Autonomous management
for your data lake
Cut costs and accelerate queries with autonomous management across your data and engines—with built-in agentic AI support.
Runs on your stack
Lakehouse Control Plane
Cost down. Performance up.
Agentic AI ready.
End-to-end optimization for every table op, across storage and engines. Telemetry-driven smart orchestration with full visibility and control.
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 |
Autonomous Management
Snapshot retention, manifest rewrites, and orphan file cleanup — automated, scheduled, and safe across every table.
Explore managementFull Lake Observability
Monitor engines, query latency, throughput, and error rates. Cross-system telemetry from one place.
Explore observabilityAgentic AI Enablement
Optimized metadata and table structure for AI agents, feature stores, and autonomous pipelines.
Explore AI enablementIntelligent Compaction
Rust-based engine that organizes data by real query usage — every cycle cuts IO and speeds up reads.
Explore compactionGovernance and Policies
Define and enforce maintenance policies across catalogs. Auditable, versioned, one toggle.
Explore governanceMulti-Engine Query Routing
Route queries across Trino, Spark, Snowflake, and more — optimized for cost, latency, or throughput.
Explore routingMinutes to value with no risk
Connect & collect telemetry
Manual or autonomous management
Operations run & optimize
Observability & governance
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 observabilityBuilt for enterprise
grade data lakes
SOC 2, SSO, RBAC, dedicated support, and the scale your largest Iceberg lakes demand.
Security & compliance
SOC 2 Type II, encryption in transit and at rest, SSO/RBAC, and audit trails. Built for regulated teams.
Scale & control
One control plane for your full lake footprint. Real-time visibility, policy control, and predictable performance.
Support & training
Dedicated onboarding, expert training, and enterprise SLAs. Deploy in your VPC or on-prem.
Loved by data platform teams
LakeOps took the pain out of compaction and maintenance. We went from ad-hoc scripts and firefighting to a single control plane. Query performance improved and our platform team finally has visibility across the lake.
We evaluated several options for Iceberg operations. LakeOps stood out for its focus on automation and multi-engine support. Deployment was straightforward and the impact on cost and latency was measurable within weeks.
Our tables were suffering from small files and fragmented metadata. LakeOps runs continuously in the background—we set policies once and the system handles the rest. Maintenance automation that actually works.
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
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