Solutions
LakeOps automates compaction, snapshot hygiene, and routing while you keep your existing tables, catalogs, and engines. Reduce spend, improve latency, and keep table health visible in one control plane—then enable agent-ready data and multi-engine routing when your team is ready.
Lakehouse Control Plane
End-to-end optimization for every table op, across storage and engines. Telemetry-driven smart orchestration with full visibility and control.
A unified control plane that understands your lake end-to-end — tables, engines, queries, and costs — and acts on it autonomously.
Read articleSnapshot expiration, manifest rewrites, orphan cleanup, and metadata — automated, scheduled, and safe.
Explore maintenanceRust-based engine that organizes data by real query patterns — every cycle cuts IO, shrinks file counts, and speeds up reads.
Explore compactionRoute queries across Trino, Spark, Snowflake, and more — optimized for cost, latency, or throughput per workload.
Explore routingAgent-native MCP interface, guardrails, and a self-optimizing lake ready for AI agents, feature stores, and autonomous pipelines.
Explore AI enablementTable health, engine metrics, cross-system telemetry, and policy-driven governance from one control plane.
Explore observability
Runs on your stack
See it in action
Get a walkthrough of how LakeOps applies policies, runs optimization, and surfaces impact across engines—on your own tables.
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.
“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, multi-writer, and terabyte-scale tables — all on the same engine, same hardware.
| Table | Size | Workload | Files (B → A) | Throughput | Time | Notes |
|---|---|---|---|---|---|---|
| balance_snapshots | 1,192 GB | TB-Scale batch | 11,957 → 3,270 | 1,572 MB/s | 11 min | Spark OOM on same hardware |
| user_accounts | 174 GB | Batch | 878 → 400 | 2,269 MB/s | 74s | Single Node |
| events_analytics | 484 GB | Delete-Heavy | 16,128 → 7,198 | 729 MB/s | 11m 21s | 23,433 delete files; 551M rows removed |
| raw_sdk_events | 8 GB | Streaming | 42,633 → 69 | 167 MB/s | 138s | 99.8% file reduction |
| site_traffic | 292 GB | Multi-Writer | 2,740 → 754 | 1,465 MB/s | 3m 25s | Single partition |
| cluster_registry | 322 GB | Batch | 998 → 440 | 2,522 MB/s | 2m | Peak throughput |
Normalized to Spark = 100%
Source: 200 GB (~1 TB uncompressed) benchmark. Spark cost index 100 vs LakeOps 10.
balance_snapshots — 1.192 TB across consecutive runs
Same data and hardware; planner learns workload telemetry and improves runtime from 22 to 11 minutes.
