BigCommerce to Superset

This page provides you with instructions on how to extract data from BigCommerce and analyze it in Superset. (If the mechanics of extracting data from BigCommerce 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 BigCommerce?

BigCommerce is an ecommerce platform that provides a hosted shopping cart for online merchants, inventory and order management features, payment integration, and marketing tools.

Getting data out of BigCommerce

BigCommerce provides several APIs that let developers retrieve customer, order, storefront, catalog, payment, and other information stored in the platform. For example, to get information about a particular product, you would call GET /catalog/products/{product_id}.

Sample BigCommerce data

Here's an example of the kind of response you might see with a query like the one above.

{
  "data": {
    "id": 174,
    "name": "1L Le Parfait Jar",
    "type": "physical",
    "sku": "",
    "description": "

Le Parfait Jars are awesome. You should buy some.

", "weight": 1, "width": 0, "depth": 0, "height": 0, "price": 7.95, "cost_price": 0, "retail_price": 10, "sale_price": 0, "map_price": 0, "tax_class_id": 0, "product_tax_code": "", "calculated_price": 7.95, "categories": [ 23, 21 ], "brand_id": 36, "option_set_id": null, "option_set_display": "right", "inventory_level": 0, "inventory_warning_level": 0, "inventory_tracking": "none", "reviews_rating_sum": 0, "reviews_count": 0, "total_sold": 7, "fixed_cost_shipping_price": 0, "is_free_shipping": false, "is_visible": true, "is_featured": false, "related_products": [ -1 ], "warranty": "", "bin_picking_number": "", "layout_file": "product.html", "upc": "", "mpn": "", "gtin": "", "search_keywords": "jar, glass", "availability": "available", "availability_description": "", "gift_wrapping_options_type": "any", "gift_wrapping_options_list": [], "sort_order": 0, "condition": "New", "is_condition_shown": false, "order_quantity_minimum": 0, "order_quantity_maximum": 0, "page_title": "", "meta_keywords": [], "meta_description": "", "date_created": "2018-08-15T14:48:46+00:00", "date_modified": "2018-09-05T20:42:07+00:00", "view_count": 10, "preorder_release_date": null, "preorder_message": "", "is_preorder_only": false, "is_price_hidden": false, "price_hidden_label": "", "custom_url": { "url": "/all/1l-le-parfait-jar/", "is_customized": true }, "base_variant_id": 345, "open_graph_type": "product", "open_graph_title": "", "open_graph_description": "", "open_graph_use_meta_description": true, "open_graph_use_product_name": true, "open_graph_use_image": true }, "meta": {} }

Preparing BigCommerce 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. BigCommerce's 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 BigCommerce 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, BigCommerce's API results include fields like date_created 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 BigCommerce to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing BigCommerce 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 BigCommerce to Redshift, BigCommerce to BigQuery, BigCommerce to Azure SQL Data Warehouse, BigCommerce to PostgreSQL, BigCommerce to Panoply, and BigCommerce 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 BigCommerce to Superset automatically. With just a few clicks, Stitch starts extracting your BigCommerce 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.