Your Data, Your Lake: How Observe Uses Iceberg and Streaming ETL for Observability
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. 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Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform. Go to dataengineeringpodcast.com/bruin today to get started. And for dbt Cloud customers, they'll give you $1,000 credit to migrate to Bruin Cloud. 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 LinkedIn 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. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links Observe Inc. Lakehouse Architecture Splunk Observability RSyslog GlusterFS Dremel Drill BigQuery Snowflake SIGMOD Paper Prometheus Datadog NewRelic AppDynamics DynaTrace Loki Cortex Mimir Tempo Cardinality FluentBit FluentD OpenTelemetry OTLP == OpenTelemetry Line Protocol Kafka VPC Flow Logs Read Amplification Lance Iceberg Hudi PromQL The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA