7 Reference Architectures for Real-Time Analytics

Seven data stacks designed for high-volume data streams, high-concurrency applications and compute-efficient scaling.

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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.

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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.

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