Data practitioners often need to integrate data between different platforms to harness the full potential of their data. And more often than not, data warehouses are chosen as the destination to run faster queries, gain a holistic view of the data collected for analysis and reporting, among many others. Most organizations these days connect Opsgenie to Snowflake for seamless integration of incident data. And if you are looking for ways to easily pull data from Opsgenie and load the same to Snowflake, you’ve come to the right place.

In this article, we will walk you through three simple methods to migrate data from Opsgenie to Snowflake, enabling you to unlock new possibilities for your data-driven initiatives.

Method 1: Using Opsgenie API to Create Custom Integrations

You can create your own custom integrations using the Opsgenie API to set up your Opsgenie Snowflake migration. Here are the steps that can help you do so:

Step 1: Create an Opsgenie API key

To create an API key in Opsgenie, you’ll have to perform the following steps– 

  • Log in to your Opsgenie account
  • Navigate to Settings > API Keys
  • Click on the Create API Key button
  • Next, you’ll have to enter a name for the API key
  • Choose the required permissions that you want to grant to the API key
  • Finally, click on the Create button

Step 2: Create a database and table in your Snowflake account to store the data from Opsgenie

Perform the following steps to create a Snowflake database and table–

  • Log in to your Snowflake account and click on the Database tab
  • Hit the Create Database button
  • Enter a suitable name for your database
  • Finally, click on the Create button

Step 3: Write a custom script that uses the Opsgenie API to migrate data from Opsgenie to Snowflake

The custom script you write must use the API key you created to authenticate with the Opsgenie API. It should then successfully query the data that you want to migrate to Snowflake. Finally, it should be able to replicate the data seamlessly into the warehouse.

This is a sample script that you can use to query incidents data with CRITICAL severity and then load it to Snowflake–

import requests
import json

# API key
API_KEY = "your_api_key"

# Snowflake connection information
ACCOUNT = "your_account"
WAREHOUSE = "your_warehouse"
DATABASE = "your_database"
TABLE = "your_table"

# Query for alerts
url = ""
params = {
    "query": "severity:CRITICAL"

response = requests.get(url, params=params, headers={"Authorization": "GenieKey " + API_KEY})

# Load alerts into Snowflake
data = json.loads(response.content)

for alert in data:
    row = {
        "alert_id": alert["id"],
        "title": alert["title"],
        "description": alert["description"],
        "severity": alert["severity"],
        "created_at": alert["createdAt"],
        "updated_at": alert["updatedAt"],
    }"", json=row, headers={"Account": ACCOUNT, "Warehouse": WAREHOUSE, "Database": DATABASE, "Table": TABLE})

Step 4: Configure this script to run at fixed intervals

The final step in this manual process is to configure your custom script to run on a regular basis using a scheduler like Cron or Airflow.

Method 2: Using Kafka to Build In-House Data Pipelines

Follow these steps to build your in-house data pipeline using Kafka:

Step 1: Create a Kafka topic to store your Opsgenie data

In order to create a Kafka topic, you’ll need to first start the Kafka broker, create a topic and then finally, configure the source.

Step 2: Configure Opsgenie to push the data to the Kafka topic

To configure Opsgenie, these are the steps you can follow–

  • Navigate to Settings > Integrations > Kafka
  • Put in the connection details for the Kafka broker
  • Select the topic to push data to
  • Finally, click on the Save button

Step 3: Create a Kafka consumer to read data from the topic and migrate the same to Snowflake

There are essentially two steps that you need to execute to to successfully load data to Snowflake–

  • The first is to write a script to create a Kafka consumer that reads data from the Kafka topic.
  • The second is to write a script that loads this data to Snowflake.

Here’s a sample script that you can use to load data to Snowflake–

import json
import requests

# Kafka connection information
KAFKA_BROKER = "your_kafka_broker"
KAFKA_TOPIC = "your_kafka_topic"

# Snowflake connection information
ACCOUNT = "your_account"
WAREHOUSE = "your_warehouse"
DATABASE = "your_database"
TABLE = "your_table"

# Create a Kafka consumer
consumer = KafkaConsumer(

# Load data into Snowflake
for message in consumer:
    data = json.loads(message.value.decode())

    row = {
        "alert_id": data["id"],
        "title": data["title"],
        "description": data["description"],
        "severity": data["severity"],
        "created_at": data["createdAt"],
        "updated_at": data["updatedAt"],
    }"", json=row, headers={"Account": ACCOUNT, "Warehouse": WAREHOUSE, "Database": DATABASE, "Table": TABLE})

You will also need to install the Kafka and the requests library using the following commands to run the script–

pip install kafka

pip install requests

Once you have followed all these steps through, you’ll have a data pipeline that loads data from Opsgenie to Snowflake.

But doesn’t the entire process seem a tad too cumbersome? In order to deploy these steps successfully, you’ll need to have a fair share of knowledge about the Opsgenie API and Kafka. Furthermore, maintaining the Kafka cluster and these pipelines is quite tedious. 

That is why, a fully-managed and automated no-code data pipeline sounds like a much better option. In the next section, we’ll talk about how you can automate the data ingestion and migration process in just a few minutes and save your engineering bandwidth for high-priority tasks.

Method 3: Using a No-Code Automated Data Pipeline

Using a third-party tool, like Hevo Data, can automate the process of Opsgenie to Snowflake data migration and alleviate your stress of writing codes or managing the pipeline on your own.

The benefits of using this method are many–

  • Simplified Data Integration: You can easily set up a no-code data pipeline using its visual and intuitive interface to extract data from Opsgenie, transform it if necessary, and load it into Snowflake without writing a single line of code. 
  • Data Transformation and Enrichment: No-code data pipelines like Hevo offer a drag-and-drop data transformation console, along with a Python console for those who want to conduct complex transformations.
  • Automated Scheduling and Monitoring: Automated data pipelines allow you to define the frequency of data extraction, transformation, and loading tasks based on your requirements, along with monitoring dashboards and alerts to track the pipeline’s health, performance, and data flow.
  • Time and Cost Efficiency: Automated data pipelines eliminate the need for manual coding and development efforts, resulting in significant time and cost savings. 
  • Scalability and Reliability: No-code data pipelines leverage their cloud-based infrastructure and distributed processing capabilities to handle growing volumes of data efficiently. 
  • Schema management: All of your mappings will be automatically discovered and handled to the destination schema using the auto schema mapping feature of automated data pipelines.

Hevo Data’s no-code data pipeline helps you tap into all these benefits for connecting Opsgenie to Snowflake in just two easy steps.

How Does Migrating Data from Opsgenie to Snowflake Help?

  • Migrating data from Opsgenie to Snowflake allows organizations to combine Opsgenie data with other relevant datasets in Snowflake to gain a comprehensive view of incidents.
  • Integrating Opsgenie data with Snowflake enables organizations to track service-level agreements’ (SLA) performance effectively, and make data-driven decisions to make the incident management process more seamless.
  • Migrating Opsgenie data to Snowflake allows organizations to analyze the impact of incidents on customers, and subsequently improve the overall customer experience.

Step 1: Configure Opsgenie as source

Opsgenie to Snowflake: Configure Source
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Step 2: Configure Snowflake as destination

Opsgenie to Snowflake: Configure Destination
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And that’s about it. 


As you can see, connecting Opsgenie to Snowflake to migrate data has multiple benefits. It helps organizations respond to incidents more promptly, understand how they could improve their SLAs, optimize resource allocation and ultimately, enhance the customer experience. That is why it is essential to automate the data migration process using data pipelines like Hevo. 

You can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 50+ free sources) like Opsgenie to Snowflake. Hevo Data is helping many customers take data-driven decisions through its no-code data pipeline solution for Opsgenie Snowflake integration. 

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

Former Content Marketing Specialist, Hevo Data

Anwesha is experienced in curating content and executing content marketing strategies through a data-driven approach. She has more than 5 years of experience in writing about ML, AL, and data science.

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