Platform

Control plane foryour data lake

End-to-end optimization for tables and metadata across storage and query engines. Telemetry-driven orchestration, real-time maintenance automation, and full visibility in one place.

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Runs on your stack

Platform overview

Full Iceberg benefits. Snowflake-level ease.

Monitor health, run compaction and maintenance—across catalogs and engines—and manage policies from a single view.

LakeOps

Last 30 days Optimization Activity

Total Operations

12211

successfully run in Last 3 months

Query speed

12.4x

Avg. acceleration across engines

Cost savings

$1,374,672

Saved in last 3 months

CPU & Storage

-76%

Resources saved in last 3 months

Data Optimized

46.8 PB

Volume compacted in last 30 days

Key Metrics

Total Tables

786

Tables in all catalogs

Critical Tables

Require Immediate attention

Warning Tables

105

Should be addressed or auto-piloted

Healthy Tables

566

Tables in optimal state

Total Data

112.4 PB

Total lake data size

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
Expire Snapshots	inventory_snapshots_20250702 warehouse	3s	2.1 TB	4 hours ago	SUCCESS

Compaction Duration

Seconds

8000

6000

4000

2000

6300s

1612s

221s

780s

S3 Tables

Apache Spark

LakeOps

LakeOps (Sort)

Cost of Compaction

Cost ($)

100%

S3 Tables

Apache Spark

LakeOps

LakeOps (Sort)

01Feature

20x Faster compaction with Rust and AI

Rust-based compaction engine for Iceberg—optimizes file layout at scale. Run more compactions in less time with minimal resource footprint, so your lake stays performant without blocking writes or queries.

Rewrite Manifests

Consolidate and optimize manifest files for improved metadata performance

Rewrite Position Delete Files

Optimize position delete files to improve query performance

Compute Table Statistics (Puffin)

Calculate statistics to optimize query planning and performance.

02Feature

Manifest Rewrites

Compact metadata so query planning stays fast across the lake. Smaller manifests mean faster planning and fewer metadata scans for every engine—Trino, Spark, Flink, and more.

Recent Operations

Last 10 operations

Operation	Table	Duration	Size Reclaimed	Time
Expire Snapshots	customer_orders/ orders	3m 47s	1.8 TB	1 hour ago
Expire Snapshots	product_catalog/ catalog	0s	-	1 hour ago
Expire Snapshots	payment_transactions/ payments	23s	12.4 TB	1 hour ago
Expire Snapshots	loyalty_points_balance/ loyalty	3s	9.2 TB	1 hour ago

03Feature

Snapshot Optimization

Automated retention and expiration—no manual snapshot hygiene. Set policies once; LakeOps expires old snapshots and cleans history safely, with full awareness of concurrent readers and writers.

Remove Orphan Files Policy

Clean up files no longer referenced by any table

Basic Information

Name and priority

Policy Name

e.g. Production Orphan Cleanup

Priority

StatusEnabled

Target Scope

Where this policy applies

Catalog

Select a catalog

Namespace / Tables (optional)

Leave empty for entire catalog

Execution Schedule

When the policy runs

Cron Expression

0 0 * * *At 12:00 AM daily

Orphan File Configuration

How orphans are identified

Older Than (Days)

7Unreferenced files older than this are removed

04Feature

Orphan File Cleanup

Detect and remove orphaned files safely. Eliminate storage drift from failed jobs, aborted commits, and legacy tables—reclaim capacity without risking data integrity.

Policies

Manage all policies including configuration, maintenance, delete, and truncate policies.

All Types

All Status

Policy	Type	Next
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	—

05Feature

Organization policies

Define and enforce policies across catalogs and tables—retention, compaction thresholds, and maintenance windows. Keep the whole organization aligned with consistent rules and guardrails.

Tables

Browse and manage your tables

Catalog

main

Namespace

All Namespaces

Status

All Statuses

Search tables...

Table name	Namespace	Records	Size	Status	Last modified
customer_orders	orders	2.4M	1.2 GB	HEALTHY	Mar 15, 2026, 12:18 PM
product_catalog	catalog	156K	84 MB	HEALTHY	Mar 15, 2026, 12:18 PM
payment_transactions	payments	8.1M	2.4 GB	HEALTHY	Mar 15, 2026, 12:17 PM
inventory_snapshots	warehouse	432K	356 MB	HEALTHY	Mar 15, 2026, 12:17 PM
loyalty_points_balance	loyalty	1.2M	128 MB	HEALTHY	Mar 15, 2026, 12:17 PM
user_sessions	analytics	5.8M	892 MB	HEALTHY	Mar 15, 2026, 12:16 PM

06Feature

Table Health Monitoring

Continuous analysis of table structure and optimization opportunities. See which tables need compaction, have too many small files, or have stale metadata—with clear priorities and one-click or automated remediation.

orders

Total Partitions

3,632

Total Data Files

110,961

Total Data Size

52.52 TB

Avg Files/Partition

Partition Details

Showing 50 of 3,632 partitions.

Page 1 of 73

PARTITION PATH	DATA FILES	DATA SIZE	RECORDS	AVG FILE SIZE	DELETE FILES
region=eu/country=de/order_date=2025-01-15	20	8.94 GB	270,209,993	457.71 MB	—
region=na/country=us/order_date=2025-02-01	4	1.54 GB	41,333,014	394.93 MB	—
region=eu/country=uk/order_date=2025-01-28	16	7.36 GB	205,158,536	470.75 MB	—

07Feature

In-depth table exploration

Drill into any table with partitions, metrics, and SQL. View partition details, file size distribution, records over time, and run queries—all in one place with full visibility.

orders

SQL Query

SQL Query ▾Spark ▾Press Ctrl+Enter to execute

SELECT *
FROM ecommerce.retail.orders
LIMIT 5;

Results5 rows · Spark · 0.4s

order_id	customer_id	region	country	order_date	amount_usd	status
ORD-2847109	C-88234	eu	DE	2025-01-15	429.00	delivered
ORD-2847112	C-91002	na	US	2025-01-15	1,249.50	shipped
ORD-2847118	C-77401	eu	UK	2025-01-16	89.99	delivered
ORD-2847124	C-55291	apac	JP	2025-01-16	312.00	processing
ORD-2847130	C-12088	na	CA	2025-01-17	567.25	shipped

08Feature

Test queries and engines

Run SQL against any table and choose the engine—Spark, Trino, Flink, or others. Validate queries and compare results across engines without leaving the control plane.

Query Engines

Manage and monitor your connected query engines.

Compare Engines

Compare performance across engines

Compare

Engine Health

Monitor health of query engines

View health

Add Engine

Connect a new query engine

Add engine

Status:All

Active

AWS Athena

Queries: 128Avg: 2.3sCost: $0.0510 min ago

View QueriesConfigure

Active

Trino

Queries: 256Avg: 1.8sCost: $0.035 min ago

View QueriesConfigure

Active

Snowflake

Queries: 192Avg: 2.1sCost: $0.0830 min ago

View QueriesConfigure

Inactive

Spark

Queries: 32Avg: 1.2sCost: $0.021 day ago

View QueriesConfigure

Maintenance

Flink

Queries: 48Avg: 0.9sCost: $0.023 days ago

View QueriesConfigure

Active

DuckDB

Queries: 64Avg: 0.5sCost: $0.012 hrs ago

View QueriesConfigure

09Feature

Multi-Engine Routing

Optimize for Trino, Spark, Flink, and more in one operational layer. No engine-specific scripts or duplicate tooling—one set of policies and one execution layer for your entire lake.

idnamecreated_at

+ add

idnamecreated_atregion

10Feature

Managed Schema Evolution

Schema changes applied safely across engines and workloads. Add, drop, or rename columns with compatibility checks and rollout orchestration so every consumer stays in sync.

11Feature

Cross-System Telemetry

One source of truth across storage, engines, and catalogs. Ingest metrics from S3, GCS, ADLS, and every engine that touches your tables—then view, alert, and act from a single control plane.

Iceberg

works with

12Feature

Native to AI Agents

Built for AI and ML pipelines—optimized metadata and layout for agents and feature stores. Fast, consistent access to table state and history so training and inference pipelines get the data they need without extra glue.

Get in touch

See LakeOps in action

Get a personalized walkthrough of the LakeOps platform with your data. Short call, your architecture.

Book a Demo

No commitment · Typically 30 min