Overview > Use Cases

Use Cases

This document describes some sample use cases where users have employed Rockset.

Data Science for Market Intelligence

Data Sets

  • App Annie (3rd party data set on app downloads, usage, and engagement in JSON format)
  • Crunchbase (3rd party data set on companies and their products in CSV format)
  • Email headers and Calendar headers (data set from real-time JSON APIs)

Application

Each data set was ingested into Rockset, which automatically inferred schema and fully indexed the data, making it available as SQL tables. Data scientists used Jupyter notebooks to interactively explore the various data sets, filter them to relevant portions, JOIN them, and subsequently construct SQL queries to show the desired insight. They later embedded these SQL queries into custom internal JavaScript apps and SQL dashboarding tools for easy repetitive access.

Result

A single data scientist built this end-to-end in a couple of days, resulting in a solution that is live, extensible, and easy to integrate with new data sets in the future. Projects of similar scope previously took a team of 2-3 about a month to build without Rockset and gave rise to a one-time static report, which was not repeatable or easily maintainable.

Personalization of E-Commerce Website Based on Real-Time User Clickstreams

Data Sets

  • Adobe Omniture logs (real-time data set in TSV format)
  • User data and Product data (both live synced from an online operational database)

Application

Real-time Adobe Omniture data is continuously ingested into Rockset, with custom data retention of 30 days. Rockset automatically purges older data. User and Product dimension tables are continuously live synced from operational databases, and only the latest copy is kept. Various personalization features (e.g. previously visited products, product characteristics of interest to individual users, etc.) are extracted via live millisecond SQL JOINs between clickstream and User/Product tables. These real-time personalization features are fed to the app and app’s relevant ML models to personalize the user experience.

Result

Building new personalization features is simple and fast, powered by a single integrated data management system. Personalization systems working on User and Product data are fully isolated from the e-commerce site’s operational databases, thereby allowing for more rapid experimentation and A/B testing.

Live Customer Support Portal for a Connected Devices Company

Data Sets

  • Real-time sensor data (streaming continuously in JSON format)
  • User data (from Postgres)
  • Customer support tickets (managed in a separate SaaS ticket management system).

Application

Rockset’s live write API is used to stream real-time connected device JSON data, with a custom retention of 90 days. A data forwarder was built to sync JSON API data from the ticketing system with Rockset. User data was continuously live synced from Postgres. A simple Python Flask internal application was built (few hundred lines of Python). The app takes in a customer ID and executes fast live search, aggregation, and JOIN queries across all the data sets to display a single-pane view to improve customer support.

Result

With a fast unified customer support portal, time to diagnosis and resolution for support tickets improved significantly. The organization previously had many of these data sets in a traditional warehouse, which could not handle real-time data and was too slow to power interactive search and aggregations. Due to these challenges, customer support involved long wait times for page loads and many unresolved support issues.