Re:amaze Data Integration with Snowflake

With Portable, integrate Re:amaze data with your Snowflake warehouse in minutes. Access your customer service platform data from Snowflake without having to manage cumbersome ETL scripts.

The Two Paths to Connect Re:amaze to Snowflake

There are two ways to sync data from Re:amaze into your data warehouse for analytics.

Method 1: Manually Developing a Custom Data Pipeline Yourself

Write code from scratch or use an open-source framework to build an integration between Re:amaze and Snowflake.

Method 2: Automating the ETL Process with a No-Code Solution

Leverage a pre-built connector from a cloud-hosted solution like Portable.

How to Create Value with Re:amaze Data

Teams connect Re:amaze to their data warehouse to build dashboards and generate value for their business. Let’s dig into the capabilities Re:amaze exposes via their API, outline insights you can build with the data, and summarize the most common analytics environments that teams are using to process their Re:amaze data.

Extract: What Data Can You Extract from the Re:amaze API?

Re:amaze is a customer service platform used for integrating live chat and helpdesk on online businesses.

To help clients power downstream analytics, Re:amaze offers an application programming interface (API) for clients to extract data on business entities. Here are a few example entities you can extract from the API:

  • Articles
  • Channels
  • Contacts
  • Contact Notes
  • Conversations
  • Messages
  • Reports
  • Response Templates
  • Staff
  • Status Page

You can visit the Re:amaze API Documentation to explore the entire catalog of available API resources and the complete schema definition for each.

As you think about the data you will need for analytics, don’t forget that Portable offers no-code integrations to other similar applications.

Regardless of the SaaS solution you use, it’s important to find a customer service platform with robust data available for analytics.

Load: Which Destinations Are Best for Your Re:amaze ETL Pipeline?

To turn raw data from Re:amaze into dashboards, most companies centralize information into a data warehouse or data lake. For Portable clients, the most common ETL pipelines are:

  1. Re:amaze to Snowflake Integration
  2. Re:amaze to Google BigQuery Integration
  3. Re:amaze to Amazon Redshift Integration
  4. Re:amaze to PostgreSQL Integration
Common Data Warehouses
Common Data Warehouses

Once you have a destination to load the data, it’s common to combine Re:amaze data with information from other enterprise applications like Jira, Mailchimp, HubSpot, Zendesk, and Klaviyo.

From there, you can build cross-functional dashboards in a visualization tool like Power BI, Tableau, Looker, or Retool.

Develop: Which Dashboards Should You Build with Re:amaze Data?

Now that you have identified the data you want to extract, the next step is to plan out the dashboards you can build with the data.

As a process, you want to consume raw data, overlay SQL logic, and build a dashboard to either 1) increase revenue or 2) decrease costs.

Replicating Re:amaze data into your cloud data warehouse can unlock a wide array of opportunities to power analytics, automate workflows, and develop products. The use cases are endless.

Now that we have a clear sense of the insights we can create, let’s compare the process of developing a custom Re:amaze integration with the benefits of using a no-code ETL solution like Portable.

Method 1: Building a Custom Re:amaze ETL Pipeline

To build your own Re:amaze integration, there are three steps:

  1. Navigate the Re:amaze API documentation
  2. Make your first API request
  3. Turn an API request into a complete data pipeline

Let’s walk through the process in more detail.

How to Interpret Re:amaze’s API Documentation

When reading API documentation, there are a handful of key concepts to consider.


There are many common authentication mechanisms. OAuth 2.0 (Auth Code and Client Credentials), API Keys, JWT Tokens, Personal Access Tokens, Basic Authentication, etc. For Re:amaze, it’s important to identify the authentication mechanism and how best to incorporate the necessary credentials into your API requests.

Re:amaze authentication is handled via HTTP Basic Auth. The header must contain 'application/json' as an Accept type of the resource url has to contain .json


It’s important to identify the Re:amaze API endpoints you want to use for analytics. Most APIs offer a combination of GET, POST, PUT, and DELETE request methods; however, for analytics, GET requests are typically the most useful. At times, POST requests can be used to extract data as well.

For Re:amaze, the notes endpoint is a great place to get started.

Request Parameters

For each API endpoint you would like to use for analytics, you need to understand the method (GET, POST, PUT, or DELETE) and the URL, but there are other considerations to take into account as well. You should look out for pagination mechanics, query parameters, and parameters that are added to the request path.

Not all Re:amaze resources have pagination. Re:amaze uses 'page_size', 'page_count' and 'total_count' for pagination.

Re:amaze relies mostly on path parameters related to the resource queried. There are some exceptions. To get contact identities, you an email parameter is required.

How Do You Call the Re:amaze API? (Tutorial)

  1. Follow the instructions above to read the Re:amaze API documentation
  2. Identify and collect your credentials for authentication
  3. Pick the API resource you want to pull data from
  4. Configure the necessary parameters, method, and URL to make your first request (e.g. with curl or Postman)
  5. Add your credentials and make your first API call . Here is an example request using curl (without real credentials):
curl 'https://{brand}{email}/notes'       -u {login-email}:{api-token}       
-H 'Accept: application/json'       
-H 'Content-type: application/json'       
-X POST -d '{ 'body': 'New note body' }'

How Do You Maintain a Custom Re:amaze to Snowflake ETL Pipeline?

Making a call to the Re:amaze API is just the beginning of maintaining a complete custom ETL pipeline.

Here is a getting-started guide to building a production-grade pipeline for Re:amaze:

  • For each API endpoint, define schemas (which fields exist and the type for each)
  • Process the API response and parse the data (typically parsing JSON or XML)
  • Handle and replicate nested objects and custom fields
  • Identify which Re:amaze fields are primary keys and which keys are required vs. optional
  • Version control your changes in a git-based workflow (using GitHub, GitLab, etc.)
  • Handle code dependencies in your toolchain and the upgrades that come with each
  • Monitor the health of the upstream API, and —when things go wrong— troubleshoot via the status page, reach out to support, and open tickets
  • Handle error codes (HTTP error codes like 400s, 500s, etc.)
  • Manage and respect rate limits imposed by the server

We won’t go into detail on all of the items above, but rate limits are a great example of the complexity found in a production-grade data pipeline.

Re:amaze states that their api is limited per API Token but does not specify what the value of that limit is.

If you don’t respect rate limits, and if you can’t handle server responses (like 429 errors with a Retry-After header), your pipeline can break, and analytics can become out-of-date.

What Are the Drawbacks of Building the Re:amaze ETL Pipeline Yourself?

You can probably tell at this point that there is a lot of work that goes into building and maintaining an ETL pipeline from Re:amaze to your data warehouse.

If you want less development work, faster insights, and no ongoing responsibilities, you should consider a cloud-hosted ETL solution.

Let’s walk through the setup process for a no-code ETL solution and its benefits.

Method 2: Using a No-Code Re:amaze ETL Solution

No-code ETL solutions are simple. Vendors specialize in building and maintaining data pipelines on your behalf. Instead of starting from scratch for each integration. Companies like Portable create connector templates that can be leveraged by hundreds or thousands of clients.

Step-By-Step Tutorial for Configuring Your Re:amaze ETL Pipeline

Off-the-shelf ETL tools offer a no-code setup process. Here are the instructions to connect Re:amaze to your cloud data warehouse with Portable.

  1. Create an account (no credit card required)
  2. Add a source —search for and select Re:amaze
  3. Authenticate with Re:amaze using the instructions in the Portable console
  4. Select Snowflake and authenticate
  5. Set up a flow connecting Re:amaze to your analytics environment
  6. Run your flow to replicate data from Re:amaze to your warehouse
  7. Use the dropdown to set your data flow to run on a cadence

What Are the Benefits of Using Portable for Re:amaze ETL?

No-Code Simplicity

Start moving Re:amaze data in minutes. Save yourself the headaches of reading API documentation, writing code, and worrying about maintenance. Leave the hassle to us.

Easy to Understand Pricing

With predictable, fixed-cost pricing per data flow, you know exactly how much your Re:amaze integration will cost every month.

Fast Development Speeds

Access lightning-fast connector development. Portable can build new integrations on-demand in hours or days.

Hands-On Support

APIs change. Schemas evolve. Re:amaze will have maintenance issues and errors. With Portable, we will do everything in our power to make your life easier.

Unlimited Data Volumes

You can move as much data from Re:amaze to Snowflake as you want without worrying about usage credits or overages. Instead of analyzing your ETL costs, you should be analyzing your data.

Free to Get Started

Sign up and get started for free. You don’t need a credit card to manually trigger a data sync, so you can try all of our connectors before paying a dime.

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