A million tags per second opens up a world of capabilities: fleet-level analytics, predictive maintenance at scale, real-time optimization across hundreds of assets. But getting there requires getting several things right.

We'll examine the architecture decisions that enable that scale, the compression strategies that make it economically viable, and where queries break under load. We'll work through real examples: what makes a good schema at scale, what happens when you get compression wrong, and how to recover query performance when it degrades. Getting these right determines whether your system becomes useful infrastructure or an expensive data sink. By the end, you'll know what it takes to make it to a million.

Learning Objectives:

• Identify

  the database architecture decisions required to reliably ingest and store up to one million tags per second.

• Evaluate schema design and compression strategies that balance high-throughput performance with storage cost.

• Recognize common causes of query degradation at scale and apply techniques to restore and accelerate query performance.

• Assess how architectural, compression, and query-design choices affect the long-term scalability and operational value of an asset-data platform

HOSTED BY

Doug Pagnutti

Developer Advocate @ Tiger Data