As a data engineer, you hold all the cards to make data easily accessible to your business teams. Your team just requested a Square to Snowflake connection on priority. We know you don’t want to keep your data scientists and business analysts waiting to get critical business insights. If this is a one-time thing, exporting data with the help of CSV files is helpful. Or, you can hunt for a no-code tool that fully automates & manages data integration for you while you focus on your core objectives.

Well, look no further. This article provides a step-by-step guide to connecting Square to Snowflake effectively and quickly delivering data to your finance team. Hevo doesn’t support Square as a source at the moment, but it will be available soon. 

Replicate Data from Square to Snowflake Using CSV

To start replicating data from Square to Snowflake, firstly, you need to export data as CSV files from Square, then import the CSV files into Snowflake and modify your data according to your needs.

  • Step 1: You must navigate to Actions > Export Library > Confirm Export by visiting Items section in the Square dashboard. You can select between xlsx and CSV files. Based on the file you select, it will be downloaded to your local system. The important factor to note is that the values will be in text format, you will need to change the required fields into number fields.
  • Step 2: You can upload CSV files to Snowflake using the Data Load Wizard present in the Snowflake web interface. You can quickly load data into Snowflake by selecting the tables you want to load and clicking the Import button in the Snowflake Web UI. By deleting all staging files as soon as they are loaded into the data warehouse, the staging and data-loading tasks are clubbed into one action.

Learn more about loading CSV files through the web interface and other practical methods here.

Square to Snowflake: Export to Snowflake
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This 2-step process using CSV  is a great way to replicate data from Square to Snowflake effectively. It is optimal for the following scenarios:

  • One-Time Data Replication: When your finance team needs the Square data only once in a long period. 
  • No Data Transformation Required: If there is a negligible need for data transformation and your data is standardized, then this method is ideal. 
  • Small Amount of Data: Since the amount of data is small, the accuracy of data replication would be high, making this method a good fit.

In the following scenarios, using CSV might not be a great fit:

  • Data Mapping: Only basic data can be moved. Complex configurations cannot take place. There is no distinction between text, numeric values, null, and quoted values.
  • Time Consuming:  If you plan to export your data frequently, there may be better choices than the CSV method since it takes a significant amount of time to replicate the data using CSV files. 

When the frequency of replicating data from Square 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… 

Automate the Data Replication process using a No-Code Tool

Here are the 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 a 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.

Hevo will support Square as a source soon, you will have to provide basic details like credentials, data pipeline name, etc., and configure your Square Source.

Till then, you can have a look at the exhaustive list of sources provided by Hevo here.

What Can You Achieve by Migrating Your Data from Square to Snowflake?

Here’s a little something for the data analyst of your team. We’ve mentioned a few core insights you could get by replicating data from Square to Snowflake. Does your use case make the list?

  • Which campaigns have the most support costs involved?
  • For which geographies are marketing expenses the most?
  • Which campaign is more profitable?
  • What does your overall business cash flow look like?
  • Which sales channel provides the highest purchase orders?

Summing It Up

CSV files is the right path for you when your team needs data from Square 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 the data demands of your product or finance 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 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 Square to Snowflake! Let us know in the comments section below!

Harsh Varshney
Research Analyst, Hevo Data

Harsh comes with experience in performing research analysis who has a passion for data, software architecture, and writing technical content. He has written more than 100 articles on data integration and infrastructure.

No-code Data Pipeline For Snowflake