# Databasin > Official public pages and product documentation for Databasin. ## Workday analytics - [Your business, thinking as one — Databasin data platform](https://www.databasin.ai/): Databasin turns everything your company knows into decisions, actions, and momentum. Ask in plain English, get the answer with the query attached. - [Workday Analytics for Enterprise Finance & HR — Databasin](https://www.databasin.ai/workday/): Workday analytics for finance and HR: Databasin replaces Prism, your connector stack, and your BI tool with one governed platform. Start with $50 credit. - [Workday Business Object Extraction Architecture — Databasin](https://www.databasin.ai/tech/workday/): Learn how Databasin extracts Workday business objects, preserves effective-date history, and handles the constraints that make generic connectors fail. ## Documentation - [Documentation](https://www.databasin.ai/docs/): Browse all public Databasin product documentation. ### Get Started - [Welcome to Databasin](https://www.databasin.ai/docs/welcome/): A five-minute tour of Databasin: connect a source, build a pipeline into an open Iceberg lakehouse, then ask questions of your data in plain English. - [Exploring the UI](https://www.databasin.ai/docs/get-started/exploring-the-ui/): Where everything lives in Databasin: projects, the Integrations hub, the lakehouse SQL editor, dashboards, and how to move between them quickly. - [The command palette](https://www.databasin.ai/docs/get-started/command-palette/): Jump to any page or start any action in Databasin with Cmd/Ctrl+K. Search projects, pipelines, tables and dashboards without leaving the keyboard. - [The Integrations hub](https://www.databasin.ai/docs/integrations/overview/): The Integrations hub is where connectors, pipelines and automations live together, so you can trace a source all the way to a governed lakehouse table. - [Build your first connector](https://www.databasin.ai/docs/get-started/first-connector/): Connect Databasin to your first data source in about ten minutes: pick a connector, authenticate, test the connection, and preview the tables you get. - [Build your first pipeline](https://www.databasin.ai/docs/get-started/first-pipeline/): Build your first ETL pipeline: move data from source to an Apache Iceberg table on a schedule, with the schema mapped and changes tracked for you. ### Connectors - [How connectors work](https://www.databasin.ai/docs/connectors/overview/): How Databasin connectors work: native certified routes versus legacy JDBC, the auth models each supports, and what "native" actually buys you. - [Native connectors in pipelines](https://www.databasin.ai/docs/connectors/native-in-pipelines/): What a certified native connector handles for you inside a pipeline: pagination, watermarks, incremental loads, schema drift and delete detection. - [Connector catalog](https://www.databasin.ai/docs/connectors/catalog/): Every data source Databasin connects to today, generated from the live registry: SaaS APIs, databases, files and warehouses, with the auth each needs. - [Live connections (no sync)](https://www.databasin.ai/docs/connectors/live-connections/): Query a source directly from SQL through the live catalog, with no sync and no copy. How live connections work, what they cost, and when to use them. ### Pipelines - [How pipelines work](https://www.databasin.ai/docs/pipelines/overview/): How a Databasin pipeline works end to end: source, ingestion mode, target table in the Iceberg lakehouse, and every transformation in between. - [Ingestion modes](https://www.databasin.ai/docs/pipelines/ingestion-modes/): Snapshot, Delta, Historical, CDC and Stored Procedure ingestion modes explained: what each one does to your target table and when to choose it. - [Scheduling and triggers](https://www.databasin.ai/docs/pipelines/scheduling/): Schedule pipelines hourly, daily, weekly or on demand, chain them behind triggers, and understand exactly when the next run will fire. - [Monitoring and alerts](https://www.databasin.ai/docs/pipelines/monitoring/): Watch pipeline health in Databasin: run history, row counts, failure alerts to Slack, Teams or email, and how to catch a broken load early. ### Automations - [What automations do](https://www.databasin.ai/docs/automations/overview/): Automations orchestrate the work around your data: run SQL, chain pipelines, trigger Databricks jobs and notebooks, and put agents on a schedule. - [The task types](https://www.databasin.ai/docs/automations/task-types/): Every task an automation can run: SQL, dbt, notebooks, pipelines, Databricks jobs, OCR, agents and notifications, with what each one needs to run. - [Stages and parallel tasks](https://www.databasin.ai/docs/automations/stages/): How automation tasks group into stages, which run in parallel and which wait, and how to model a dependency without serializing the whole board. - [Pipelines vs. Automations](https://www.databasin.ai/docs/automations/pipelines-vs-automations/): When to reach for a pipeline and when to reach for an automation: moving data versus orchestrating the work that happens once the data has landed. - [Extract data from PDFs](https://www.databasin.ai/docs/automations/document-extraction/): The OCR task reads a folder of PDFs with a vision model and lands structured, queryable rows in your lakehouse, with every field cited to its page. ### Databasin One - [All about Databasin One](https://www.databasin.ai/docs/databasin-one/overview/): Databasin One answers questions about your data in plain English and shows the SQL it ran, so every number that comes back carries its own receipt. - [Meet the agent](https://www.databasin.ai/docs/databasin-one/the-agent/): How the Databasin One orchestrator thinks: how it plans, which specialist skills it delegates to, and the turn and query budgets that bound each run. - [Skills and what they do](https://www.databasin.ai/docs/databasin-one/skills/): The specialist skills behind the agent: analytical SQL, chart selection, report structure, semantic search and semantic modeling, and when each loads. - [Choosing your model](https://www.databasin.ai/docs/databasin-one/models/): Run the Databasin One agent on Claude or GPT, bring your own LLM key, and understand where the choice of model actually changes the answer. - [Documents and chat](https://www.databasin.ai/docs/databasin-one/embeddings-and-chat/): How the agent searches inside the files you upload: embeddings, semantic search over your documents, and asking questions across data and PDFs together. - [Build a semantic model by chatting](https://www.databasin.ai/docs/databasin-one/semantic-models/): Turn raw lakehouse tables into a curated semantic model through conversation, so business terms map to the right columns before anyone writes SQL. - [Bring Power BI models into One](https://www.databasin.ai/docs/databasin-one/power-bi/): Upload a Power BI semantic model into Databasin One and ask questions of it in plain English, keeping the measures and relationships you already built. - [Build a dashboard from questions](https://www.databasin.ai/docs/databasin-one/dashboard-builder/): The AI Dashboard Builder turns a handful of plain-English questions into a cohesive dashboard, with a chart per answer and the SQL behind each tile. - [Discover](https://www.databasin.ai/docs/databasin-one/discover/): Let the agent profile your tables and surface what is interesting in the data on its own: trends, outliers and relationships you did not ask about. - [Background tasks](https://www.databasin.ai/docs/databasin-one/background-tasks/): Send long-running agent work to the background and keep exploring: how background tasks queue, report progress, and hand you results when they finish. ### Lakehouse - [Lakehouse overview](https://www.databasin.ai/docs/lakehouse/overview/): Catalogs, schemas and tables in the Databasin lakehouse: an open Apache Iceberg surface you own, queryable from Trino, Spark, Doris and DuckDB. - [The SQL editor](https://www.databasin.ai/docs/lakehouse/sql-editor/): The Databasin SQL editor: tabs, run modes, saved scripts, query history and the toolbar, running against Trino, Doris, Spark, DuckDB or Databricks. - [Notebooks](https://www.databasin.ai/docs/lakehouse/notebooks/): Multi-language notebooks over your lakehouse: SQL, Python and Spark cells, the keyboard model, Jupyter import, and scheduling a notebook as a task. - [Multi-engine SQL](https://www.databasin.ai/docs/lakehouse/multi-engine/): Query one Apache Iceberg lakehouse from Trino, Apache Doris, Spark, DuckDB and Databricks, choosing the engine per workload without copying data. - [Apache Doris (real-time OLAP)](https://www.databasin.ai/docs/lakehouse/doris/): Apache Doris brings low-latency, high-concurrency OLAP SQL to your Apache Iceberg lakehouse, for dashboards and interactive analytics at speed. - [Clusters, wake and sleep](https://www.databasin.ai/docs/lakehouse/clusters/): Cost-aware compute in Databasin: wake a cluster when you need it, let it sleep when you do not, and see exactly what each running engine costs. ### Dashboards & Sharing - [Dashboard Canvas](https://www.databasin.ai/docs/dashboards/canvas/): Drag, drop, resize and filter tiles into a live dashboard over your lakehouse data, with global filters that apply across every chart on the canvas. - [Chat with a dashboard's data](https://www.databasin.ai/docs/dashboards/explore-the-data/): Let dashboard viewers ask their own questions of the data behind a published dashboard, scoped to exactly the tables that dashboard already uses. - [Publishing to Gallery](https://www.databasin.ai/docs/dashboards/publishing/): Publish a dashboard to the Gallery and share it with your team or the whole organization, with per-dashboard permissions and a live embed option. - [Workspaces (co-work)](https://www.databasin.ai/docs/workspaces/overview/): Workspaces are persistent, shareable Databasin One sessions: a room where a team keeps its data sources, uploaded files, chats and published artifacts. ### Admin & Billing - [Credits and billing](https://www.databasin.ai/docs/billing/credits/): How Databasin billing works: credits versus per-minute metered usage, what consumes each, where to watch spend, and how to set a limit before it bites. - [Users and permissions](https://www.databasin.ai/docs/admin/users-and-permissions/): Invite teammates to Databasin and scope what each can reach: project access, roles, and the difference between org admin and project-level permissions. - [Governance](https://www.databasin.ai/docs/admin/governance/): Govern access in Databasin: per-connector permissions, teams, cost centers, and row-level security so people only query what they are allowed to see. - [System administration](https://www.databasin.ai/docs/admin/system-administration/): The superadmin console: users, modules, the metastore, usage reporting and the org-wide settings that control how a Databasin deployment behaves. ### Help & Troubleshooting - [Common issues](https://www.databasin.ai/docs/help/troubleshooting/): A playbook for the things that go wrong most often in Databasin: failed connections, stalled pipelines, schema drift, and queries that will not run. - [Getting support](https://www.databasin.ai/docs/help/support/): How to reach the Databasin team, what to include so we can reproduce the problem quickly, and where to look first for an answer you can self-serve.