As a data engineer, you hold all the cards to make data easily accessible to your business teams. Your team just requested a BigCommerce to Redshift connection on priority. We know you don’t wanna keep your data scientists and business analysts waiting to get critical business insights. As the most direct approach, you can go straight for the CSV files exporting if this is a one-time thing. Or, hunt for a no-code tool that fully automates & manages data integration for you while you focus on your core objectives.
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Well, look no further. With this article, get a step-by-step guide to connecting BigCommerce to Redshift effectively and quickly, delivering data to your marketing team.
Replicate Data from BigCommerce to Redshift Using CSV
To start replicating data from BigCommerce to Redshift, firstly, you need to export data as CSV files from BigCommerce, then import the CSV files into Redshift and modify your data according to the needs.
- Step 1: You can export the data as CSV or XML file. You can export data of all the objects such as Products, Orders, Customers, E-Commerce, etc.
- Step 2: You can upload the CSV to Redshift using an Amazon S3 bucket. Firstly, you must upload the CSV data to the Amazon S3 bucket. To do so, create a manifest file(preferably gzip file) that contains the data of the CSV file to be uploaded. Once loaded into S3, run the COPY command to pull and get the file from S3 and load it into the desired table. If you have used gzip, your code will be of the following structure:
COPY <schema-name>.<table-name> (<ordered-list-of-columns>) FROM '<manifest-file-s3-url>' CREDENTIALS'aws_access_key_id=<key>;aws_secret_access_key=<secret-key>' GZIP MANIFEST;
Here the use of the CSV keyword is important as it helps Amazon Redshift to identify the file format. You also need to specify any column arrangements layouts or row headers to be discarded as shown below:
COPY table_name (col1, col2, col3, col4) FROM 's3://<your-bucket-name>/load/file_name.csv' credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>' CSV; -- Ignore the first line COPY table_name (col1, col2, col3, col4) FROM 's3://<your-bucket-name>/load/file_name.csv' credentials 'aws_access_key_id=<Your-Access-Key-ID>;aws_secret_access_key=<Your-Secret-Access-Key>' CSV INGOREHEADER 1;
This 2-step process using CSV files is a great way to replicate data from BigCommerce to Redshift effectively. It is optimal for the following scenarios:
- One-Time Data Replication: When your marketing team needs the BigCommerce data only once in a long period of time.
- No Data Transformation Required: If there is a negligible need for data transformation and your data is standardized, then this method is ideal.
In the following scenarios, using CSV files might be cumbersome and not a wise choice:
- Data Mapping: Only basic data can be moved. Complex configurations cannot take place. There is no distinction between text, numeric values, and null and quoted values.
- Two-way Synchronization: To achieve two-way synchronization, the entire process must be run frequently to access updated data on the destination.
- Time Consuming: If you plan to export your data frequently, the CSV method might not be the best choice since it takes time to recreate the data using CSV 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?
How about you focus on more productive tasks than repeatedly writing custom ETL scripts? This sounds good, right?
In these cases, you can…
Replicate data from BigCommerce to Redshift Using an Automated ETL Tool
Here, are the following benefits of leveraging a no-code tool:
- Automated pipelines allow you to focus on core engineering objectives while your business teams can directly work on reporting without any delays or data dependency on you.
- Automated pipelines provide a beginner-friendly UI. Tasks like configuring and establishing connection with source and destination, providing credentials and authorization details, performing schema mapping etc. are a lot simpler with this UI. It saves the engineering teams’ bandwidth from tedious preparation tasks.
For instance, here’s how Hevo, a cloud-based ETL tool, makes BigCommerce to Redshift data replication ridiculously easy:
Step 1: Configure BigCommerce as a Source
Authenticate and Configure your BigCommerce Source.
Step 2: Configure Redshift as a Destination
Now, we will configure Redshift as the destination.
Step 3: All Done to Setup Your ETL Pipeline
Once your BigCommerce to Redshift ETL Pipeline is configured, Hevo will collect new and updated data from BigCommerce every five minutes (the default pipeline frequency) and duplicate it into Redshift . Depending on your needs, you can adjust the pipeline frequency from 5 minutes to an hour.
Data Replication Frequency
|Default Pipeline Frequency||Minimum Pipeline Frequency||Maximum Pipeline Frequency||Custom Frequency Range (Hrs)|
|1 Hr||15 Mins||24 Hrs||1-24|
In a matter of minutes, you can complete this No-Code & automated approach of connecting BigCommerce to Redshift using Hevo and start analyzing your data.
Hevo offers 150+ plug-and-play connectors(Including 40+ free sources). It efficiently replicates your data from BigCommerce to Redshift, databases, data warehouses, or a destination of your choice in a completely hassle-free & automated manner. Hevo’s fault-tolerant architecture ensures that the data is handled securely and consistently with zero data loss. It also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
Hevo’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. By employing Hevo to simplify your data integration needs, you get to leverage its salient features:
- Fully Managed: You don’t need to dedicate time to building your pipelines. With Hevo’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
- Data Transformation: Hevo provides a simple interface to cleanse, modify, and transform your data through drag-and-drop features and Python scripts. It can accommodate multiple use cases with its pre-load and post-load transformation capabilities.
- Faster Insight Generation: Hevo offers near real-time data replication, so you have access to real-time insight generation and faster decision-making.
- Schema Management: With Hevo’s auto schema mapping feature, all your mappings will be automatically detected and managed to the destination schema.
- Scalable Infrastructure: With the increase in the number of sources and volume of data, Hevo can automatically scale horizontally, handling millions of records per minute with minimal latency.
- Transparent pricing: You can select your pricing plan based on your requirements. Different plans are clearly put together on its website, along with all the features it supports. You can adjust your credit limits and spend notifications for any increased data flow.
- Live Support: The support team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Take our 14-day free trial to experience a better way to manage data pipelines.Get started for Free with Hevo!
What Can You Achieve by Migrating Your Data from BigCommerce to Redshift?
Here’s a little something for the data analyst on your team. We’ve mentioned a few core insights you could get by replicating data from BigCommerce to Redshift. Does your use case make the list?
- What percentage of customers from a region have the most engagement with the product?
- Which features of the product are most popular in a country?
- Your power users are majorly from which location?
- What are the custom retention trends over a period of time?
- What is the trend of a particular feature adoption with time?
Summing It Up
Exporting and importing CSV files is the right path for you when your team needs data from BigCommerce once in a while. However, an ETL solution becomes necessary if there are rapid changes in the source and frequent data replication needs to be done to meet data demands of your product or marketing channel. You can free your engineering bandwidth from these repetitive & resource-intensive tasks by selecting Hevo’s 150+ plug-and-play integrations.Visit our Website to Explore Hevo
Saving countless hours of manual data cleaning & standardizing, Hevo’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 simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo for a ride? 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 replicating data from BigCommerce to Redshift! Let us know in the comments section below!