Freshsales to Superset

This page provides you with instructions on how to extract data from Freshsales and analyze it in Superset. (If the mechanics of extracting data from Freshsales seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Freshsales?

Freshworks' Freshsales sales CRM platform provides lead management, AI-based lead scoring, and built-in phone, email, and activity capture.

Getting data out of Freshsales

Freshsales provides a REST API that lets developers retrieve information stored in the platform about leads, accounts, deals, and more. For example, to get information about a particular lead, you would call GET /api/leads/{id}.

Sample Freshsales data

	"lead": {
		"id": 1,
		"job_title": "Sales Manager",
		"department": "Sales",
		"email": "",
		"work_number": "(368) 493-2360",
		"mobile_number": "1-926-652-9503",
		"address": "604-5854 Beckford St.",
		"city": "Glendale",
		"state": "Arizona",
		"zipcode": "100652",
		"country": "USA",
		"time_zone": null,
		"do_not_disturb": false,
		"display_name": "Jane Sampleton (sample)",
		"avatar": "",
		"keyword": "B2B Success",
		"medium": "Blog",
		"last_seen": "2019-02-10T02:36:06-08:00",
		"last_contacted": "2019-02-08T02:36:06-08:00",
		"lead_score": 96,
		"stage_updated_time": "2019-02-10T02:36:06-08:00",
		"first_name": "Jane",
		"last_name": "Sampleton (sample)",
		"company": {
			"id": 2000010568,
			"name": " (sample)",
			"address": "160-6802 Aliquet Rd.",
			"city": "New Haven",
			"state": "Connecticut",
			"zipcode": "68089",
			"country": "United States",
			"number_of_employees": null,
			"annual_revenue": null,
			"website": "",
			"phone": "503-615-3947",
			"industry_type_id": 2492,
			"business_type_id": 354
		"deal": null,
		"links": {
			"conversations": "/leads/1/conversations?include=email_conversation_recipients%2Ctargetable%2Cphone_number%2Cphone_caller%2Cnote%2Cuser\u0026per_page=3",
			"activities": "/leads/1/activities"
		"updated_at": "2019-02-10T02:36:06-08:00",
		"has_authority": false,
		"facebook": null,
		"twitter": "",
		"linkedin": ""

Preparing Freshsales data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. Freshsales' documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Keeping Freshsales data up to date

At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Freshsales' API results include fields like updated_at that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

From Freshsales to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Freshsales data in Superset is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Freshsales to Redshift, Freshsales to BigQuery, Freshsales to Azure SQL Data Warehouse, Freshsales to PostgreSQL, Freshsales to Panoply, and Freshsales to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data from Freshsales to Superset automatically. With just a few clicks, Stitch starts extracting your Freshsales data via the API, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Superset.