DataLinks API/SDK QuickStart Guide

​

Introduction

This guide shows you how to use the DataLinks web platform and SDK kit to find shared sales opportunities across two North American data sources. You will learn by doing: uploading files, connecting records, and exploring the results directly in the platform.

​

Scenario

You’re a sales manager for a dental products company in North America. Your goal is to see how your existing customer base overlaps with members of a major dental society to uncover new prospecting opportunities. You’ll work with two datasets:

• A JSON file that contains your current customers, including names, locations, and contact details
• A dental society member list that includes dentists and clinic owners across the United States and Canada

Using the DataLinks web platform, you’ll ingest both datasets, manage how they connect, and query the linked data to find customers who are also society members or have professional ties to them.

​

Outcome

By the end of this guide, you will have a table showing which of your current customers also appear in the dental society list. You can explore these matches in the DataLinks platform or export them as a CSV file for follow-up. This exercise helps you practice the full workflow in DataLinks: importing data, linking records, exploring relationships, and using the insights for focused outreach.

​

Prerequisites

To follow along on your machine and complete the steps in this guide, make sure you have the following ready:

1. Access to a DataLinks account.

2. Download a copy of quickstart_api.zip to your local machine and extract it.
3. Python installed on your local machine and the package-manager pip available. To download Python, see the official website.
4. The DataLinks SDK installed on your machine.
   For installation instructions, see the official DataLinks SDK Installation Steps.
5. A code editor such as Microsoft Visual Studio Code, which is free to download on Windows, Linux, or Mac.

​

Part 1: Create your namespace and first dataset

1. Log in to the DataLinks platform.
2. Click Create New Dataset.
3. Enter the following dataset name: customer_list.
   Use underscores, not spaces, when naming your datasets.
4. Enter namespace dental_prospects_api.
5. Ensure the Dataset visibility is set to Private using the toggle.
6. Click Create dataset and upload data.

​

Part 2: Upload the structured customer list

Now that you’ve created an empty dataset, you need to populate it with data. To do so, follow these steps:

1. Click anywhere in the Click to upload area to open the file explorer.
2. Select customer_list.json from the quickstart_api.zip folder you downloaded, then click Open.
3. Wait while DataLinks ingests your data.
4. Click the eye icon under Ingestion Results to review the data and confirm that everything looks correct.
5. Scroll down to the bottom of the page, then click Upload data.
6. Click Append to confirm.

​

Part 3: Create your DataLinks API token.

1. Log in to the DataLinks platform.
2. Click Settings in the left navigation menu.
3. Click + Add a new token.
4. Enter the name dental_prospects and set an expiration date for the token (for example, one month).
5. Click + Create token.
6. Copy and save your token. (You’ll need in later steps.)
7. Click I’ve copied the token to close.

​

Part 4: Use a script to upload and transform unstructured data

For the structured data you uploaded through the DataLinks web platform, you first created a dataset (Part 1) and then uploaded data into it (Part 2). For the unstructured data in this section, you’ll create and populate the dataset at the same time using a Python script. The unstructured data file you’ll use is a simple text file created by copying and pasting a list of dental society members into a text file. This is the society_members_api.txtfile you downloaded quickstart_api.zip folder you downloaded in the Prerequisites section. To upload and transform your data, follow the steps below:

1. Open the ingest_dental.pyfile in your code editor.
2. Add the following information to the file:
   • On line 17, apikey, paste in your API token.
   • On line 19, namespace, enter the namespace you’re using (dental_prospects_api)
   • On line 20, objectname, enter the name for the new dataset you’re creating (society_members)
   • On line 26, textfile, enter society_members_api.txt and update the location if needed based on where you’ve placed the file in your directory.
   • On line 49, helper_prompt, paste in the following prompt:
     Copy

     Ask AI

     This is the data from a dental societies where the names of members (dental practices), the addresses, the phone numbers, and the websites, are included. The file contains several entries.  
     # **Instructions**
     * Extract **all data** provided. 
     * **Do not skip any entry.**
     
     Whenever possible abide to the following schema:
     MemberName = this is the set of names that are used to define the member entity, which may be a person, or may be an entity like and institution, business, etc. Do the import without academic titles such as "Dr." for Doctor, "Prof." for professor and similar, and without personal honorifics like "Ms.", "Mr.", "Frau", "Herr", and extensions of these like "Miss".
     
     Address = this is the street address belonging to the member name.
     
     city = this is the name of the city to which the address belongs to.
     
     Country = this is the name of the country to which the address belongs to. Normalize all names to English.
     
     Geo = these are the geographical coordinates that you need to calculate from the Address. These include latitude and longitude. The format should be "XX,XX", meaning "latitude,longitude".
     
     Phone = this is the phone number associated with the member name. If it does not contain the country code (+XX) add the respective country code based on country of member. Pay attention to which country we are dealing with and substitute the leading 0 by the country code when appropriate.
     
     WebSite = this is the web address associated with the member name.
     

3. Verify everything looks correct, then save your file.
4. Run your Python program. (In Visual Studio Code, press the play icon in the top-right corner of your screen.)
5. Wait for the data to finish uploading. This may take 10-20 minutes.

​

Part 5: Manage connections

1. Open the DataLinks web platform again and return to the Home page.
2. Click the customer_list dataset in the dental_prospects namespace.
3. Click the Connections tab.
4. Click Select all under active Active Links. This will select all active links and ready them for removal.
5. Click Save connections. This will remove all active links.
6. Click the row containing customer_list.full_name and society_members.membername.
7. Click Add connections.

​

Next steps

Congratulations! You’ve used structured and unstructured data to find a list of prospects. Thank you for taking the time to try this guide and explore DataLinks. To learn more about DataLinks and how we can help your organization, get started by joining the DataLinks waitlist.