Autonomous Lakehouse Control Plane
Connect catalogs, metadata, storage, and query engines through one autonomous control layer for optimization, governance, and cost-efficient scale.
Runs on your stack
Full Iceberg benefits.
Snowflake-level ease.
Monitor health, run compaction and maintenance—across catalogs and engines—and manage policies from a single view.
LakeOpsLast 30 days Optimization Activity
Key Metrics
Recent Operations
Last 10 operations| Operation | Table | Duration | Impact | Time | Status |
|---|---|---|---|---|---|
| Compact Data Files | customer_orders orders | 4s | 1.24 TB, 16 → 1 files | 57 minutes ago | SUCCESS |
| Expire Snapshots | payment_transactions payments | 27s | 8.2 TB | 4 hours ago | SUCCESS |
| Rewrite Manifests | raw_clickstream analytics | 1.9s | 3 → 1 manifests | 5 hours ago | SUCCESS |
| Compact Data Files | product_catalog products | 6m 11.3s | 3,008 → 1,256 files | 6 hours ago | SUCCESS |
| Remove Orphan Files | user_sessions analytics | 13m 6.9s | 59,831 files, 74.81 GB freed | 7 hours ago | SUCCESS |
Table Status Distribution
Top 5 Tables Needing Optimization
| Table Name | Table Size | Status | Last Scan |
|---|---|---|---|
| analytics.raw_clickstream | 4.6 TB | CRITICAL | 2 hours ago |
| analytics.search_query_logs | 3.2 TB | CRITICAL | 3 hours ago |
| analytics.user_sessions | 1.9 TB | CRITICAL | 4 hours ago |
| orders.customer_orders | 1.24 TB | CRITICAL | 1 hour ago |
| payments.payment_transactions | 860 GB | CRITICAL | 2 hours ago |
Minutes to value with no risk
Connect & collect telemetry
Manual or autonomous management
Operations run & optimize
Observability & governance
Capabilities
Managed Data. Optimized Ops.
Agentic AI ready.
Every layer of your lakehouse — from compaction and metadata to engines, observability, and policy enforcement — managed from one control plane.
Compaction Duration
Seconds
Cost of Compaction
Cost ($)
Compaction
Intelligent Compaction
Rust-based compaction engine for Iceberg — analyzes query patterns and access frequency to optimize file layout at scale. Run more compactions in less time with minimal resource footprint, so your lake stays performant without blocking writes or queries.
- 95% faster engine with Rust and AI
- Organize data by real query usage to cut IO
| SNAPSHOT ID | TIMESTAMP | OPERATION | MANIFESTS | ADDED | ACTIONS |
|---|---|---|---|---|---|
| 6847201938742 | Mar 15, 2026 12:18 PM | Append | 12 | +4 | 🔍⇄⏱ |
| 6847201938740 | Mar 15, 2026 11:45 AM | Append | 11 | +2 | 🔍⇄⏱ |
| 6847201938738 | Mar 15, 2026 10:30 AM | Append | 10 | +6 | 🔍⇄⏱ |
| 6847201938736 | Mar 14, 2026 08:00 PM | Append | 8 | +3 | 🔍⇄⏱ |
Version History & Time Travel
Total Snapshots
154
Retention Policy
30 days
Latest Operation
Append
Management
Snapshot Lifecycle Management
Automated 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.
Rewrite Manifests
Consolidate manifest files for faster query planning
Rewrite Position Deletes
Optimize position delete files to improve read performance
Compute Statistics (Puffin)
Calculate column stats to optimize query planning and pruning
Manifest & Metadata Optimization
Consolidate 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 that touches your lake. Includes position delete file optimization and Puffin statistics computation.
Remove Orphan Files Policy
Clean up files no longer referenced by any table
Basic Information
Name and priority
Target Scope
Where this policy applies
Execution Schedule
When the policy runs
Orphan File Configuration
How orphans are identified
Orphan File Detection & Cleanup
Detect 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.
Engine Load Distribution
Trino
Active- Queries: 256Avg: 1.8s
Snowflake
Active- Queries: 192Avg: 2.1s
AWS Athena
Active- Queries: 128Avg: 2.3s
DuckDB
Active- Queries: 64Avg: 0.5s
Engines and AIs
Multi-Engine Query Routing
Connect 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 — all without engine-specific scripts or duplicate tooling.
Groups
4
Configured endpoints
Active
3
Passing traffic
Inactive
1
Paused groups
Endpoints
8
Publicly available
All routing groups
Interactive customer behavior and funnel exploration workloads.
Operational writes and near-real-time checkout event ingestion.
Nightly catalog transformations and product availability sync jobs.
Scheduled BI reports and financial dashboard refresh workloads.
Agentic AI Enablement
Built for AI and ML pipelines — optimized metadata, layout, and table structure for agents, feature stores, and autonomous data workflows. Run simulations on file layout changes before applying them. Fast, consistent access to table state and history so AI pipelines get the data they need without extra glue.
Active Engines
4/6
Avg Latency
1.2s
↓ 15% vs yesterday
Throughput
2.4K q/h
↑ 8% vs last week
Error Rate
0.02%
↓ vs last week
Recent Alerts
Table optimization completed for orders.customer_orders
2 min ago
Snapshot expiration policy executed — 12 snapshots cleaned
15 min ago
High query latency detected on Trino endpoint
1 hr ago
Orphan file cleanup completed — 847 MB reclaimed
3 hrs ago
Observability
Full Lake Observability
Continuous 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.
Policies
Manage all policies including configuration, maintenance, delete, and truncate policies.
| Status | Policy | Type | Next | Actions |
|---|---|---|---|---|
Orders compaction | Manifests | Mar 16, 02:00 | ||
Catalog manifest rewrite | Manifests | — | ||
Payments orphan cleanup | Orphan Files | Mar 16, 03:00 | ||
Warehouse snapshot expiry | Snapshots | Mar 16, 01:00 | ||
Loyalty stats refresh | Config | — |
Governance
Governance and Policies
Define 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.
Why LakeOps
The control plane
for your lakehouse
From cost and performance to AI readiness — one platform that covers every dimension of lake operations.
Managed Iceberg
Autonomous compaction, snapshots, manifests, and orphan cleanup for every table.
Explore Managed IcebergAgentic AI readiness
Agent-native MCP interface, guardrails, and a self-optimizing lake for AI workloads.
Explore AI enablementCost reduction
Eliminate small files, orphans, and over-provisioned compute automatically.
Explore cost optimizationQuery performance
Adaptive data layout, lean manifests, and optimized file sizes for faster reads.
Explore performance impactMulti-engine routing
Route queries across Trino, Spark, Snowflake, and more — optimized per workload.
Explore routingLakehouse observability
Table health, engine metrics, and cross-system telemetry from one control plane.
Explore observabilityResults
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
Get in touch
See LakeOps in action
Get a personalized walkthrough of the LakeOps platform with your data. Short call, your architecture.
No commitment · Typically 30 min