For AI agents: the complete documentation index is available at https://docs.dataplatform.ovh.net/llms.txt, the full documentation bundle is available at https://docs.dataplatform.ovh.net/llms-full.txt, and this page is available as Markdown at https://docs.dataplatform.ovh.net/lakehouse-manager-key-concepts.md.
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    • Discover the key concepts in the Lakehouse Manager

    At the heart of your data-driven initiatives, the Lakehouse Manager stands as the pivotal hub for all your data assets

    Objective

    Info

    At the heart of your data-driven initiatives, the Lakehouse Manager stands as the pivotal hub for all your data assets. With the Lakehouse Manager, you're primed to jumpstart your data-driven progress, bypassing the arduous tasks of data infrastructure cleanup and management, and instead focusing on what truly matters, unleashing the full potential of your data.

    The Lakehouse Manager component has 8 submenus, corresponding to its main features:

    • Overview giving information on the various data assets (like their distribution, issues related to them, associated tags, storage usage, execution analysis)

    • Datasets to logically group and manage Tables easily.

    • Tables to create tables from sources and organize them in databases

    • Attributes to visualize attributes across your data lake / data warehouse and their lineage in the Project

    • Buckets to allow you to stock non-structured objects (pictures for example)

    • Explorer to inspect your data hands-on with visual or SQL queries.

    • Policy Tags to help you with fine-grained Advanced Data Access Control

    • Jobs to give all users an overall overview of all the jobs and their status

    The Lakehouse Manager takes data modelling a step further by proposing the creation of data lakes and data warehouses under the tables tab. Manage all your ingestion and primary tables, and design powerful 'data marts' - i.e. the aggregations designed to answer specific data visualization or AI needs. The Lakehouse Manager further provides collaborative working (with autosave) by allowing multiple users to work together on the same set of tables under separate views which could be specific to a Project or department. Tables are composed of attributes which can be processed, queried, or used as ML features.

    Buckets is the next subcomponent, which brings along the flexibility by allowing you to store any unstructured data in an S31 compatible object-store.

    Datasets allows you to logically group and manage Tables easily.

    Policy Tags was developed to help the user set 'policy tags' on tables, attributes, and datasets for fine-grain Advanced Data Access Control.

    Lastly, through the Jobs tab, you will be able to view the summary of your job performances. It provides you with quick hand information like status of jobs, jobs run overtime, job’s runtime, also running jobs, queued jobs, past jobs executed.

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