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Zhang"}}],"image":{"id":"image-831a823af7","height":"720","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--211a1072-65a1-4523-81b7-c98455b9518e/iceberg-summit-preview-blogheader-1680x720.jpg?quality=85&preferwebp=true","alt":"Digital illustration of an iceberg in a ring with a blue background with white dots","width":"1680",":type":"snowflake-site/components/image"},"timeToRead":"6","publicationDate":"JUL 30, 2026","tag":{"tagText":"Product and Technology","tagColor":"#3F808A"},"title":{"lines":["Unlocking Open Observability with Observe on Apache Iceberg"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"],":type":"snowflake-site/components/container"},"responsivegrid_content":{"columnCount":12,"columnClassNames":{"image":"aem-GridColumn aem-GridColumn--default--12","wistia_video":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn 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This is part of Observe by Snowflake’s broader commitment to open standards in observability. In addition to using \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://opentelemetry.io/\"\u003EOpenTelemetry\u003C/a\u003E-native ingestion, your data now lives in a format any Iceberg-compatible engine can read, in storage that you own and control.\u003C/p\u003E\r\n\u003Cp\u003EThe volume of logs, metrics and traces generated by modern systems has grown dramatically. Leading enterprises now ingest massive amounts of data daily. Yet traditionally that data has almost always lived in a silo — encoded in vendor-specific formats, queryable only through the vendor's interface and inaccessible to the rest of the organization. Engineering teams troubleshoot in one tool while business and data teams work in another, and joining telemetry signals with business data requires ETL, manual exports or a custom pipeline. When your needs outgrow the vendor’s platform, or when you want to run analytics on top of your telemetry, getting that data out is often harder than it should be.\u003C/p\u003E\r\n\u003Cp\u003EThese issues are a consequence of how traditional observability platforms were architected: built to serve a single vendor's interface, with no expectation that the data would ever need to live anywhere else. Open table formats such as Apache Iceberg change that. When your telemetry is stored in Iceberg, it lives alongside the rest of your data, under your control and accessible through the tools your teams already use. This minimizes the need to extract or replicate data into a separate system — and the related costs of that process.\u003C/p\u003E\r\n\u003Ch2\u003EYour data, in your object storage, in an open format\u003C/h2\u003E\r\n\u003Cp\u003EWith Observe on Iceberg, your telemetry is written as Iceberg tables directly to an Amazon S3 bucket in your AWS account. Observe accesses that bucket through an IAM role you create and control. If you revoke that role or remove the data lake configuration, Observe loses access. The data stays in your bucket in an open format, yours to keep. You can configure multiple data lakes, and data sets across them are designed to remain isolated.\u003C/p\u003E\r\n\u003Cp\u003EObserve on Iceberg is built on Snowflake-managed Iceberg tables using the v3 table specification. Because these tables are managed, compaction, file sizing, and snapshot and metadata upkeep all happen automatically — as a result, you get high-performance queries without having to maintain the tables yourself. The v3 spec also brings new table capabilities, including richer data types and performance improvements.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-292cee4760","height":"1184","lazyEnabled":true,"src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--f5861de8-cb26-4747-805a-906a30ab9651/observe-iceberg-blog-reference-architecture.png?quality=85&preferwebp=true","alt":"Architecture diagram for Observe on Iceberg.","width":"2214","title":"Figure 1: Architecture diagram for Observe on Iceberg.",":type":"snowflake-site/components/image"},"blog_text_1081390971":{"id":"blog-text-a60723c647","text":"\u003Cp\u003E \u003C/p\u003E\r\n\u003Cp\u003EIn the private preview, you can:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIngest data into Iceberg via the Observe pipeline without a separate export or ETL step: \u003C/b\u003EYour existing ingestion setup is unchanged. Data flows through the Observe ingestion pipeline and is written into standard Iceberg tables in your own S3 bucket.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EStore observability data in an open format on your own object storage:\u003C/b\u003E Data sets are written as standard Iceberg tables in your S3 bucket, under your governance, retention policies and access controls. If you remove Observe's access, the data remains yours.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EBuild monitors, dashboards and correlations on top of your data within Observe: \u003C/b\u003EThe full Observe experience — monitoring, dashboards, OPAL transformations, AI SRE and correlations — is designed to work identically on Iceberg-backed data sets. Nothing changes about how you use Observe day to day.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"wistia_video":{"id":"wistia-video-5014225a95","title":{"id":"title","type":"heading1","lines":["Unlocking Open Observability with Observe on Apache Iceberg"],":type":"snowflake-site/components/title"},"wistiaVideoId":"ts1aw6nugj","showVideoLength":true,"showRewatchButton":true,":type":"snowflake-site/components/wistia-video"},"blog_text_1003967330":{"id":"blog-text-2ac5b45254","text":"\u003Ch2\u003EWhy Iceberg is the right format for observability data\u003C/h2\u003E\r\n\u003Cp\u003EObservability data is well-suited to columnar storage, and Iceberg stores data in Parquet by default. Time stamps, service names, severity levels and structured attributes repeat across millions of events in patterns that compress efficiently and scan well with partition pruning.\u003C/p\u003E\r\n\u003Cp\u003EBut Iceberg is more than just Parquet. Parquet provides columnar files that are easy to query if you already know where they are. Iceberg adds the table layer on top, providing schema evolution, partitioning, snapshots and a catalog that any compatible engine can discover and read without custom integration. That's what makes your telemetry open and accessible instead of just stored in an efficient format.\u003C/p\u003E\r\n\u003Cp\u003EIn our testing, observability workflows also run with similar performance on Iceberg tables as on Observe-native data, so you don’t need to choose between openness and performance. Engineering teams can continue using Observe for incident investigation, dashboards, monitors and AI-assisted root cause analysis, while the rest of your organization gets direct access to the same data, in the tools they already use.\u003C/p\u003E\r\n\u003Ch2\u003EQuery with any engine\u003C/h2\u003E\r\n\u003Cp\u003EOnce your data is in Iceberg tables, it isn't readable only through Observe. Observe exposes a read-only Iceberg REST Catalog on the Observe API so that any Iceberg-compatible query engine can discover and read your tables directly. This interoperability is backed by Snowflake Horizon Catalog. Apache Spark™, DuckDB, Trino and PyIceberg all work today. External engines read data files straight from your S3 bucket, and the data path does not go through Observe at all.\u003C/p\u003E\r\n\u003Cp\u003EThis opens up workflows that proprietary observability storage has historically made difficult. In early conversations with customers, we're already seeing use cases that allow customers to:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EJoin telemetry with internal business data: \u003C/b\u003EQuery observability data alongside revenue, customer or product data in a single analysis — without building a pipeline to sync the two.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ERun periodic batch data engineering jobs: \u003C/b\u003ESchedule transformations or aggregations on historical telemetry using the tools your data team already operates.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAudit observability data for compliance:\u003C/b\u003E Query raw telemetry directly from your own storage using your own credentials, without routing through a third-party system.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETrain ML models on operational data: \u003C/b\u003EPoint a Spark or PyIceberg job at your telemetry history without the need to export, copy or execute a separate data preparation step.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EWhen telemetry lives in the same lake as your business data, the boundary between your operational data and your analytical data starts to dissolve. Correlating an incident with customer impact, revenue metrics or a recent deployment becomes a simple query. Teams across engineering, data and product can work from the same source of truth, without anyone needing to own a pipeline that copies data between systems.\u003C/p\u003E\r\n\u003Ch2\u003ECut and control costs at scale\u003C/h2\u003E\r\n\u003Cp\u003EStoring large volumes of telemetry in a proprietary observability system is typically expensive, and the costs compound as data volumes grow. With Iceberg, your data sits on your own object storage rather than in a vendor-managed system with its own pricing model.\u003C/p\u003E\r\n\u003Cp\u003EBecause Iceberg decouples storage from compute, you pay only for the queries you run. That means you can retain months of telemetry at a fraction of the cost of traditional proprietary observability platforms, without tiering, rehydrating or making trade-offs about what to keep.¹\u003C/p\u003E\r\n\u003Cp\u003EAnd because the same data serves multiple teams — engineering teams for incident investigation, data teams for analytics, ML engineers for model training — you can eliminate the cost of replicating or moving data between systems. Store it once, and read from the same source across teams.\u003C/p\u003E\r\n\u003Ch2\u003EGet started today\u003C/h2\u003E\r\n\u003Cp\u003EObserve on Iceberg is now available in private preview, with broader availability coming soon. If you need open, portable storage for your observability data, we want to hear from you. We invite you to:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003ERead the \u003Ca href=\"https://docs.observeinc.com/docs/observe-on-iceberg\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EObserve on Iceberg documentation\u003C/a\u003E\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003EReach out to your Observe account team to get access\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003ELearn more about \u003Ca href=\"https://www.snowflake.com/en/product/observe/\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003EObserve\u003C/a\u003E\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EWhether you're looking for long-term retention, interoperability with an existing data lake or the ability to run your own analytics on telemetry, we look forward to hearing how Observe on Iceberg works for your team.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cbr\u003E\r\n¹Actual storage cost savings may vary by data volume, cloud provider, region and existing vendor contracts.\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EThis article contains forward-looking statements, including about our future product offerings, and are not commitments to deliver any product offerings. Actual results and offerings may differ and are subject to known and unknown risk and uncertainties.\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003E\u003Ci\u003EApache®, Apache Iceberg, Apache Spark, and related marks are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.\u003C/i\u003E\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","image","blog_text_1081390971","wistia_video","blog_text_1003967330"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"columnClassNames":{},"gridClassNames":"aem-Grid aem-Grid--12 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