Managed Lakehouse & Data Lake
The autonomous control plane that keeps your Iceberg lakehouse healthy, fast, and cost-efficient. Compaction, maintenance, query routing, observability, and governance — one platform across every engine, catalog, and cloud.
For data platform teams running Apache Iceberg at production scale. Set up in 10 minutes — no code changes, no vendor lock-in.
Runs on your stack
The Problem
Without active management, Iceberg tables degrade silently — files accumulate, metadata bloats, costs grow, and every query across every engine pays the price.
Streaming and frequent writes create thousands of tiny files — each costs an S3 GET, a metadata read, and a planner entry.
Without query-aware sort, predicate pushdown fails. Engines scan every row group regardless of filter patterns.
Hundreds of manifests and stale snapshots accumulate. Query planning grows from milliseconds to seconds.
Unreferenced objects and position delete files pile up — inflating storage costs and adding read-time overhead.
Multiple engines read the same tables but telemetry is siloed — no unified view of health, costs, or performance.
Without routing, every query lands on one engine — dashboards wait behind ETL, scan-priced queries waste compute.
Retention, compaction, and access rules stay manual per-table — no auditable, lake-wide governance.
AI pipelines need fast, consistent, well-structured tables — unmaintained lakes break agent queries silently.
Results
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
Table health
Autonomous maintenance keeps every table optimized
Lakehouse Control Plane
Compaction, maintenance, cost optimization, query routing, observability, governance, and AI readiness — managed autonomously from a single control plane across your entire data lake.
The complete control plane — compaction, maintenance, cost optimization, routing, observability, governance, and AI readiness.
Explore platformSnapshot expiration, manifest rewrites, orphan cleanup, and metadata — automated, sequenced, and safe.
Explore maintenanceLearns from production queries and sorts data to match — autonomous, event-driven, and 95% faster than Spark on a Rust engine.
Explore compactionRoute queries across Trino, Spark, Snowflake, and more — optimized for cost, latency, or throughput per workload.
Explore routingTable health, insights, cross-engine telemetry, policies, retention, and audit trails — one control plane.
Explore observabilityAgent-native MCP interface, guardrails, and a self-optimizing lake ready for AI agents and autonomous pipelines.
Explore AI enablementPlatform Preview
See how LakeOps manages maintenance, compaction, routing, and observability from a single interface — across your entire data lake.
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 |
Only metadata is processed — never retained or stored.
Telemetry reveals table health and actions needed.
Autopilot, manual approval, or policy-driven.
“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.”
Production benchmarks
Real workloads. Real data. Batch, streaming, delete-heavy, and multi-writer tables — same engine, same hardware.
| Table | Size | Workload | Files (B → A) | Throughput | Time |
|---|---|---|---|---|---|
| balance_snapshots | 1,192 GB | TB-Scale batch | 11,957 → 3,270 | 1,572 MB/s | 11 min |
| events_analytics | 484 GB | Delete-Heavy | 16,128 → 7,198 | 729 MB/s | 11m 21s |
| raw_sdk_events | 8 GB | Streaming | 42,633 → 69 | 167 MB/s | 138s |
| site_traffic | 292 GB | Multi-Writer | 2,740 → 754 | 1,465 MB/s | 3m 25s |
200 GB benchmark (seconds)
95% faster
Normalized to Spark = 100%
90% cheaper
Avg. query latency after compaction
8x faster queries
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.
How to route queries across Trino, Spark, DuckDB, Snowflake, Athena, and Flink on shared Iceberg tables — SQL routing proxy, dialect translation, and table-aware optimization.
FAQ
See LakeOps running on your own tables. Connect a catalog, set policies, and watch your data lake manage itself — in a 30-minute walkthrough.
No commitment · Typically 30 min · Works with your existing stack
