The CTO Podcast with Fexingo · 2026-07-24 · 12 min
In this episode, Lucas and Luna dig into how Splunk - the data platform that ingests massive machine data - rebuilt its observability engine to handle 10 petabytes of new data every single day. They walk through the architectural pivot from a monolithic indexing layer to a microservices-based streaming ingest pipeline, the tough trade-offs between search speed and storage cost, and how the engineering team managed backward compatibility for thousands of enterprise customers who rely on Splunk for security and operations. Along the way, they discuss the role of tiered storage, the decision to adopt Apache Kafka as the central nervous system, and why Splunk's CTO bet on a new query language rather than patching the old one. If you've ever wondered how real-time log analysis works at hyperscale, or how a company migrates a legacy system without breaking the world, this episode is for you.
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