Data is the raw material for all experiments. All your data are scoped to a project and you can access them from the « Data » section on the collapsing sidebar

data section

Data section

The datas section holds 5 kind of assets :

  • datasets : input for train, predictions and pipelines
  • Image folders : folders containing image
  • Data sources : information about location ( database, table, folder) or remote dataset
  • Exporters : information about remote table for exporting prediction


Datasets are tabular data resulting from a flat file upload, a Datasource or a pipeline execution. They are input for training, predicting and pipeline execution

List datasets

Dataset list


  • may be created from file upload
  • may be created from data source
  • may be created from pipeline output
  • may be downloaded
  • may be exported with exporters
  • may be used as pipeline input

When you click on the datasets tabs, you see a list of your dataset with :

  • filter checkbox for origin of the dataset ( pipeline output, Datasource, File upload, Pipeline intermediate file)
  • search box filtering on the name of datasets
  • name of the datasets and information about them
  • status. A dataset could be unavailable a short time after its creation due to parsing.
  • a button to compute embeddings or explore them if already computed ( see the guide about exploring data )

Create a new dataset

In order to create new experiments you need a dataset. They can be created by clicking on the « import dataset » button.

Create datasets

Dataset import

Dataset are always created from tabular data (database table or files ). You can import data from a previously created datasource or from a flat csv file.

For data coming from file ( upload, ftp, bucket, S3,…) you could input the columns and decimals separator but the auto detect algorithm will work in most of case.

When you click on the « save dataset » button, the dataset will immediately be displayed in your list of dataset but won’t be available for a few seconds. Once a dataset is ready, its status will change to « Ready to be used » and you can then compute embedding and use it from training and predicting.

Analyse dataset

Once a dataset is ready, you got access to a dedicated page with detail about your dataset

  • General Information : Dataset summary, feature distribution and correlation matrix of its features
Details about a dataset

Dataset detail

  • Columns : Information about its columns. You can click on a column to get more information about it, its distribution for example
Details about a column

Detail of a column

  • Sample : a very short sample of the dataset
Sample of your dataset

Sample of a dataset

Edit and delete

You can edit name of a dataset, download it or remove it of your storage either in the top nav menu of the dataset details page or from the dot menu in the list of your dataset.

Dataset operation

Edit and remove dataset

When you remove a dataset, local data are completely removed. Data source data are left untouched.

Compute embeddings

See the : Complete Guide for exploring data

Image folders

Image folders are storage for your image. It is source material for image experiments (classification, object detector, …). For Images experiments you need an image folder.

Image folders

  • may be creating from file upload
  • can not use datasource or connectors
  • can be downloaded
  • can not be exported

Create a new image folder

Upload a folder of image

Upload a folder of image

When clicking on « Upload Image Folder » button, you can upload a zip file either from drag and dropping it or from selecting it from your local file browser. The zip file must contain only image but they can be organized into folders.

After having given a name, just click on « upload image folder » and wait. Your images will be available for experiments in a few seconds.

Edit and remove

You can edit the name of of your folder from the list of image folder, using the three-dots menu on the right. Removing the image folder from your storage is available from this menu too.


Connectors are used to hold credentials used to access external databases or filesystems. You need to create a connector first to use Datasources and Exporters.


  • may be used for creating data source
  • may be used for creating exporter

There are two kind of connectors :

  • Connectors to database table ( Any SQL Database )
  • Connectors to storage ( FTP, SFTP, Amazon S3 or Google Cloud Platform ) that contain dataset

In the platform you can set up connectors in order to connect the application directly to your data sources and generate datasets.

The general logic to import data in is the following:

  • Connectors hold credentials & connection information (url, etc.)
  • Datasources point to a specific table or file
  • Datasets are imported form datasource

You can see all your existing Connectors in the data section or your project, in the Connectors tab

List your connectors

Connector List

Create Connector

By clicking on the “new connector” button, you will be able to create and configure a new connector. You will need to provide information depending on connector’s type in order for the platform to be able to connect to your database/file server.


TIPS : you can test your connector when configured by clicking the “test connector” button.


You can connect any SQL Database by providing standard information :

  • an host url
  • a port
  • a username
  • a password
Create a new connector

Create a new sql connector


FTP and SFTP use the same informations as an SQL database :

  • an host url
  • a port
  • a username
  • a password
Create a new connector

Create a new ftp connector

When creating an FTP or SFTP connector, remember that the connector will open to the root folder of your ftp server so you must use the complete path for datasrouce created from this connectors.

Amazon S3

You can get any dataset hosted on Amazon S3 storage by using an acess key ( see The Amazon Guide to get yours )

Create a new connector

Create a new S3 connector


If you have data hosted on Google Cloud Bucket, you can connect your bucket with the GCP connector.

Create a new connector

Create a new GCP connector

GCP connector required a json file with your bucket credentials. See here how to get them. Your key should look like that :

"type": "service_account",
"project_id": "project-id",
"private_key_id": "key-id",
"private_key": "-----BEGIN PRIVATE KEY-----\nprivate-key\n-----END PRIVATE KEY-----\n",
"client_email": "service-account-email",
"client_id": "client-id",
"auth_uri": "",
"token_uri": "",
"auth_provider_x509_cert_url": "",
"client_x509_cert_url": ""

Once you got your GCP json credentials file, just upload it to connect your Bucket

Once connectors are added, you will find them in the list of all your connectors. You can, by clicking on the action button :

  • test the connector
  • edit the connector
  • delete the connector

Once at least one connector is well configured, you will be able to use the data sources menu in order to create CSV from your database or file server.

Data sources

data sources

  • need a connector
  • may be used as input of a pipeline
  • may be used to import dataset

Datasources represent « dynamic » datasets, whose data can be hosted on an external database or filesystem. Using a pre-defined connector, you can specify a query, table name or path to a file to extract the data.

Each datasource is one tabular dataset that can be import to Prevision. All your datasources are available from the datas section of you project, under the Data sources tab.

See all my Datasource

View all my datasource

By clicking the New datasource button you can create a datasource from a connector

A datasource is created from a connector thus you always need to select one when creating a new datasource.

Create datasource instead of dataset allows to point to dynamic data and thus schedule train and prediction on always the same datasource while the datas are updated.

For example, you can create a « weekly_sales » datasource that is pointing to a database table where data about sales are loaded each Sunday and then schedule a predit from this datasource each Monday.

Table as a datasource

You can create a datasource from any table from an sql connector. When selecting an SQL connector, you will be prompted to input a database and table names :

Import a table from a database

Import a table from a database

Using this for train or predict will import all the table. Be warned that if the table is big, the import process may last long. In most of case you’d better use a query.

Query as a datasource

From an SQL connector you can create datasource with an sql query :

Import a table from an SQL Query

Import a table from an SQL Query

Any query can run as long as your source database support it

S/FTP server file as datasource

When using an FTP or SFTP server, you could set an csv file as datasource :

Import a file from FTP server

Import a file from FTP server

Note that :

  • you could ony import csv data
  • the path to you file starts from the root of your ftp server

S3 Bucket

If you want to create a datasource from a file in Amazon S3 Storage, you must input name of the bucket and name of the file :

Import a file from an S3 Bucket

Import a file from an S3 Bucket

Only CSV files are supported and you can not import images folder from S3 Bucket.

Datasource from GCP

GCP Connectors offer two options :

  • either point to a file in a bucket
  • or point to a table of a big Query database

If you use the storage method, you have to input name of your bucket and name of your file :

Import a file from a GCP  Bucket

Import a file from a GCP Bucket

When using BigQuery, you must input the name of the dataset and the name of the Table

Import a file from a GCP  Big Query Table

Import a file from a GCP Big Query Table

Once you input all your information, click on Test Data Source to test it and then on Save Data Source when datasource is fine. The Data Source is created immediately and is displayed in your list of datasource.

Note that data are notimported into your account.

You can either build an experiment by creating a dataset from your datasource or build a pipeline in order to schedule predictions

Import your datas

Once you created a datasource, you can import it as a dataset to start to experiment by going to the Datasets tab of your projects’s data section :

Your Dataset

List all your datasets

Clicking on the import dataset button opens a modal windows where you can select a datasource previously created.

Import Datas from a datasource

Import Datas from a datasource

By clicking on save, the importation will start and your dataset and its status will be displayed in your dataset list. There are 3 status :

  1. Copy on going : dataset is copied from remote datasource
  2. Dataset Statistics pending : copy is done and dataset analysis is running
  3. Ready to be used : you can start to experiment ( or launch an embedding )


In the same way that Datasources are used to import data into, Exporters are used when you write the data generated in the platform to an external database or filesystem. They also require a connector, and have similar configuration options.

Once you had create an exporter, you may use it :

  • to write the result of a pipeline
  • to export one of your dataset


  • need a connector
  • may be use as output of a pipeline
  • may be used to export dataset

You can view all your exporters in the Exporters tab of your project’s Data section

List of exporters

List Your exporters

Using an exporter allows you to deliver your dataset transformations and prediction to your user or stakeholder either once or on a scheduled basis.

Create an exporter

When clicking on the new exporter button inside the Exporters tabs , you will be prompted to enter some information to the location you want to export your data when using this exporter, depending on the connector used.

Whatever the connector you used, you need to select an overwrite mode :

Exporters overwrite options

Exporters overwrite options

The options are :

  • Add a timestamp: a timestamp will be added to the file name. For example label_cheezam.csv becomes label_cheezam_2021-07-12T15:22:29.csv ( or the table name )
  • Cancel export: if a file or a table with the same name already exists, cancel the export and do not overwrite it.
  • Overwrite File: overwrite the file or table if exists.

Depending on your usecase, you may choose one of this options. Of course in order to works, your exporter must used a connecotr with write permissions.


Create an ftp exporter

Create an ftp exporter

For FTP and SFTP, you only have to enter the path where to export your dataset. If it does not exist on the server, it won’t be created.

S3 and GCP

For Amazon S3 and google bucket, you need to input the name of the bucket and the name of a file for saving.

Create a storage exporter

Create a storage exporter

SQL Database

When exporting to a remote databse, you must input both the database name and a table name.

Create a db exporter

Create a db exporter

Using an exporter - export your dataset

Typical examples for exporters are :

  • delivering predictions to external system
  • delivering transformed dataset to external system

Once you had created an exporter, you can use it on any datasets from your dataset list, with the action button

export from dataset

export form dataset List

When selecting the « Use an exporter » action from a dataset, you only have to select the exporter to use and then click the button export

export from dataset

export a dataset

The export of your dataset will start to the location you enter when creating your exporter, with the name input in the exporter. Status of current exportation is available in the list of exporters :

export from dataset

Last export status

And the status all all your export done ever done with one particuliar exporter is available by clicking on its name in the exporter list :

exporter history

Exporter history

Usage in a Pipeline

Any exporter can be used in a pipeline as an output node, meaning that each time the pipeline will be executed, the exporter will be used to write the result of the pipeline execution on a remote location

A typical pipeline

A typical pipeline : import data, make a prediction and export data

The exporter to used for your pipeline will be input when configuring a schedule run, in the configuration screen :

using a previous exporter

Using a user defined exporter in a schedule run

Then each time your pipeline is executed, the exporter will write the pipeline output to the location you use as exporter parameter when creating it ( either a file location or a database table )