Working with Data Connectors#

In addition to the Edge internal file store (Working with Files), you can configure Edge to connect to external data sources such as SQL databases and S3 buckets. Data connectors are the mechanism to address this.

A data connector is a record in Edge which points to a remote resource (SQL database, S3 bucket, HTTP API), combined with a standard Python API for retrieving data. This makes it very easy to access information directly from a Jupyter notebook.

Managing connectors in the Data App#

Probably the easiest way to get started with data connectors is by browsing in the Data App. Even if your administrator has not given you access to create connectors yourself, you can still view the contents of existing connectors. For example, here are the contents of a remote S3 bucket, displayed in the Data App:


To create a new connector (if you have the required permissions), click on the "+" button and a dialog will prompt you to select a connector type and fill out basic information. The various types are described in this document.


Working with connectors in Python#

The built-in EdgeSession (edge) object in your Jupyter notebook will allow you to access data connectors, via the edge.sources attribute. Here's how the two connectors (SQL and S3) above show up in Python:

>>> edge.sources.list_names()
['data-warehouse-sql', 'data-lake-s3']

You'll notice the names are a little different than they appear in the Data App user interface. That's because each connector has a "short name" (data-warehouse-sql or data-lake-s3 in this example), as well as a "title" (Data Warehouse (SQL) and Data Lake (S3)).

To retrieve a connector, use its short name:

>>> conn = edge.sources.get('data-warehouse-sql')

The exact details of each connector's API vary, but all have some basic attributes in common, including name and title:

>>> conn.title
'Data Warehouse (SQL)'

The SQL Connector#

The SQL connector allows you to connect to a remote SQL database. Currently, only PostgreSQL databases are supported. Viewing a SQL connector in the Data App will display a list of tables, and allows you to click on each of them to preview the data:


In Python, you have programmatic access to both the table names and the table contents, loaded as Pandas data frames. Both are accessed through the .tables attribute, which provides a dictionary-like interface:

>>> list(conn.tables)
['experiments', 'batchresults', 'formulations']
>>> table = conn.tables['experiments']
>>> table.to_dataframe()
    index Experiment ID  Description
 0      0        6fbfee  Unprocessed
 1      1        9f47e9      Mixture
 2      2        8e3ca0     Hot/Cold
 3      3        8c6d14  Pressurized
 4      4        39c51a    High-Temp

The S3 Connector#

The S3 connector allows browing the contents of a remote data store as if it were a file system. The Python interface borrows heavily from that used by the Edge internal file store (Working with Files). In this case, file access begins via the .root attribute, which represents the root of the virtual file system:

>>> conn = edge.sources.get('data-lake-s3')
>>> conn.root.list()
['Experiment data',
 'SEM micrographs',

Files, and nested "folders", are retrieved using open():

 >>> subfolder ='Experiment data')
 >>> subfolder.list()

>>> myfile ='sample_medium_0.jpg')
>>> type(myfile)

The OpenAPI3 Connector (beta)#

This connector is will allow you to autogenerate a Python API based on an OpenAPI3 JSON specification. Here's an example of the Python API it generates, based on the popular "Pet Store" demo HTTP API:

>>> conn = edge.sources.get('petstore')
>>> client = conn.generate_new_client()
>>> pets =

In this example, the findPetsByStatus method is automatically generated based on information available in the remote HTTP definition. You can browse that definition here: