7 Reference Architectures for Real-Time Analytics
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.
Real-time analytics encourages consumers to take desired actions from reading more content, to adding items to our cart to using takeout and delivery services for more of our meals. 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.