7 Reference Architectures for Real-Time Analytics
We’re introducing 7 new data stacks for real-time analytics that are built for speed, scale and and simplicity.
We experience real-time analytics everyday. The content displayed in the Instagram newsfeed, the personalized recommendations on Amazon, the promotional offers from Uber Eats are all examples of real-time analytics.
For many big tech companies, the investment in real-time analytics has had huge financial gains.
Yet, for many companies, real-time analytics remains out of reach.
In this 7 Reference Architectures Guide, Rockset is introducing new data stacks that reduce the barriers preventing many companies from implementing real-time analytics including:
- Data Preparation: Constructing rigid data pipelines, defining schemas and denormalizing the data
- Performance Engineering: Manual configuration and tuning to get sub-second query performance whenever new data or queries are introduced
- Operations: Managing complex distributed systems including configuring, scaling and capacity planning clusters
We introduce new architectures for real-time analytics that are built for speed, simplicity and scale.
These modern data stacks for logistics tracking, real-time customer 360s, personalization and more put real-time analytics within reach of all companies from lean startups to large enterprises.