Episode Details

Summary 
In this episode Jacob Leverich, cofounder and CTO of Observe, talks about applying lakehouse architectures to observability workloads. Jacob discusses Observe’s decision to leverage cloud-native warehousing and open table formats for scale and cost efficiency. He digs into the core pain points teams face with fragmented tools, soaring costs, and data silos, and how a lakehouse approach - paired with streaming ingest via OpenTelemetry, Kafka-backed durability, curated/columnarized tables, and query orchestration - can deliver low-latency, interactive troubleshooting across logs, metrics, and traces at petabyte scale. He also explore the practicalities of loading and organizing telemetry by use case to reduce read amplification, the role of Iceberg (including v3’s JSON shredding) and Snowflake’s implementation, and why open table formats enable “your data in your lake” strategies. 
Announcements 

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I'm interviewing Jacob Leverich about how data lakehouse technologies can be applied to observability for unlimited scale and orders of magnitude improvement on economics

Interview
 

• Introduction
• How did you get involved in the area of data management?
• Can you start by giving an overview of what the major pain points have been in the observability space? (e.g. limited scale/retention, costs, integration fragmentation)
• What are the elements of the ecosystem and tech stacks that led to that state o