Managed Iceberg Lakehouse
Connect your catalog and every table gets maintained, optimized, and governed — across every engine, without a single script.
LakeOps| 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 |
| Expire Snapshots | inventory_snapshots_20250702 warehouse | 3s | 2.1 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 |
Open and runs on your stack
The Platform
Observe, maintain, compact, route, and govern — from one console, across every engine in your lake.
| Table | NS | Size | Status |
|---|---|---|---|
| customer_orders | orders | 1.24 TB | HEALTHY |
| payment_transactions | payments | 860 GB | WARNING |
| raw_clickstream | analytics | 4.6 TB | CRITICAL |
| product_catalog | products | 42 GB | HEALTHY |
| user_sessions | analytics | 1.9 TB | WARNING |
| inventory_levels | operations | 320 GB | HEALTHY |
| shipping_events | logistics | 580 GB | HEALTHY |
| search_query_logs | analytics | 3.2 TB | CRITICAL |
Learns from query patterns and table signals to decide what to optimize, when, and how — no schedules, no manual tuning.
Each capability kicks in automatically — under the hood, this is what runs.
Monitoring started at seq #4,812. Everything written from that point on is in scope.
Maintenance
Snapshots pile up. Manifests bloat. Orphan files sit on the bill. LakeOps expires, cleans, and rewrites metadata — triggered by table health, sequenced so each step’s output is the next step’s clean input.
customer_idevent_dateproduct_idregionBefore
After
12×
Faster queries
76%
Less CPU
95%
vs Spark
90%
Cheaper ops
200 GB compaction · same hardware
Compaction
Most compaction just bins small files. LakeOps watches the columns your queries filter, join, and group on — then physically re-sorts data to match. Engines skip entire files. The work itself runs on a Rust engine: 95% faster and 90% cheaper than Spark.
Compare engines side-by-side on cost, latency, throughput, and data scanned.
| Metric | Spark | Trino | Athena | Snowflake |
|---|---|---|---|---|
| Query success rate | 99.2% | 99.5% | 99.9% | 99.8% |
| Average runtime | 3.1s | 1.8s | 2.3s | 2.1s |
| Cost per query | $0.04 | $0.03 | $0.05 | $0.08 |
| Total queries | 3,120 | 2,456 | 1,280 | 1,876 |
| Data scanned | 4.2 TB | 2.8 TB | 1.5 TB | 3.5 TB |
Lower-left is ideal
Routing
The same query costs $0.08 on Snowflake and $0.03 on Trino. LakeOps compares cost, latency, and success rate across every connected engine, then routes each query to the one that wins.
Manage maintenance, configuration, and lifecycle policies for your data lakehouse
| On | Policy | Type | Next Run | Last Run | Updated | Actions |
|---|---|---|---|---|---|---|
orders_critical | Compact Files | Apr 25, 2026, 8:12 AM | Apr 25, 2026, 03:05 AM | Feb 01, 2025, 3:46 PM | ••• | |
payments_compact | Compact Files | Feb 15, 2026, 12:06 AM | Feb 1, 2025, 02:18 PM | Feb 5, 2025, 4:03 PM | ••• | |
Remove orphan files (e-ip...) For all tables in all catalogs every 7 days | Orphan Files | Apr 25, 2026, 8:12 AM | Apr 25, 2026, 04:07 PM | Jun 23, 2025, 04:01 PM | ••• | |
clickstream_cdc_events_p | Expire Snapshots | Apr 25, 2026, 12:03 AM | Apr 25, 2026, 03:05 AM | Jan 28, 2025, 03:25 PM | ••• | |
sessions_cdc_events_p | Expire Snapshots | Apr 25, 2026, 12:03 AM | Apr 26, 2026, 03:05 AM | Jun 26, 2025, 11:11 PM | ••• | |
global_expire_snapshots Runs snapshot expiration on all tables once a day | Expire Snapshots | Apr 26, 2026, 1:18 PM | Apr 07, 2026, 01:08 PM | Mar 14, 2026, 8:42 AM | ••• | |
manifest_rewrite_weekly Rewrite manifests for all critical tables weekly | Rewrite Manifests | Apr 28, 2026, 2:00 AM | Apr 21, 2026, 02:00 AM | Mar 10, 2026, 9:15 AM | ••• | |
staging_config | Configuration | — | — | Dec 31, 2025, 02:45 PM | ••• |
Governance
Retention windows, compaction thresholds, and cleanup rules live in scripts owned by whoever wrote them. LakeOps replaces that with declarative policies that cascade from org to table.
raw_clickstreamCritical312 partitions exceed file threshold
Query scan amplified 8×
search_query_logsHighExcessive manifests (487) — planning overhead
Planner latency +2.1s
payment_transactionsWarningSmall file ratio 38% — compaction recommended
S3 GET costs elevated
1.8 TB
Space freed
4.2 TB
Optimized
247
Tables
2%
Failure rate
Observability
You cannot maintain what you cannot see. LakeOps scores every table as Healthy, Warning, or Critical — combining file fragmentation, manifest depth, snapshot velocity, sort-order drift, and orphan volume into a single health signal.
list_catalogsreadEnumerate all registered catalogsget_table_healthreadHealth score + maintenance statusrun_queryexecuteRouted through guardrails pipelineanalyze_storage_reclaimreadSnapshot bloat + orphan insightsget_optimization_statusreadCompaction + manifest stateReadOnly
Blocks DDL and DML from agent sessions
CostEstimate
Rejects queries exceeding scan thresholds
PIIMask
Hashes sensitive columns before results reach the model
HumanApproval
Pauses high-stakes operations for review
AI Agents
AI agents issue SQL iteratively, at high frequency, without human review. LakeOps provides a native MCP interface so any agent discovers schemas, executes queries, and analyzes health — with composable safety guardrails.
See It In Action
From catalog connection to fully autonomous maintenance, routing, and governance — in one walkthrough.
Results
Benchmarks from production-grade tables across multiple engines and clouds.
Query speed
After compaction + layout optimization
CPU reduction
Compute hours across all engines
Storage saved
Orphans, snapshots & bloat removed
Table health
Autonomous maintenance keeps every table optimized
Only metadata is processed — never retained or stored.
Telemetry reveals table health and actions needed.
Autopilot, manual approval, or policy-driven.
SOC 2, SSO, RBAC, dedicated support, and the scale your largest Iceberg lakes demand.
SOC 2 Type II, encryption, SSO/RBAC, and audit trails for regulated teams.
One control plane for your full lake. Real-time visibility, policies, and predictable performance.
Dedicated onboarding, training, and enterprise SLAs. Deploy in VPC or on-prem.
Testimonials
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.
Resources
Iceberg tables degrade silently — small files pile up, snapshots bloat metadata, and query latency creeps higher. A breakdown of the nine components every production data lake needs to stay healthy.
Netflix spent years building an intelligent lakehouse — Polaris, Autotune, janitors, and Metacat. LakeOps lets every team build the same — and go beyond — in minutes.
A data lakehouse control plane is the automated operational intelligence layer on top of your lakehouse infrastructure — providing full observability, governance, and control while continuously maintaining and optimizing every Iceberg table and query engine for performance and cost, without vendor lock-in.
FAQ
Glue, Polaris, REST, S3 Tables, or Nessie. About 10 minutes. Data never leaves your cloud account.
No commitment · Typically 30 min · Works with your existing stack
