Overview

A Data Source is a saved, reusable connection to external data — an uploaded or referenced CSV/Excel file, a JSON file or URL, a database query, or an API endpoint. You create it once, then bind its columns into any test. This guide walks the Data Sources manager and the per-test Data Source configuration exactly as they appear in Shift Left Studio. Explore the capability at data sources.

Data Sources use [[variable]] tokens. This is a different system from the Data-driven testing panel, which uses {{row.COLUMN}} tokens — never mix the two. For the inline/no-code path, see Data-driven testing.

Before you begin

Open the Data Sources manager for your project. It opens as a panel titled Data Sources with a Create Data Source button in the top-right.
If no project is selected you'll see "Project ID is required" — pick a project first.
Existing sources are listed as cards showing the name, description, an uppercase type badge (e.g. CSV), and the created date. Each card has three icon buttons: Test Data Source (play), Edit Data Source (pencil), and Delete Data Source (trash).

Step 1 — Open the Create Data Source form

Click Create Data Source. A modal titled Create Data Source opens.
Fill the common fields:

Field	What it does
Name *	Required identifier for the source.
Description	Optional notes.
Type *	The connector type: CSV, Excel, Database, JSON, or API. Changing the type resets the type-specific fields below.

Step 2 — Fill the type-specific fields

CSV

Choose File Path or Upload File. For File Path, enter a path like /path/to/file.csv. For Upload File, pick a .csv file (it uploads when you save).
Set the Delimiter (default ,).
Tick Has Headers if the first row holds column names. When ticked, set Header Row (0-based) — the row number where headers live.
Optionally set Data Range (Optional) in startRow:endRow form, e.g. 2:100. Leave empty to read all rows.

Excel

Choose File Path (/path/to/file.xlsx) or Upload File (.xlsx/.xls).
Set Sheet Name (leave empty to use the first sheet).
Tick Has Headers and set Header Row (0-based) if applicable.
Optionally set Data Range (Optional), e.g. A1:C10 or Sheet1!A3:B4.

Database

Choose the Database Type: MySQL, PostgreSQL, SQL Server, SQLite, MongoDB, or Oracle.
For SQLite, enter the Database File Path (e.g. /path/to/database.db). For all others, enter Host, Port (the placeholder shows the default for the selected engine, e.g. 3306 for MySQL), Database, Username, and Password.
Enter the Query. For SQL engines use SQL like SELECT * FROM table LIMIT 100. For MongoDB use the documented JSON form, e.g. {"collection": "users", "find": {}, "options": {"limit": 100}}.

JSON

Choose File Path or URL — enter a local path (/path/to/file.json) or an https://… URL — or Upload File and pick a .json file. A value starting with http is treated as a URL automatically.

API

Enter the Endpoint URL (e.g. https://api.example.com/data).
Choose the Method: GET or POST.

Step 3 — Save

Click Create (the button reads Update when editing an existing source; during save it shows Creating… / Updating…, and Uploading… while a file uploads). Uploaded files attach after the source is created, so the source saves first and then the file transfers. Secrets such as passwords, tokens, and connection strings are never returned in listings.

Step 4 — Test the connection

On a source card, click the Test Data Source (play) button. A result banner appears on the card:

Success: "Connection successful. N rows found."
Failure: "Connection failed: …" with the error.

Use Edit Data Source to change settings (removing an uploaded file is done here via Remove), and Delete Data Source to remove it — a Delete data source confirmation asks "Are you sure you want to delete '…'? This cannot be undone."

Step 5 — Bind a data source into a test

Open the test and go to its data-source configuration:

Data Source — pick your source from the dropdown (each entry shows name and type, e.g. Customers (CSV)). Choose None (use default test data) to detach it.
A collapsible Preview (N rows, M columns) section shows the first rows so you can see the columns.
Auto-create variables from all columns — tick this checkbox to create one variable per column automatically. Leave it off to define variables selectively.

Variable Definitions

Under Variable Definitions, click Add Variable for each value you want to expose. The help text reads: "Define variables from data source columns. Use [[variableName]] in headers, assertions, body, or parameters." Each row has:

Field	What it does
Source field	The column to read (choose from the detected columns).
Use as: [[ … ]]	The variable name; reference it elsewhere as `[[variableName]]`. A copy button copies the `[[…]]` token to your clipboard.
Default value	Value used when the column is missing.
Map to param	Tick to also bind the variable straight into a test parameter, then pick the target from the Select parameter dropdown (each option shows the parameter name and location, e.g. `userId (query)`).

A collapsible Available Variables (N) panel lists every resulting [[variable]] with a sample value so you can confirm the names before using them.

Execution Strategy

Set Execution Strategy:

Single Row (use one row per execution) — uses one row; set the Row Index (0-based, default 0).
Iterate All (run test once per row) — runs the test once for every row in the source.

Troubleshooting

"Project ID is required to configure data sources" — the test isn't associated with a project; open it from within a project.
Columns show "Loading columns…" — the preview hasn't loaded yet; wait, or reselect the source.
Connection failed on Test Data Source — verify host/port/credentials or the file path/URL; the banner shows the underlying error.
A [[variable]] isn't substituted — confirm the variable name matches exactly, and that the Source field is set for that row.

Data Sources