When it comes to managing digital operations, teams require a proactive and preventative approach. PagerDuty helps achieve just that-with its real time-visibility into critical applications and services all in one place. Snowflake has been a large partner for PagerDuty and has been using PagerDuty’s services, primarily for engineering paging which includes getting alerts from systems and notifying when there are critical events.
Senior Director for Business Intelligence and Data Engineering at PagerDuty, Manu Raj, and his small, but mighty team called ‘DataDuty’ shared about their challenges that led them to implement Snowflake during the Snowflake Customer Office Hour Webinar series.
PagerDuty previously had a mix of multiple data warehouse solutions, including those in the cloud, Postgres, and MySQL along with complex transformations in Spark. The major issue Raj and his team were dealing with was the conflict between multiple workloads and workload management tools. It was difficult to meet their SLAs on BI reports and they were increasingly reliant on IT. PagerDuty also has seen a proliferation of use cases from AI and data applications which add layers of complexity to their data warehouses and administration. When they attempted to do self-serving analytics and share data with internal business units, scaling was very difficult with their previous solutions.
While the company was rapidly expanding, total data warehouse costs were increasing by 30% and it was difficult to manage the growing costs. PagerDuty wanted a more consolidated data platform that was SQL-based, with high performance, and included key security features. The company ran multiple POCs with several market-leading data platform solutions and decided on Snowflake on AWS.
Snowflake serves as a single source of truth for PagerDuty. PagerDuty’s data ingestion happens through a variety of solutions such as MuleSoft, Segment, Fivetran, Kafka, and Spark jobs that all go to Snowflake. Data quality tools such as data transformation (Python, SQL) and data science are pushed down into Snowflake.
PagerDuty currently serves an estimated 400 users for their 1,000+ member company. These users were steered in the direction of using more data insights after the company implemented Snowflake. The focus of initiatives shifted from primarily administration tasks within the organization to data-driven insights, analytics, and data science.
Snowflake allowed Pagerduty to leverage their data to drive business value, provide new capabilities to the business, and enabled them to free resources from administrative tasks to actually working on data insights.
The ability to run multiple workloads simultaneously and meet SLAs with finance, ELT teams, and other stakeholders has been pivotal. As a high-growth company, Snowflake is enabling Raj’s team to serve PagerDuty’s global needs at scale with high confidence in the reliability of their data. After implementing Snowflake, PagerDuty saw a 50% reduction in costs and up to 5x performance improvements.
“Snowflake is much more than a data platform! The scalability, features, and innovations enabled us to serve Pagerduty business users and our customer’s analytics needs 5x faster with more efficiency.”Manu Raj, Senior Director, BI and Data Engineering
PagerDuty’s partnership with Snowflake is providing the company with new goals and use cases for the future as well. Currently, the company is heavily interested in looking at Snowflake’s secure data sharing. Raj sees the native ability to publish data sets and make them discoverable for customers (with descriptions and example queries) as a key future benefit, and a way to open much deeper levels of collaboration than ever before while maintaining the data governance, monitoring, and control between who has data provider and access privileges.
According to Raj, “Now that we’ve finished the migration, we get to reap the benefits. We see a long roadmap ahead and a bright future with Snowflake, with many use cases that we’re excited for.” PagerDuty will continue to leverage Snowflake for data science and data applications use cases that will enhance their data’s value to the rest of the business, such as real-time analytics and embedded AI tools.
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