How 3 SaaS Companies Built Real-Time Analytics
February 25th at 11am PST/ 2pm EST
We highlight the key considerations for three SaaS companies implementing real-time analytics at scale.
As SaaS companies build real-time analytics into their applications, development teams are challenged to deliver features with more demanding speed and scale requirements in a timely and cost-effective manner.
How have some SaaS companies navigated this? In this tech talk, we highlight the key considerations for SaaS companies implementing real-time analytics at scale:
- Low query and data latency: A CRM company built a real-time customer 360 that joins data from multiple product lines. The company needed a solution that could deliver sub-second queries not just for simple searches but also for complex joins.
- Ops costs of managing the solution at scale: A logistics company introduced a new product offering, an analytics suite, that allowed users to track shipments in real-time. The company had invested in serverless technologies and wanted an analytics database that was also easy to manage at scale.
- Flexibility of the system to changes in data or queries: A security company wanted to augment high level metrics with ad-hoc queries on real-time data so that users could more easily assess risk in their organization. Given that the queries were not known ahead of time, the team needed a flexible system that could support constantly changing queries on security data that was only a few seconds old.
About the Speakers
Justin Liu is a Product Manager at Rockset, where he works closely with SaaS customers to create a seamless developer experience for building applications on Rockset. Prior to Rockset, he worked in engineering at Google on various teams including Google Cloud IAM and Data Protection.
Julie Mills is a Product Marketing Manager at Rockset and has previously worked with teams to adopt SaaS products at Wrike and Meltwater.~