Managed Apache Iceberg
Managed Iceberg,
smart and simple
LakeOps continuously optimizes compaction, data layout, and autonomous table maintenance — snapshots, manifests, metadata, and orphan files — across every engine and cloud, so your Iceberg tables stay fast, lean, and production-ready.
The challenge
Iceberg is powerful — but hard
to operate at scale
Rising cost & latency
Small files, stale snapshots, and orphaned files compound quietly, driving up compute and storage cost while query latency keeps drifting.
Manual engine operations
Spark, Trino, Athena, Snowflake, and Databricks optimize differently. Teams juggle per-engine scripts, configs, and schedules that do not scale.
Metadata & layout drift
Manifests bloat, partitions skew, and layouts drift from real workloads, degrading scan efficiency, cache locality, and query planning.
Ops debt & compliance
Ad-hoc scripts and reactive firefighting create operational debt. Retention, DR, and GDPR policies stay manual instead of control-plane enforced.
Results
Measured impact on
real Iceberg workloads
Benchmarks from production-grade tables across multiple engines and cloud providers.
Compaction speed
vs. Apache Spark on identical datasets
Query performance
After compaction + layout optimization
Cost savings
In compute & storage spend
Capabilities
Every layer of your Iceberg lake — managed
From compaction and metadata to engine routing, observability, and policy enforcement — one AI-driven control plane that optimizes continuously and autonomously.
Compaction Duration
Faster and Smarter Compaction
01Rust-based compaction engine that analyzes query patterns and access frequency to optimize file layout at scale. 95% faster than Spark, organizes data by real query usage to cut IO — so your lake stays performant without blocking writes or queries.
Snapshot Lifecycle Management
02Automated retention, expiration, and version history for every table. Set policies once — LakeOps expires old snapshots safely with full awareness of concurrent readers. Time-travel to any point, compare snapshots, and roll back without manual intervention.
Manifest & Metadata Optimization
03Consolidate and rewrite manifest files so query planning stays fast at any scale. Smaller manifests mean faster planning and fewer metadata scans for Trino, Spark, Flink, and every engine. Includes position delete file optimization and Puffin statistics computation.
Orphan File Detection & Cleanup
04Detect and safely remove files no longer referenced by any table. Eliminate storage drift from failed jobs, aborted commits, and legacy tables. Configurable retention thresholds, catalog-wide or per-table scope, and scheduled execution — reclaim capacity without risking data integrity.
Full-Stack Observability
05Continuous analysis of table structure, file health, and optimization opportunities. Monitor active engines, query latency, throughput, and error rates. Cross-system telemetry from S3, GCS, ADLS, and every engine — view, alert, and act from one place.
Organization-Wide Policies
06Define and enforce compaction, retention, orphan cleanup, and maintenance policies across catalogs and tables. Set schedules, priorities, and target scopes — then let LakeOps execute continuously. Every policy is auditable, versioned, and controllable with one toggle.
Multi-Engine Query Routing
07Connect Trino, Spark, Snowflake, Athena, DuckDB, and Flink to one routing layer. Intelligent query routing optimizes for cost, latency, or throughput automatically. Compare engine performance, monitor health, and add new engines — no engine-specific scripts or duplicate tooling.
Agentic AI Enablement
08Built for AI and ML pipelines — optimized metadata, layout, and table structure for agents, feature stores, and autonomous data workflows. Fast, consistent access to table state and history so AI pipelines get the data they need without extra glue.
Value in minutes with zero risk
No agents, no data movement, no pipeline changes. Connect your catalog, let LakeOps analyze, simulate, and optimize — safely.
Connect in ~10 minutes
Connect your catalog and storage in ~10 minutes. No agents, data movement, or pipeline changes.
AI analyzes & simulates
LakeOps continuously models table health from metadata, query patterns, and cost signals.
Automated optimization
Compaction, manifest cleanup, snapshot hygiene, and layout tuning run continuously on autopilot.
Visibility & governance
Unified dashboards track cost, performance, and table health. Every action is logged and reversible.
Works with your stack
One control plane — any engine, catalog, or cloud
LakeOps connects to your existing infrastructure. No vendor lock-in — your data, metadata, and execution stay under your control.
LakeOps Control Plane
Connects, analyzes, optimizes
Engines
Snowflake
Databricks
Amazon Athena
Dremio
DuckDB
Apache Flink
ClickHouseCatalogs
AWS Glue
Apache Polaris
Apache Gravitino
Project Nessie
LakeKeeperClouds & on-prem
AWS
Azure
Google Cloud
Agentic AI readiness
Your Iceberg lake,
ready for AI agents
AI agents are becoming primary consumers of SQL infrastructure. LakeOps is the control plane that makes your lake intelligent — agent-native interface, built-in guardrails, self-optimizing storage, and a closed-loop feedback system that learns from every query.
AI Agents
Claude, LangChain,
custom MCP agents
LakeOps
Iceberg Lake
Tables, metadata,
engines, catalogs
Agent-native interface
Native MCP server connects any compatible agent — Claude, LangChain, or custom — with zero integration code. Schema-aware tools, async queries with SSE streaming, and Postgres/MySQL/Arrow Flight wire compatibility.
Safety & governance
Layered guardrails for unsupervised execution — ReadOnlyGuard blocks DDL, CostEstimateGuard rejects expensive scans, PIIMaskGuard scrubs sensitive columns, HumanApprovalGuard pauses high-stakes queries.
Intelligent routing
Three-router stack — Adaptive routes on history, LLM reasons over new templates with live table stats, Semantic matches intent. 0ms cached decisions, data-quality-aware routing enriched by IceProbe.
Self-optimizing lake
Agents querying uncompacted tables pay 5–10× latency penalty. The workload analyst feeds agent query signals to the Rust compaction engine, and the feedback loop auto-updates routing as tables improve.
Full Iceberg benefits.
Snowflake-level ease.
Get a personalized walkthrough on your own Iceberg tables — see the impact in minutes.