Klaviyo to Snowflake Integration: 2 Easy Ways to Connect

Klaviyo is the best out there for seamlessly integrating with Shopify-powered online stores. It has several email automation and customer database management features that help you keep and engage lost customers. 

Snowflake’s Data Cloud is built on a cutting-edge data platform that is delivered as Software-as-a-Service (SaaS). Snowflake provides Data Storage, Processing, and Analytic Solutions that are faster, easier to use, and more flexible than traditional options.

This article explains two different methods to set up Klaviyo to Snowflake integration in a few easy steps. In addition to that, it also describes Klaviyo and Snowflake briefly.

Why Integrate Klaviyo to Snowflake?

Integrating Klaviyo to Snowflake has a lot of benefits. With the help of Klaviyo, automated bulk email campaigns can be improved. The information from this app will be used to analyze the effectiveness of the advertising campaigns, including Click Rates, Open Rates, Customer Interest, Verification Rate, and Demography.

As a result, you can import data from Klaviyo to Snowflake from your social media campaigns, Google Ads, Sales Database, Inventory Management Systems, Payment Gateways, and other useful applications, and analyze that data frequently.

When connecting Klaviyo to Snowflake, moving data from Klaviyo to Snowflake could solve some of the biggest data problems for businesses. This integration makes report generation and analysis simpler. It also saves time and increases efficiency.

You can identify your target market by looking at visitor clicks on ads, quickly selling products, call-to-action buttons, customer reviews, and products with payment problems. Additionally, you can optimize your advertising budgets and strategies by understanding your product demands. Using data from a variety of sources, you can pinpoint the ideal customers for your goods and use Klaviyo to promote through interesting emails. In Snowflake, combine all of these data to carry out further analysis.

Klaviyo to Snowflake Data Migration

• Method 1: Using Hevo Data to Set Up Klaviyo to Snowflake
• Method 2: Using Custom Code to Move Data from Klaviyo to Snowflake

Method 1: Using Hevo Data to Set Up Klaviyo to Snowflake

Hevo provides an Automated No-code Data Pipeline that helps you move your Klaviyo to Snowflake. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ Sources(including 40+ free sources) but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It takes a lot of time and effort to manually extract data. The time and energy that marketers spend on gathering data and creating reports lower their effectiveness.

There is a chance that this delay will cost money. Businesses may want to consider purchasing a cloud data pipeline like Hevo in place of this strenuous procedure. It is a highly automated data pipeline that gathers information from well-known apps and uploads it to cloud data storage services like Snowflake. Copy data from Klaviyo to Snowflake to automate reporting and make it easier to analyze for insightful business information.

Using Hevo Data Klaviyo to Snowflake Migration can be done in the following 2 steps:

• Step 1: Configure Klaviyo as the Source in your Pipeline by following the steps below:
  • Step 1.1: In the Asset Palette, select PIPELINES.
  • Step 1.2: In the Pipelines List View, click + CREATE.
  • Step 1.3: Select Klaviyo on the Select Source Type page.
  • Step 1.4: Set the following in the Configure your Klaviyo Source page:
    • Pipeline Name: Give your Pipeline a unique name.
    • Private API Key: Your Klaviyo account’s private API key.
    • Historical Sync Duration: The time it takes to ingest historical data.

• Step 1.5: TEST & CONTINUE is the button to click.
• Step 1.6: Set up the Destination and configure the data ingestion.
• Step 2: To set up Snowflake as a destination in Hevo, follow these steps:
  • Step 2.1: In the Asset Palette, select DESTINATIONS.
  • Step 2.2: In the Destinations List View, click + CREATE.
  • Step 2.3: Select Snowflake from the Add Destination page.
  • Step 2.4: Set the following parameters on the Configure your Snowflake Destination page:
    • Destination Name: A unique name for your Destination.
    • Snowflake Account URL: This is the account URL that you retrieved.
    • Database User: The Hevo user that you created in the database. In the Snowflake database, this user has a non-administrative role.
    • Database Password: The password of the user.
    • Database Name: The name of the Destination database where data will be loaded.
    • Database Schema: The name of the Destination database schema. Default value: public.
    • Warehouse: SQL queries and DML operations are performed in the Snowflake warehouse associated with your database.

• Step 2.5: Click Test Connection to test connectivity with the Snowflake warehouse.
• Step 2.6: Once the test is successful, click SAVE DESTINATION.

Method 2: Using Custom Code to Move Data from Klaviyo to Snowflake

This method of connecting Klaviyo to Snowflake would be time-consuming and tedious to implement. Users will have to write custom codes to enable Klaviyo to Snowflake migration. This method is suitable for users with a technical background.

You use an indirect method here to connect Klaviyo to Snowflake, first you migrate Klaviyo to Redshift and Redshift to Snowflake.

Klaviyo to Redshift

This is the first step to connecting Klaviyo to Snowflake. Follow the given steps to connect Klaviyo to Redshift:

Getting Data out of Klaviyo

• Developers can access data on metrics, profiles, lists, campaigns, and templates using Klaviyo’s REST APIs. You can refine the information returned by using two to seven optional parameters in each of these APIs. A simple call to the Klaviyo Metrics API to retrieve data, for example, would look like this:

GET https://a.klaviyo.com/api/v1/metrics

• As a response, the GET request returns a JSON object containing all of the fields from the specified dataset. For any given record, all fields might not be available. The JSON may appear as follows:

{
  "end": 1,
  "object": "$list",
  "page_size": 50,
  "start": 0,
  "total": 2,
  "data": [
    {
      "updated": "2017-11-03 17:28:09",
      "name": "Active on Site",
      "created": "2017-11-03 17:28:09",
      "object": "metric",
      "id": "3vtCwa",
      "integration": {
        "category": "API",
        "object": "integration",
        "id": "4qYGmQ",
        "name": "API"
      }
    },
    {
      "updated": "2017-11-03 20:54:40",
      "name": "Added integration",
      "created": "2017-11-03 20:54:40",
      "object": "metric",
      "id": "8qYK7L",
      "integration": {
        "category": "API",
        "object": "integration",
        "id": "4qYGmQ",
        "name": "API"
      }
    ]
  }

Loading Data into Redshift

You can use Redshift’s CREATE TABLE statement to define a table that will receive all of the data once you’ve identified the columns you want to insert.     

After you’ve created a table, you might want to migrate your data to Redshift by using INSERT statements to add data row by row. For inserting data one row at a time, Redshift isn’t designed for it. If you have a large amount of data to insert, save it to Amazon S3 and then load it into Redshift with the COPY command.

Keeping Klaviyo Data up to Date

• It’s not a good idea to duplicate all of your data every time your records are updated. This would be a painfully slow and resource-intensive process.
• Instead, identify key fields that your script can use to bookmark its progress through the data and return to as it searches for updated data. The best option for this is to use auto-incrementing fields like updated at or created at.
• You can set up your script as a CRON job or a continuous loop to get new data as it appears in Klaviyo once you’ve added this functionality.
• And, as with any code, you must maintain it once you’ve written it. You may need to change the script if Klaviyo changes its API, or if the API sends a field with a datatype your code doesn’t recognize. You will undoubtedly have to if your users require slightly different information.

Redshift to Snowflake

Database Objects Migration

The first step is, to begin with, database objects, which primarily include Schema, Table Structures, Views, etc. Instead of changing the object’s structure during migration, we should prefer to leave it alone because doing so would harm the entire migration process. Later, DB objects in Snowflake must be created with the same structure as those in Redshift.

Data Migration

• Any project involving migration must include this activity. The first step is to determine the historical data sets for each table and how they can be migrated because the data volume will be quite high and we need to take this into account before beginning data migration activity. It is strongly advised to create separate batches for each table (based on the filter options like transaction date or any other audit columns) and migrate data in these batches rather than in one batch. 
• After all historical data from all tables has been transferred to Snowflake, it will be relatively easy to move incremental data.
• Using Redshift’s “Unload Command” to unload data into S3 and Snowflake’s “Copy Command” to load the data from S3 into Snowflake tables could be one solution. This may also display some errors caused by compatibility problems, which you have already experienced on several occasions. 
• Another strategy involves using any data replication tool that supports Snowflake as a target, which can then be used to load raw data from the source system into Snowflake. ETL/ELT pipelines can fill facts, dimensions, and metrics tables on the Snowflake platform on top of this raw data.

Migrating Code to Snowflake 

• Compared to the previous two steps, this one has fewer difficulties and restrictions. In theory, Redshift and Snowflake both support ANSI-SQL, but they use different formats for various items, such as the lack of DISTKEY, SORTKEY, and ENCODE concepts in Snowflake. 
• There are many other examples of using functions, but one of the key distinctions between date functions is that Redshift’s GETDATE() and Snowflake’s CURRENT TIMESTAMP() are both date functions. While JSON, AVRO, and PARQUET are semi-structured data types that can be supported by Snowflake’s “VARIANT” datatype, they cannot be directly stored in Redshift.
• To match the JSON field names in Redshift, the target table must be created by examining the JSON source data. Without a set structure, we cannot import it directly. Only the first-level elements can be parsed into the target table by the COPY functions. 
• As a result, the multi-level elements are loaded into a single column and treated as strings. Redshift provides JSON SQL functions that must be used to further parse the intricate, multi-level data structures or arrays of JSON files. When migrating code, one must be extremely cautious and convert the code to supported SQL syntax.

Data Comparison between Redshift & Snowflake

• The final step in any migration project is to compare the data sets from the legacy and newly migrated platforms to make sure that everything was successfully migrated and that the output was accurate for the business. 
• Given that you are moving from Redshift to Snowflake in this scenario, you must contrast the outcomes from the two systems. Data comparison between Redshift and Snowflake manually is a time-consuming and difficult task.
• Therefore, you created a custom Python script solution to connect to each DB, run some crucial checks against both DBs (Redshift as the source and Snowflake as the target), and compare the results.
• Some crucial checks include Record Counts, Data Type Comparisons, Metrics Comparisons in Fact Tables, DB Object Counts, Duplicate Checks, etc. A daily CRON job can be scheduled or automated to execute this solution.

Conclusion

This article talks about the distinct ways for Setting up Klaviyo to Snowflake integration. It also gives an overview of Klaviyo and Snowflake.

Visit our Website to Explore Hevo

Hevo offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Marketing, Customer Management, etc.

This platform allows you to transfer data from 150+ sources (including 40+ Free Sources) such as Klaviyo and Cloud-based Data Warehouses like Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.

Want to take Hevo for a spin? 

Sign Up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.