You are going about your day setting up and operating your organization’s data infrastructure and preparing it for further analysis. Suddenly, you get a request from one of your team members to replicate data from PagerDuty to BigQuery.
We are here to help you out with this requirement. You can transfer data from PagerDuty to BigQuery using custom ETL scripts. Or you can pick an automated tool to do the heavy lifting. This article provides a step-by-step guide to both of them.
Table of Contents
- How to Connect PagerDuty to BigQuery?
- What can you hope to achieve by replicating data from PagerDuty to BigQuery?
- Key Takeaways
How to Connect PagerDuty to BigQuery?
To replicate data from PagerDuty to BigQuery, you can either use CSV files or a no-code automated solution. We’ll cover replication via CSV files next.
Export PagerDuty to BigQuery using CSV Files
You can download CSV files from PagerDuty for the data in the following reports:
- System Report
- Team Report
- User Report
- Notifications Report or Incidents Report
The steps to save these reports as CSV files is mentioned below:
Save a System Report CSV file
You have to drill down into the data by selecting View Incidents, and then on the right, select Download CSV.
Save a Team Report CSV file
Click See All High-Urgency Incidents from [DATE RANGE SELECTED] to drill down to the data, and then click Download CSV.
Save a User Report CSV file
To download a CSV with data for a specific user, click data from the table and then click Download CSV to the right.
Save a Notifications Report or Incidents Report CSV file
Historical Table is a historical record for the time range selected. For example, if you select Week, you will see a table of the historical week-over-week range. By selecting View Online, you can drill down to a certain date range.
- Historical Table: In the Actions column, select Download CSV.
- Drill Down: To the right of the date range, click View Online and then click Download CSV.
To import the CSV files into BigQuery, follow the steps given below:
- Step 1: To load CSV data into a new BigQuery table, navigate to “Explorer” > “Dataset info.” Click on “Create table” on the BigQuery page.
- Step 2: In the Create Table panel, specify the following details:
- Select “Google Cloud Storage” from the “Create table” list in the Source section.
- In the Destination section, select the dataset you want to create and enter the table name. Set Table type to Native Table.
- In the Schema section, enter the schema definition.
- Step 3: Click on Create Table.
Using CSV files and SQL queries is a great way to replicate data from PagerDuty to BigQuery. It is ideal in the following situations:
- One-Time Data Replication: When your business teams require these PagerDuty files quarterly, annually, or for a single occasion, manual effort and time are justified.
- No Transformation of Data Required: This strategy offers limited data transformation options. Therefore, it is ideal if the data in your spreadsheets is accurate, standardized, and presented in a suitable format for analysis.
- Lesser Number of Files: Downloading and composing SQL queries to upload multiple CSV files is time-consuming. It can be particularly time-consuming if you need to generate a 360-degree view of the business and merge spreadsheets containing data from multiple departments across the organization.
You face a challenge when your business teams require fresh data from multiple reports every few hours. For them to make sense of this data in various formats, it must be cleaned and standardized. This eventually causes you to devote substantial engineering bandwidth to creating new data connectors. To ensure a replication with zero data loss, you must monitor any changes to these connectors and fix data pipelines on an ad hoc basis. These additional tasks consume forty to fifty percent of the time you could have spent on your primary engineering objectives.
How about you focus on more productive tasks than repeatedly writing custom ETL scripts, downloading, cleaning, and uploading CSV files? This sounds good, right?
In that case, you can…
Automate the Data Replication process using a No-Code Tool
Going all the way to use CSV files for every new data connector request is not the most efficient and economical solution. Frequent breakages, pipeline errors, and lack of data flow monitoring make scaling such a system a nightmare.
You can streamline the PagerDuty to BigQuery data integration process by opting for an automated tool. To name a few benefits, you can check out the following:
- It allows you to focus on core engineering objectives. At the same time, your business teams can jump on to reporting without any delays or data dependency on you.
- Your marketers can effortlessly enrich, filter, aggregate, and segment raw PagerDuty data with just a few clicks.
- The beginner-friendly UI saves the engineering team hours of productive time lost due to tedious data preparation tasks.
- Without coding knowledge, your analysts can seamlessly aggregate campaign data from multiple sources for faster analysis.
- Your business teams get to work with near real-time data with no compromise on the accuracy & consistency of the analysis.
As a hands-on example, you can check out how Hevo Data, a cloud-based No-code ETL/ELT Tool, makes the PagerDuty to BigQuery data replication effortless in just 2 simple steps:
Step 1: Configure PagerDuty as a Source
Configure PagerDuty as the source in Hevo Data.
Step 2: Configure BigQuery as a Destination
Configure Google BigQuery as your Destination.
That’s it, literally! You have connected PagerDuty to BigQuery in just 2 steps. These were just the inputs required from your end. Now, everything will be taken care of by Hevo Data. It will automatically replicate new and updated data from PagerDuty to Google BigQuery every 1 hour (by default). However, you can also increase the pipeline frequency as per your requirements.
Data Replication Frequency
|Default Pipeline Frequency||Minimum Pipeline Frequency||Maximum Pipeline Frequency||Custom Frequency Range (Hrs)|
|1 Hr||1 Hr||24 Hrs||1-24 Hrs|
In a matter of minutes, you can complete this No-Code & automated approach of connecting PagerDuty to BigQuery using Hevo Data and start analyzing your data.
Hevo Data’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 writing a single line of code.
Hevo Data’s reliable data pipeline platform enables you to set up zero-code and zero-maintenance data pipelines that just work. By employing Hevo Data to simplify your data integration needs, you can leverage its salient features:
- Fully Managed: You don’t need to dedicate any time to building your pipelines. With Hevo Data’s dashboard, you can monitor all the processes in your pipeline, thus giving you complete control over it.
- Data Transformation: Hevo Data 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 Data offers near real-time data replication, so you have access to real-time insight generation and faster decision-making.
- Schema Management: With Hevo Data’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 Data 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 put together on its website and all the features it supports. You can adjust your credit limits and spend notifications for 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.
What can you hope to achieve by replicating data from PagerDuty to BigQuery?
Here are a few benefits of replicating data from PagerDuty to BigQuery:
- You can centralize the data for your project. Using data from your company, you can create a single customer view to analyze your projects and team performance.
- Get more detailed customer insights. Combine all data from all channels to comprehend the customer journey and produce insights that may be used at various points in the sales funnel.
- You can also boost client satisfaction. Analyze customer interaction through email, chat, phone, and other channels. Identify drivers to improve customer pleasure by combining this data with consumer touchpoints from other channels.
These data requests from your marketing and product teams can be effectively fulfilled by replicating data from PagerDuty to BigQuery. If data replication must occur every few hours, you will have to switch to a custom data pipeline. This is crucial for marketers, as they require continuous updates on the ROI of their marketing campaigns and channels. Instead of spending months developing and maintaining such data integrations, you can enjoy a smooth ride with Hevo Data’s 150+ plug-and-play integrations (including 40+ free sources such as PagerDuty).
BigQuery’s “serverless” architecture prioritizes scalability and query speed and enables you to scale and conduct ad hoc analyses much more quickly than with cloud-based server structures. The cherry on top — Hevo Data will make it further simpler by making the data replication process very fast!Visit our Website to Explore Hevo Data
Saving countless hours of manual data cleaning & standardizing, Hevo Data’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 Data’s interface and get your data in the final analysis-ready form.
Want to take Hevo Data 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.