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/legacy/api-reference-data-manager.md.
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    • API reference: Data Manager

    Warning

    This page describes the ForePaaS Legacy Platform, which no longer accepts new signups. If you are using OVHcloud Data Platform, see the Lakehouse Manager.

    When interacting with the Data Manager through the ForePaaS API, you have to use different endpoint patterns for different purposes.

    Below, you will find the reference for these patterns.

    Create a source

    Endpoint pattern:

    • POST /datastores/db?token=xxxxx:

    JSON Body example for a Dropbox source:

    {     
"dbms": "dropbox",     
"parameters":  { 
  "path": "/folder/path",         
  "token": "xxxxxxxxxx"     
},     
"database_name": "dropbox_source",     
"dwhName": "your_dwh_name",     
"level_name": "source 
}

    Add tables to a source

    Endpoint pattern:

    • POST /metas/tables?dwh_name=your_dwh_name&token=xxxxx

    JSON Body example to add a table to a Dropbox source:

    { 
  "table_name": "your_file.csv",     
  "database_name": "dropbox_source" 
} ```

 
You can also add several tables in one shot: 
```json
[{ 
   table_name": "file1.csv",       
  "database_name": "dropbox_source" 
 },
 {  
  "table_name": "file2.csv",       
  "database_name": "dropbox_source" 
}]

    Metas extraction

    Endpoint pattern:

    • PUT /metas/sources/extract/source_name/table/table_name?dwhName=dwh_name&token=xxxxxxx

    If you need to extract metas from a file1.csv file from a Dropbox source, you need to search: /metas/sources/extract/dropbox_source/file1.csv

    This call accepts settings like:

    • sample: to extract metas on a data sample, not all (set it up on "true")
    • sample_max_lines: if the sample is set up on "true", use it to set up the max number of lines you want for the sample
    • flashzone: set up the option as "true" to have an overview of the file / table data on your DataPlant

    Add virtual attributes

    Endpoint pattern:

    • POST /attributes/<your_dwh_name>?token=xxxxxxx

    Here is an example of a JSON Body:

    {
        "attribute_name": "my_attribute",   
	"parameters": {},   
	"is_dimension": false,   
	"is_virtual": true,   
	"is_summable": true,   
	"sql": "SELECT COUNT(id) FROM clients.csv",   
	"is_measure": true 
}

    Save your table schema

    Endpoint pattern:

    • POST /logical/object?dwhName=dwh_name&token=xxxxxx

    Here is an example of a JSON Body:

    [  
   {  
      "attributes":[  
         {  
            "attribute_name":"conso_id",
            "is_dimension":true,
            "sources":[  
               {  
                  "rules":[  

                  ],
                  "attributes":[  
                     "dropbox/consomations.csv/conso_id"
                  ]
               }
            ],
            "is_summable":false,
            "attribute_path":null,
            "fpui":{  
               "operation":"mapping"
            },
            "type":{  
               "main":"num",
               "sub":"smallint"
            },
            "is_measure":false
         },
         {  
            "attribute_name":"ca",
            "database_name":"data_prim",
            "is_dimension":false,
            "sources":[  
               {  
                  "rules":[  

                  ],
                  "attributes":[  
                     "dropbox /consomations.csv/ca"
                  ]
               }
            ],
            "is_summable":false,
            "table_name":"consomations",
            "fpui":{  
               "operation":"mapping"
            },
            "type":{  
               "max":999.99,
               "main":"num",
               "precision":2,
               "min":0.02
            },
            "attribute_path":null,
            "is_measure":true
         }
      ],
      "object_name":"consomations",
      "display_name":"consomations",
      "object_type":"measure",
      "to_materialize":true,
      "measure_date_field":"date",
      "key":[  
         "conso_id"
      ],
      "date_type":false,
      "relations":[  

      ],
      "indexes":[  

      ]
   }
]

    Build tables

    Below, are the endpoints that have to be executed to build your tables.

    Build logical structure of a Data Prim:

    • PUT /logical/build/prim?dwhName=dwh_name&token=xxxxxx

    Build logical structure of a Data Mart:

    • PUT /logical/build/prim?dwhName=dwh_name&token=xxxxxx

    Build physical structure:

    • PUT /datastores/db/physical?token=xxxxx

    Here is a JSON Body example:

    {
   "levelsToBuild": "["data_prim"]",   
   "dwhName": "plw1uzhy_jm224kp5" 
}