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Your data lake, optimized for the way ML teams actually work.

Data Lake ML

Designs and operates lakehouse architectures optimized for ML workloads — partitioned for training data access patterns, with schema evolution support and query performance tuned for large-scale feature extraction. Eliminates the data engineering bottleneck that prevents ML teams from accessing the data they need in the format they require.

Why It Matters

DataLakeML fixes the mismatch that quietly throttles every enterprise ML program — data lakes built for BI reporting, being hammered by ML teams with completely different access patterns.
Lakehouse architecture partitioned for training workloads, with schema evolution and feature-extraction-tuned queries, gives ML teams data in the shape and speed they actually need.
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ML workloads aren't BI workloads

Training jobs scan terabytes in patterns dashboard-optimized lakes handle terribly — ML-specific partitioning and query tuning turn multi-hour data pulls into minutes.

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Ends the data engineering queue

ML teams waiting weeks for data engineers to prepare extracts is the hidden bottleneck in most programs — self-serve, ML-ready data removes the dependency entirely.

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Schema evolution without breakage

Iceberg and Delta Lake support means upstream schema changes don't silently shatter training pipelines — the data foundation stays stable as source systems evolve.

The Cloudly Advantage

DataLakeML is the foundation layer beneath Cloudly’s entire platform — FeatureVault, TrainForge, and BatchInferenceEngine all perform only as well as the data lake feeding them.
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The platform's foundation deal

Every data-hungry product we sell runs better on a lake we built — DataLakeML engagements seed the account for the entire portfolio to follow.

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Largest services footprint

Lakehouse design, Spark and dbt pipelines, and ongoing operations are heavyweight data engineering engagements — long-duration consultancy with managed-operations tails.

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S3/Glue/Athena migration engine

Pre-built AWS stack patterns make every deployment an AWS Migration engagement — with BigQuery covering GCP-side deals in the same motion.

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Earliest-stage account entry

Enterprises not yet ready for MLOps still know their data is a mess — DataLakeML lands accounts at the start of their AI journey, before competitors have anything to sell them.

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Security defaults accelerate approval

Cloudly-hardened configurations pass security review faster — the same trust asset IaCForML builds, applied to the data layer where scrutiny is highest.

The Final Takeaway

Feeds the governance suite — A well-architected lake makes DataLineage360, DataQualityGuard, and DataDriftScan dramatically easier to deploy — the foundation purchase that lowers friction on every subsequent one.

Powered By

Apache Iceberg

Core to the DataLakeML technology stack.

Delta Lake

Core to the DataLakeML technology stack.

AWS S3/Glue/Athena

Core to the DataLakeML technology stack.

GCP BigQuery

Core to the DataLakeML technology stack.

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