This 7 Reference Architectures Guide contains architectures for logistics tracking, real-time customer 360s, personalization and more. These data stacks are designed to address implementation challenges facing engineering teams including:

• Data Preparation: Constructing rigid data pipelines, defining schemas, denormalizing data while still keeping data latency low.
• Performance Engineering: Manual configuration and tuning to get sub-second query performance whenever new data or queries are introduced.
• Infras Ops: Managing complex distributed systems including configuring, scaling and capacity planning clusters.
• Compute Costs: Rising compute costs caused by misusing a batch-based system for real-time analytics. This includes using brute-force scans and micro-batching to achieve lower latency while incurring high compute costs.

As a result, these data stacks are more efficient and require less operational overhead making it possible for engineering teams to implement real-time analytics.

This whitepaper is sponsored by Intel and Rockset. Rockset achieves 84% faster performance with Intel Xeon Scalable processors for real-time analytics in the cloud.