Anatomy of an AI Application – Clumio Final Cut Box Office Analyzer with Commvault

Cloud-native applications depend on data distributed across databases, object stores, retrieval pipelines, and lakehouse tables. When that data is deleted, corrupted, or changed by a bad deployment, recovery can require full restores, temporary resources, application reconfiguration, and rebuilding downstream dependencies. In the final scenario of their Cloud Field Day 26 session, Jeff Cassianna and his colleague Akshay turned to the Box Office Analyzer feature of the fictitious Clumio Final Cut app. The feature draws on a rich dataset stored in a lakehouse built on Apache Iceberg, which Akshay described as the preferred format for analytics at scale. The data lives in Amazon S3 Tables, is queried through Amazon Athena, and is visualized in QuickSight; it serves end users, internal employees, and production houses who build their own dashboards on it. Iceberg’s weakness is that a schema change or dropped column creates a mismatch with what visualizations expect. In the example, someone altered the table’s column structure, which broke both the in-app charts and any external dashboards. The revenue-by-genre chart then showed every movie as a comedy.

From an SRE perspective, Akshay noted that the last known good recovery point for an Iceberg table can be a point in time or a snapshot, depending on whether you ask a data admin or a storage admin. An out-of-place recovery would also force teams to recreate every dashboard built on the data. Before native support existed, Iceberg data could only be protected by backing up the general-purpose S3 bucket underneath it. Recovery that way means restoring every bucket in full, rebuilding the Iceberg structure of metadata, manifest, and data files, repointing the application to the new tables, and recreating all dashboards inside and outside the app, which Akshay called “recovery hell.” Clumio, which he said was the first offering to support Iceberg, makes backups and restores that are Iceberg-aware and transactionally consistent. That preserves the table structure and reduces recovery to a table-level, in-place rollback with no reconfiguration. Clumio also places no limit on retained snapshots. Customers facing FINRA or HIPAA retention requirements can therefore keep a lean primary lakehouse and offload older snapshots, since a growing snapshot tree degrades performance. Clumio can also migrate Iceberg tables from Glue Data Catalog or general-purpose buckets to S3 Tables, which Jeff noted is only the third new bucket type in AWS’s 20-year history. The capability currently covers AWS only.

Jeff’s demo showed the AI Movie Assistant correctly identifying “Clumio to the Moon” as an action-adventure film. He then staged a double failure by corrupting both the Iceberg table and the movie’s S3 JSON object, after which the revenue chart attributed about $400 million entirely to comedy and the chatbot mislabeled the genre. He restored the table from the isolated Secure Vault copy by selecting a point in time matched against available Iceberg snapshots, and he restored the object through Backtrack for S3. Both tasks finished in about a minute, and the chart and chatbot answers returned to normal. In response to delegate questions, Akshay explained that restores work with native Iceberg snapshots. Customers can recover one, several, or the full snapshot chain and set any of them as the latest. A restore to a new table rewrites the data, while an in-place restore places the snapshot where it belongs. Jeff closed with Commvault’s guidance on architecting for resiliency in AI applications. He urged teams to protect the entire data pipeline, not isolated pieces, as agentic AI adds complexity; plan for the near-real-time resolution customers demand to avoid brand damage and churn; and choose a solution that can keep pace as apps scale to millions of users and new geographies.