--- title: "Data Platform Extension" description: "Browse the documentation, explore your datasets and buckets, run read-only SQL, open ready-to-use notebooks wired to your own data" url: https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/dpe-notebooks-data-platform-extension lang: es lastUpdated: 2026-09-14 --- > For AI agents: the complete documentation index is available at https://docs.ovhcloud.com/es/llms.txt, the full documentation bundle is available at https://docs.ovhcloud.com/es/llms-full.txt. # Data Platform Extension ## Objective Every Data Platform notebook ships with the **Data Platform extension**: a companion panel inside JupyterLab that brings the platform to your notebook. Browse the **documentation**, explore your **datasets and buckets**, run **read-only SQL**, open **ready-to-use notebooks** wired to your own data, and **export a notebook as a custom action** when it is ready to run as a job. Nothing to install, no credentials to configure: it works the moment your [notebook](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/dpe-notebooks.md) starts. ![Dpx overview](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-overview.png) :::info Browsing, previewing and querying are **read-only**: they never modify your data. The only things the extension creates are the notebooks you generate from the gallery, saved in your home folder, and the [custom actions you export](#export-a-notebook-as-a-custom-action). ::: ## Open the extension There are two ways to open it, depending on how you like to work: - **Side panel:** click the **Data Platform icon** in the left sidebar of JupyterLab. The panel opens next to your notebook, so you can explore data and write code side by side. Use the **expand button** in its top bar to switch to a full-width tab. - **Full view:** click the **Data Platform** tile in the JupyterLab launcher (under _Other_). ![Open the extension — Dpx main panel](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-main-panel.png) The top bar is always available: a **global search** field, a **refresh** button that reloads the platform data (new tables show up immediately), and the **language** switch. :::info The extension is available in **English, French and Japanese**: use the **EN / FR / JP** switch in the top bar to change the language of the whole panel at any time. ::: ## Documentation The **Documentation** tab shows the live platform catalog: complete action scenarios, SDK snippets, a PySpark section and a FAQ. It is the same content that powers the helpers of the platform console, always up to date. Every snippet has a **copy button** and a link to the full documentation page. Combined with the table pin (see [Data](#data-browse-your-datasets) below), the snippets are filled with your real dataset and table names: what you copy runs as-is in your notebook. ![Documentation — Dpx documentation](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-documentation.png) ## Data: browse your datasets The **Data** tab lists the datasets of your project, the same ones you see in the [Lakehouse Manager](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/lakehouse-manager-datasets.md). Open a dataset to browse its tables: the search stays instant even with thousands of tables. ![Data: browse your datasets — Dpx data](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-data.png) Expand a table to get everything you usually open the console for: ![Data: browse your datasets — Dpx data expanded](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-data-expanded.png) | Action | What you get | | --------------------- | ----------------------------------------------------------------------------------------------- | | **Schema** | Column names and types, in a compact scrollable grid | | **Count rows** | The live row count of the table | | **Preview** | The first 20 rows, with a **Copy for notebook** button that gives you the matching pandas code | | **Copy as** | Ready-to-paste snippets: `connect()` call, SQL SELECT, filtered `select()`, pandas DataFrame | | **Open in SQL** | Jumps to the [SQL tab](#sql-query-without-leaving-jupyter) with a query prefilled on this table | | **Analyze (gallery)** | Jumps to the [gallery](#notebook-gallery) with this table preselected | | **Use in snippets** | Pins the table: every documentation and gallery snippet is filled with its real name | ## SQL: query without leaving Jupyter The **SQL** tab is a lightweight editor to query a dataset in read-only SQL. Pick the dataset, type a SELECT, press **Run** (or Cmd/Ctrl+Enter): the results show up right below, with the execution time. ![SQL: query without leaving Jupyter — Dpx sql](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-sql.png) A few guardrails keep it safe and fast: - only **SELECT** queries are accepted: nothing can be modified from here; - a row limit is added automatically if you forget one; - **Copy for notebook** on any result gives you the exact SDK code to reproduce the query in a notebook cell. ## Buckets: explore your files The **Buckets** tab is a file explorer over the buckets of your project: navigate folders, see file sizes. Every file comes with a copy-ready snippet to read it from your notebook. ![Buckets: explore your files — Dpx buckets](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-buckets.png) ## Notebook gallery The **Gallery** is a set of ready-to-use notebooks, organized in packs. When you click **Create & open**, a pop-up lets you choose: - **wire it to a real table of your project**: the notebook opens already connected to your data, ready to run top to bottom; - **keep the placeholders**: you get a generic template to adapt later. The notebook is created in your home folder and opens immediately as a JupyterLab tab. ![Notebook gallery — Dpx gallery](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-gallery.png) | Pack | Notebooks | | ------------------------------ | ------------------------------------------------------------------------------------- | | **Getting started** | Data Platform 101: the guided tour of the platform on your project's real data | | **Explore & assess** | Explore a table, Data quality report, Cross-dataset analytics | | **Pipelines** | ETL table to table, Ingest an API into the DWH, Load bucket files into the DWH | | **ML & Spark** | ML quickstart, Segmentation, Anomaly detection, Time-series forecast, PySpark starter | | **AI (OVHcloud AI Endpoints)** | LLMs on your data, Talk to your data (SQL agent) | The gallery adapts to your notebook image: the ML notebooks appear on **Data Science** and **TensorFlow** notebooks, and the PySpark starter on **PySpark** notebooks. :::info The **AI pack** uses [OVHcloud AI Endpoints](https://www.ovhcloud.com/en/public-cloud/ai-endpoints/) with your own API key. The rows you select are sent to the AI Endpoints API, so mind data sensitivity. See also [Jupyter AI Extension](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/dpe-notebooks-jupyter-ai-extension.md) for the chat assistant. ::: ## Export a notebook as a custom action Once your notebook code is ready to run as a job, the extension can turn it into a [Custom action](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/dpe-actions-custom.md) without leaving JupyterLab, and without copying code around. ![Export a notebook as a custom action — Dpx export menu](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-export-menu.png) Start the export from the extension, then fill in the export form: 1. Select the **notebook** to export. 2. Pick the **repository** of the Data Processing Engine the action is exported to. 3. Choose whether to **create a new custom action** or **replace an existing action** with this export. 4. Give the action a **name**, and pick its type: **Custom Python** or **Custom PySpark**. 5. Click **Preview**. ![Export a notebook as a custom action — Dpx export setup](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-export-setup.png) The preview sums up what will be created: target repository and version, action name and type, entry function, code size, and the dependencies detected from the notebook's **pip installs**, automatically converted into action dependencies. Below the summary you can review the generated code: non-code cells are dropped, and the code is wrapped in a `def customfunc(event):` entry function to match the action format. ![Export a notebook as a custom action — Dpx export preview](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-export-preview.png) Happy with the result? Click **Create action**. The action is then available in the [Actions](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/landing-page-dpe-actions.md) screen like any other custom action: configure it, run it, or schedule it in a [workflow](https://docs.ovhcloud.com/es/guides/public-cloud/data-platform/dpe-workflows.md). ![Export a notebook as a custom action — Dpx export confirmation](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-export-confirmation.png) ## Search everything The **search bar** at the top looks across the whole extension at once: documentation snippets, datasets, tables you have browsed, and buckets. Results are grouped by type; clicking a table opens it directly with its details expanded. ![Search everything — Dpx search](/images/public-cloud/data-platform/product/dpe/notebooks/picts/dpx-search.png) ## Go further If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/es-es/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project. Ask questions, give your feedback and interact directly with the team building the Data Platform on the dedicated [Discord channel](https://discord.gg/ovhcloud). If you need support with your OVHcloud services, create a request in our [Help Centre](https://help.ovhcloud.com/csm?id=csm_get_help). Join our [community of users](https://community.ovhcloud.com/).