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 of the world?
What are you building at Observe that circumvents those pain points?
What are the major ecosystem evolutions that make this a feasible architecture? (e.g. columnar storage, distributed compute, protocol consolidation)
Can you describe the architecture of the Observe platform?
How have the design of the platform evolved/changed direction since you first started working on it?
What was your process for determining which core technologies to build on top of?
What were the missing pieces that you had to engineer around to get a cohesive and performant platform?
The perennial problem with observability systems and data lakes is their tendency to succumb to entropy. What are the guardrails that you are relying on to help customers maintain a well-structured and usable repository of information?
Data lakehouses are excellent for flexibility and scaling to massive data volumes, but they're not known for being fast. What are the areas of investment in the ecosystem that is changing that narrative?
As organizations overcome the constraints of limited retention periods and anxiety over cost, what new use cases does that unlock for their observability data?
How do AI applications/agents change the requirements around observability data? (collection, scale, complexity, applications, etc.)
What are the most interesting, innovative, or unexpected ways that you have seen Observe/lakehouse technologies used for observability?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Observe?
When is Observe/lakehouse technologies the wrong choice?
What do you have planned for the future of Observe?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Your Data, Your Lake: How Observe Uses Iceberg and Streaming ETL for Observability

Data Engineering Podcast

Observability pain points and data modes

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