databricks.techfabric.com/notes/lakeflow-pipelines-system-tables-are-now-generally-available/) (2026-10-22): The Lakeflow pipeline system tables are generally available. How we would use them for pipeline spend by name and full refresh audits.
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