Building an all-new data connector is challenging, especially when you are already overloaded with managing & maintaining your existing custom data pipelines. To fulfill an ad-hoc PagerDuty to Databricks connection request from your customer experience & analytics team, you’ll have to invest a significant portion of your engineering bandwidth.

We know you are short on time & need a quick way out of it. This can be a piece of cake for you if you just need to download and upload a couple of CSV files. Or you could directly opt for an automated tool that fully handles complex transformations and frequent data integrations for you.

Either way, with this article’s stepwise guide to connect PagerDuty to Databricks effectively, you can set all your worries aside and quickly fuel your data-hungry business engines in 8 nifty minutes.

How to connect PagerDuty to Databricks?

PagerDuty stores data in multiple tables such as Business Services, Change Events,

Escalation policies, Extension Schemas, Incidents, On-calls, Schedules, etc. To quickly get this data from PagerDuty to Databricks, you can go along with the following 2 paths:

Exporting & Importing PagerDuty Reports as CSV Files

PagerDuty offers various reports that provide account-wide views of historical data, allowing you to monitor your business’s operational efficiency. You can go to Analytics > Reports to find different reports based on the pricing plan. 

Step 1: Download CSV files

Let’s see how you can easily download these reports as CSV files for the PagerDuty to Databricks integration:

  • System Report: It provides account information by escalation policies and services. To download a CSV, go to View Incidents > Download CSV.
  • Team Report: This shows the team’s performance and operational load over time. You can select the data range to view All High-Urgency Incidents and click on the Download CSV option. 
  • User Report: Provides detailed information about individual responders in PagerDuty. Select the data from the table for the specific user to download your CSV file and then click Download CSV to the right.
  • Notifications Report: This shows details about the notifications that are sent to your users. For historical data, you can click Download CSV under the Actions column. Else, you can click on the View Online option to the right of the date range and then click on the Download CSV option.
  • Incidents Report: Offers a detailed view of an incident’s service, duration, who resolved it, and escalations. Similar to the Notifications Report, you can click on the Download CSV under the Actions column for the historical table. Or, click on View Online to the right of the date range and then click Download CSV.

Step 2: Import CSV Files

  • Log in to your Databricks account. On your Databricks homepage, click on the “click to browse” option. A new dialog box will appear on your screen. Navigate to the location on your system where you have saved the CSV file and select it.
PagerDuty to Databricks - click to browse option
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  • In the Create New Table window in Databricks, click on the Create New Table with UI button. Interestingly, while uploading your CSV files from your system, Databricks first stores them in the DBFS(Databricks File Store). You can observe this in the file path of your CSV file, i.e., in the format “/FileStore/tables/<fileName>.<fileType>”.      
PagerDuty to Databricks - Create Table with UI button
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  • Select the cluster where you want to create your table and save the data. Click on the Preview Table button once you are done.
PagerDuty to Databricks - Preview Table button
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  • Finally, you can name the table and select the database where you want to create the table. Click on the Infer Schema check box to let Databricks set the data types based on the data values. Click the Create Table button to complete your data replication from PagerDuty to Databricks. 
PagerDuty to Databricks - Table Attributes
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In a nutshell, this manual approach allows you to download the data you desire from PagerDuty and then upload it to Databricks. This integration method for replicating data from PagerDuty to Databricks is extremely fruitful for the following scenarios:

  • Single-Time Data Replication: This method is a good choice for those once-in-a-blue-moon data transfer requests. The manual effort required is completely justified as you can easily download all the data as CSV files and upload it. 
  • Analysis-Ready Data: If your PagerDuty data is already in an analysis-ready form or requires little transformation, then this approach will suffice. 
  • Few Reports: When you only need to download a selected few reports for your analysis, as that can be done in a matter of minutes.

However, you might also land yourself in a tricky situation when your business teams request ad-hoc multiple data connections as your business starts to grow. Here, instead of manually downloading data, you would need to ask your engineering team to build and maintain custom data connectors.

However, they already have a lot on their plate. This includes providing transformed & clean data to Data Scientists, tuning databases for creating table schemas, and conforming data to a specified data model. This will eventually eat up 40-60% of their bandwidth earlier reserved for the primary engineering goals. Also, they have to be on a constant lookout for any data leakages and fix them on priority.

Is there a more fluid and effortless alternative to this resource-intensive & time-consuming solution? Yes, Of course. You can always…     

Automate the Data Replication process using a No-Code Tool

Going all the way to write custom scripts 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 Databricks 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 while your business teams can jump on to reporting without any delays or data dependency on you.
  • Your product, customer experience, and analytics teams 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 create thorough reports for various business verticals to drive better decisions. 
  • Your business teams get to work with near-real-time data with no compromise on the accuracy & consistency of the analysis. 
  • You can easily analyze your projects and team performance by creating a single customer view from your organization’s data.

As a hands-on example, you can check out how Hevo, a cloud-based No-code ETL/ELT Tool, makes the PagerDurty to Databricks data replication effortless in just 2 simple steps:

  • Step 1: To get started with replicating data from PagerDuty to Databricks, configure PagerDuty as a source by providing your PagerDuty credentials.
PagerDuty to Databricks - Configure PagerDuty as a Source
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  • Step 2: Configure Databricks as your destination and provide your Databricks credentials to complete the PagerDuty to Databricks integration.
PagerDuty to Databricks - Configure Databricks as a Destination
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This automated approach gets you started with the PagerDuty to Databricks with 2 simple steps in just a few clicks. The pipeline will automatically replicate new and updated data from PagerDuty to Databricks every hour (by default). However, you can also adjust the PagerDuty to Databricks data replication frequency per your requirements.

Data Replication

Default Pipeline FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
1 Hr1 Hr24 Hrs1-24

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 PagerDuty to Databricks data integration needs, you can leverage its salient features:

  • Reliability at Scale: With Hevo Data, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 
  • Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every state of the pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs. 
  • Stay in Total Control: When automation isn’t enough, Hevo Data offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    
  • Auto-Schema Management: Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo Data automatically maps the source schema with the destination warehouse so that you don’t face the pain of schema errors.
  • 24×7 Customer Support: With Hevo Data, you get more than just a platform, you get a partner for your pipelines. Discover peace with round-the-clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-feature free trial.
  • Transparent Pricing: Say goodbye to complex and hidden pricing models. Hevo Data’s Transparent Pricing brings complete visibility to your ELT spending. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in the data flow.
GET STARTED WITH HEVO FOR FREE

What can you achieve by replicating data from PagerDuty to Databricks?

Replicating data from PagerDuty to Databricks can help your data analysts get critical business insights. Here’s a short list of questions that this data integration helps answer:

  • What percentage of customers’ queries from a region is through email?
  • The customers acquired from which channel have the maximum number of tickets raised?
  • What percentage of agents respond to customers’ tickets acquired through the organic channel?
  • Customers acquired from which channel have the maximum satisfaction ratings?
  • How does customer SCR (Sales Close Ratio) vary by Marketing campaign?
  • How does the number of calls to the user affect the activity duration with a Product?
  • How does Agent performance vary by Product Issue Severity?

Bringing It All Together

That’s it! Now you know all the ways to replicate data from PagerDuty to Databricks. If the PagerDuty data replication is a rarity, you can always go with the trusty download and upload CSV file approach. However, if your business teams need data from multiple sources every few hours in an analysis-ready form, you might need to burden your engineering team with custom data connections.

This is a time-consuming task that requires continuous monitoring of the data pipelines to ensure no data loss. Sounds challenging, right? Well, no worries. You can also automate your data integration with a No-code ELT tool like Hevo data, which offers 150+ plug-and-play integrations.

Visit our Website to Explore Hevo

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’s interface and get your data in the final analysis-ready form. 

Want to take Hevo for a spin? 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 connecting PagerDuty to Databricks! Let us know in the comments section below!

mm
Former Research Analyst, Hevo Data

Sanchit Agarwal is a data analyst at heart with a passion for data, software architecture, and writing technical content. He has experience writing more than 200 articles on data integration and infrastructure.

No-code Data Pipeline for Databricks