So, you’re a BigCommerce user, right? It’s always a delight to speak with someone who has set up an online store for a low cost and with minimal effort. Focusing on adding different product variants with utmost simplicity while keeping customer experience at the top is what makes you a great player. 

BigCommerce has consolidated dashboards based on multiple reports of products, orders, and customer data. But, there would be times when this data needs to be integrated with that of other functional teams. That’s where you come in. You take the responsibility of replicating data from BigCommerce to a centralized repository so that analysts and key stakeholders can make super-fast business-critical decisions.

We’ve prepared a simple and straightforward guide to help you replicate data from BigCommerce to Databricks. Read the 2 simple methods to understand the replication process quickly.

How to Replicate Data From BigCommerce to Databricks?

To replicate data from BigCommerce to Databricks, you can either:

  • Use CSV Files or 
  • A no-code automated solution. 

We’ll cover replication via downloading CSV files next.

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Replicate Data from BigCommerce to Databricks Using CSV Files

BigCommerce, being a hosted e-commerce solution, majorly stores data about customers, products, orders, online stores, etc. 

You can export the following types of data from BigCommerce:

  • Products & Product Options
  • Product SKUs
  • Customers
  • Orders
  • BigCommerce Analytics Reports
  • 301 Redirects
  • Newsletter Sign-ups

Let’s dive into the process of replicating this data from BigCommerce to Databricks in CSV format:

Step 1: Export Data from BigCommerce

  • Open BigCommerce and go to the navigation pane to the left of the screen.
  • Select the category of data for which you want to export data. Here, the 3 categories are: “Products,” “Customers,” and “Orders.”
Category selection in BigCommerce
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  • After selecting the category, click on the “Export” option.
Export option
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  • Then, click on the “Bulk Edit” export option.
Bulk Edit export option in BigCommerce
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  • This template will provide you with the same column headers used by BigCommerce, which will streamline the process and save you the trouble of selecting import settings later.
  • Additionally, you can build your own custom export template from scratch or customize a copy of one to suit your needs. For that, click on the “Export Templates” option in the top-right corner.
Export Templates for creating customized templates
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  • Then, select the format in which you want to export your data. BigCommerce supports 2 file formats for exporting data, i.e., CSV and XML. In this case, we’re going with CSV as our file format.
  • If you have a large product catalog, order history, or customer base, you may want to save the export to the server to avoid timeouts during download.
Save export to the server for later download
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  • Click on the “Continue” button.
Click on the continue button
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  • A pop-up window arises. Click on the following link to start the export process.
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  • Now, click on the “Download my <category> file” link to save the file to your computer.
Exporting as CSV file
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Step 2: Import CSV Files into Databricks

  • In the Databricks UI, go to the side navigation bar. Click on the “Data” option. 
  • Now, you need to click on the “Create Table” option.
  • Then drag the required CSV files to the drop zone. Otherwise, you can browse the files in your local system and then upload them.

Once the CSV files are uploaded, your file path will look like: /FileStore/tables/<fileName>-<integer>.<fileType>

Creating table while exporting data from BigCommerce to Databricks
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If you click on the “Create Table with UI” button, then follow along:

  • Then select the cluster where you want to preview your table.
  • Click on the “Preview Article” button. Specify the table attributes such as table name, database name, file type, etc. Then, select the “Create Table” button.
  • Now, the database schema and sample data will be displayed on the screen.

If you click on the “Create Table in Notebook” button, then follow along:

  • A Python notebook is created in the selected cluster.
Opening Python notebook
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  • You can edit the table attributes and format using the necessary Python code. You can refer to the below image for reference.
Displaying the structure of the dataframe
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  • You can also run queries on SQL in the notebook to get a basic understanding of the data frame and its description.
Querying the data in SQL
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In this case, the name of the table is “emp_csv.” However, in our case, you can keep it as per your choice.

  • Now, on top of the Pandas data frame, you need to create and save your table in the default database or any other database of your choice.
Saving the csv file in a database
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In the above table, “mytestdb” is a database where we intend to save our table. 

  • After you save the table, you can click on the “Data” button in the left navigation pane and check whether the table has been saved in the database of your choice.
Checking the table in the database
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Step 4: Modify & Access the Data

  • The data now gets uploaded to Databricks. You can access the data via the Import & Explore Data section on the landing page.
For modifying and accessing the data in Databricks
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  • To modify the data, select a cluster and click on the “Preview Table” option.
  • Then, change the attributes accordingly and select the “Create Table” option.

With the above 3-step approach, you can easily replicate data from BigCommerce to Databricks using CSV files. This method performs exceptionally well in the following scenarios:

  • Low-frequency Data Replication: This method is appropriate when your product and marketing teams need the BigCommerce data only once in an extended period, i.e., monthly, quarterly, yearly, or just once. 
  • Dedicated Personnel: If your organization has dedicated people who have to select categories manually, customize templates, then download and upload CSV files, then accomplishing this task is not much of a headache.
  • Low Volume Data: It can be a tedious task to repeatedly select different categories, select templates or customize them, and download & upload CSV files. Moreover, merging these CSV files from multiple departments is time-consuming if you are trying to measure the business’s overall performance. Hence, this method is optimal for replicating only a few files.

When the frequency of replicating data from BigCommerce increases, this process becomes highly monotonous. It adds to your misery when you have to transform the raw data every single time. With the increase in data sources, you would have to spend a significant portion of your engineering bandwidth creating new data connectors. Just imagine — building custom connectors for each source, transforming & processing the data, tracking the data flow individually, and fixing issues. Doesn’t it sound exhausting?

Instead, you should be focussing on more productive tasks. Being relegated to the role of a ‘Big Data Plumber‘ that spends their time mostly repairing and creating the data pipeline might not be the best use of your time.

To start reclaiming your valuable time, you can…

Replicate Data from BigCommerce to Databricks Using an Automated ETL Tool

Going all the way to write custom scripts for every new data connector request is not the most efficient and economical solution. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare.

You can streamline the BigCommerce to Databricks data replication process by opting for an automated tool. Here are the benefits of leveraging an automated no-code tool:

  • It allows you to focus on core engineering objectives while your business teams can jump on to reporting without any delays or data dependency on you.
  • Your sales & support teams can effortlessly enrich, filter, aggregate, and segment raw BigCommerce data with just a few clicks.
  • The beginner-friendly UI saves the engineering team hours of productive time lost due to tedious data preparation tasks.
  • Without coding knowledge, your analysts can seamlessly create thorough reports for various business verticals to drive better decisions. 
  • Your business teams get to work with near-real-time data with no compromise on the accuracy & consistency of the analysis. 
  • You get all your analytics-ready data in one place. With this, you can quickly measure your business performance and deep dive into your BigCommerce data to explore new market opportunities.

For instance, here’s how Hevo Data, a cloud-based ETL tool, makes BigCommerce to Databricks data replication ridiculously easy:

Step 1: Configure BigCommerce as a Source

Configuring BigCommerce as a source
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Step 2: Configure Databricks as a Destination

Configuring Databricks as a destination
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All Done to Setup Your ETL Pipeline

After implementing the 2 simple steps, Hevo Data will take care of building the pipeline for replicating data from BigCommerce to Databricks based on the inputs given by you while configuring the source and the destination.

The pipeline will automatically replicate new and updated data from BigCommerce to Databricks every 1 hr (by default). However, you can also adjust the data replication frequency as per your requirements.

Data Pipeline Frequency

Default Pipeline FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
1 Hr1 Hr48 Hrs1-48

For in-depth knowledge of how a pipeline is built & managed in Hevo Data, you can also visit the official documentation for BigCommerce as a source and Databricks as a destination.

You don’t need to worry about security and data loss. Hevo’s fault-tolerant architecture will stand as a solution to numerous problems. It will enrich your data and transform it into an analysis-ready form without having to write a single line of code.

By employing Hevo to simplify your data integration needs, you can leverage its salient features:

  • Reliability at Scale: With Hevo Data, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 
  • Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every state of the pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs. 
  • Stay in Total Control: When automation isn’t enough, Hevo Data offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    
  • Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo Data automatically maps the source schema with the destination warehouse so that you don’t face the pain of schema errors.
  • 24×7 Customer Support: With Hevo Data, you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. Moreover, you get 24×7 support even during the 14-day full-feature free trial.
  • Transparent Pricing: Say goodbye to complex and hidden pricing models. Hevo Data’s transparent pricing brings complete visibility to your ELT spending. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow. 
Get started for Free with Hevo Data!

What Can You Achieve by Replicating Your Data from BigCommerce to Databricks?

Replicating data from BigCommerce to Databricks can help your data analysts get critical business insights. Does your use case make the list?

  • Customers acquired from which channel have the maximum satisfaction ratings?
  • Your power users are majorly from which location?
  • How many orders were completed from a particular Geography?
  • What percentage of customers from a region have the most engagement with the product?
  • What is the Marketing Behavioural profile of the Product’s Top Users?

Summing It Up

Selecting a data category, creating customized templates, downloading, transforming, and uploading the CSV files would be the smoothest process when your product and marketing teams require data from BigCommerce only once in a while. But what if the product and marketing team requests data of multiple objects with numerous filters and customizations in the BigCommerce data every once in a while? Going through this process over and again can be monotonous and would eat up a major portion of your engineering bandwidth. The situation worsens when these requests are for replicating data from multiple sources.

So, would you carry on with this method of manually selecting categories, creating templates, and customizing them every time you get a request from the Product or Marketing team? You can stop spending so much time being a ‘Big Data Plumber’ by using a custom ETL solution instead.

A custom ETL solution becomes necessary for real-time data demands such as monitoring email campaign performance or viewing the sales funnel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources).

Visit our Website to Explore Hevo Data

Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can run complex SQL transformations from the comfort of Hevo Data’s interface and get your data in the final analysis-ready form. 

Want to take Hevo Data for a spin? Sign Up for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.

Share your experience of connecting BigCommerce to Databricks! Let us know in the comments section below!

mm
Former Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has written more than 100 articles on diverse topics related to data industry.

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