--- title: Introduction description: Introduction to dltHub --- ## What is dltHub? dltHub is an LLM-native data engineering platform that lets any Python developer build, run, and operate production-grade data pipelines, and deliver end-user-ready insights without managing infrastructure. dltHub is built around the open-source library [dlt](../intro.md). It uses the same core concepts (sources, destinations, pipelines) and extends the extract-and-load focus of `dlt` with: * Enhanced developer experience * Transformations * Data quality * AI-assisted (“agentic”) workflows * Managed runtime dltHub supports both local and managed cloud development. A single developer can deploy and operate pipelines, transformations, and notebooks directly from a dltHub Workspace, using a single command. The dltHub Runtime, customizable workspace dashboard, and validation tools make it straightforward to monitor, troubleshoot, and keep data reliable throughout the whole end-to-end data workflow: ```mermaid flowchart LR A[Create a pipeline] --> B[Ensure data quality] B --> C[Create reports & transformations] C --> D[Deploy Workspace] D --> E[Maintain data quality] E --> F[Share] ``` In practice, this means any Python developer can: * Build and customize data pipelines quickly (with LLM help when desired). * Derisk data insights by keeping data quality high with checks, tests, and alerts. * Ship fresh dashboards, reports, and data apps. * Scale the data workflows easily without babysitting infra, schema drift, and silent failures. :::tip Want to see it end-to-end? Watch the dltHub [Workspace demo](https://youtu.be/rmpiFSCV8aA). ::: To get started quickly, follow the [installation instructions](getting-started/installation.md). ## Overview ### Key capabilities 1. **[LLM-native workflow](../dlt-ecosystem/llm-tooling/llm-native-workflow)**: accelerate pipeline authoring and maintenance with guided prompts and copilot experiences. 2. **[Transformations](features/transformations/index.md)**: write Python or SQL transformations with `@dlt.hub.transformation`, orchestrated within your pipeline. 3. **[Data quality](features/quality/data-quality.md)**: define correctness rules, run checks, and fail fast with actionable messages. 4. **[Data apps & sharing](../general-usage/dataset-access/marimo)**: build lightweight, shareable data apps and notebooks for consumers. 5. **[AI agentic support](features/mcp-server.md)**: use MCP servers to analyze pipelines and datasets. 6. **[Managed runtime](runtime/overview.md)**: deploy and run with a single command—no infra to provision or patch. 7. **[Storage choice](ecosystem/iceberg.md)**: pick managed Iceberg-based lakehouse, DuckLake, or bring your own storage. ### How dltHub fits with dlt (OSS) dltHub embraces the dlt library, not replaces it: * dlt (OSS): Python library focused on extract & load with strong typing and schema handling. * dltHub: Adds transformations, quality, agentic tooling, managed runtime, and storage choices, so you can move from local dev to production seamlessly. dltHub extends the dlt developer experience with new [local workspace layout](workspace/init.md), [configuration profiles](core-concepts/profiles-dlthub.md), [additional CLI commands](command-line-interface.md), workspace dashboard, [MCP server](features/mcp-server.md) and more. Those developer experience improvements belong to **dltHub Free tier** and are distributed side by side with the `dlt` under [Apache 2.0 license](https://github.com/dlt-hub/dlt?tab=Apache-2.0-1-ov-file#readme). You can use **dltHub Free tier** right away - like you use regular `dlt`. All features that require a license are part of dltHub paid tiers (e.g., Pro, Scale, Enterprise) and are clearly marked as such in this documentation. Those features are shipped via `dlthub` Python package (available on [PyPI](https://pypi.org/project/dlthub/)) which is not open source and can be used with a valid license. ## dltHub products dltHub consists of three main products. You can use them together or compose them based on your needs. ### Workspace **[Workspace](workspace/overview.md) [Public preview]** - the unified environment for building, running, and maintaining data workflows end-to-end. * Scaffolding and LLM helpers for faster pipeline creation. * Integrated transformations (`@dlt.hub.transformation` decorator). * Data quality rules, test runs, and result surfacing. * Notebook and data apps (e.g., Marimo) for sharing insights. * Visual dashboards for pipeline health and run history. ### Runtime **[Runtime](runtime/overview.md)** - a managed cloud runtime operated by dltHub: * Scalable execution for pipelines and transformations. * APIs, web interfaces, and auxiliary services. * Secure, multi-tenant infrastructure with upgrades and patching handled for you. :::tip Prefer full control? See [Enterprise](#tiers--licensing) below for self-managed options. ::: ### Storage **[Storage](ecosystem/iceberg.md) [In development]**. Choose where your data lives: * Managed lakehouse: Iceberg open table format (or DuckLake) managed by dltHub. * Bring your own storage: connect to your own lake/warehouse when needed. ## Tiers & licensing Some of the features described in this documentation are free to use. Others require a paid plan. Latest pricing & full feature matrix can be found live on our website. Most features support a self-guided trial right after install, check the [installation instructions](getting-started/installation.md) for more information. | Tier | Best for | Runtime | Typical use case | Notes | Availability | | --------------------- |-------------------------------------------------------------------------------------------------| ------------------------------ |-------------------------------------------------------------------------------|-------------------------------------------------|-----------------| | **dltHub Pro** | Solo developers or small teams owning a **single pipeline + dataset + reports** end-to-end | Managed dltHub Runtime | Set up a pipeline quickly, add tests and transformations, share a simple app | Optimized for velocity with minimal setup | Private preview | | **dltHub Scale** | Data teams building **composable data platforms** with governance and collaboration | Managed dltHub Runtime | Multiple pipelines, shared assets, team workflows, observability | Team features and extended governance | Alpha | | **dltHub Enterprise** | Organizations needing **enterprise control** or **self-managed runtime** | Managed or self-hosted Runtime | On-prem/VPC deployments, custom licensing, advanced security | Enterprise features and deployment flexibility | In development | ### Who is dltHub for? * Python developers who want production outcomes without becoming infra experts. * Lean data teams standardizing on dlt and wanting integrated quality, transforms, and sharing. * Organizations that prefer managed operations but need open formats and portability. :::note * You can start on Pro and upgrade to Scale or Enterprise later, no code rewrites. * We favor open formats and portable storage (e.g., Iceberg), whether you choose our managed lakehouse or bring your own. * For exact features and pricing, check the site; this section is meant to help you choose a sensible starting point. :::