When should I use Ilum instead of lakeFS?

Managed Spark job orchestration and monitoring UI for teams running Spark on Kubernetes. Organizations wanting a visual management plane over Spark scheduling without custom tooling. Teams who need a self-service interface for data engineers to submit and monitor Spark jobs

When should I use lakeFS instead of Ilum?

Git-like branching, commits, and rollback for data lake objects on S3, GCS, or ADLS. Isolated data environments for CI/CD testing of data pipelines without duplicating data. Teams wanting atomic commits and reproducible data states across ETL pipeline runs

What are the main weaknesses of Ilum?

Commercial product; full features require a paid license beyond the evaluation tier. Niche use case — most teams use Databricks or EMR for managed Spark and don't need this layer. Limited community resources and third-party integrations outside the vendor's own materials

What are the main weaknesses of lakeFS?

Adds an API proxy layer in front of object storage, introducing latency on all data access. Requires teams to adopt lakeFS CLI and API instead of direct S3 or GCS access patterns. Metadata server is an additional stateful component to operate, back up, and maintain

Ilum vs lakeFS: Key Differences for Python Data Engineering

Data Lake Management

Ilum

Data Lakehouse Platform

★ 3.9

Commercial

N/A — web application

lakeFS

Git-Like Data Lake Versioning

★ 4.5

Apache-2.0

pip install lakefs

Side-by-Side Comparison

Ilum

lakeFS

Ilum

lakeFS

Best For

✓Managed Spark job orchestration and monitoring UI for teams running Spark on Kubernetes
✓Organizations wanting a visual management plane over Spark scheduling without custom tooling
✓Teams who need a self-service interface for data engineers to submit and monitor Spark jobs

✓Git-like branching, commits, and rollback for data lake objects on S3, GCS, or ADLS
✓Isolated data environments for CI/CD testing of data pipelines without duplicating data
✓Teams wanting atomic commits and reproducible data states across ETL pipeline runs

Best For

✓Managed Spark job orchestration and monitoring UI for teams running Spark on Kubernetes
✓Organizations wanting a visual management plane over Spark scheduling without custom tooling
✓Teams who need a self-service interface for data engineers to submit and monitor Spark jobs

✓Git-like branching, commits, and rollback for data lake objects on S3, GCS, or ADLS
✓Isolated data environments for CI/CD testing of data pipelines without duplicating data
✓Teams wanting atomic commits and reproducible data states across ETL pipeline runs

Weaknesses

•Commercial product; full features require a paid license beyond the evaluation tier
•Niche use case — most teams use Databricks or EMR for managed Spark and don't need this layer
•Limited community resources and third-party integrations outside the vendor's own materials

•Adds an API proxy layer in front of object storage, introducing latency on all data access
•Requires teams to adopt lakeFS CLI and API instead of direct S3 or GCS access patterns
•Metadata server is an additional stateful component to operate, back up, and maintain

Weaknesses

•Commercial product; full features require a paid license beyond the evaluation tier
•Niche use case — most teams use Databricks or EMR for managed Spark and don't need this layer
•Limited community resources and third-party integrations outside the vendor's own materials

•Adds an API proxy layer in front of object storage, introducing latency on all data access
•Requires teams to adopt lakeFS CLI and API instead of direct S3 or GCS access patterns
•Metadata server is an additional stateful component to operate, back up, and maintain

License

Commercial

Apache-2.0

License

Commercial

Apache-2.0

Install

N/A — web application

pip install lakefs

Install

N/A — web application

pip install lakefs

Rating

★ 3.9

★ 4.5

Rating

★ 3.9

★ 4.5

Key Features

Ilum

1Managed Apache Spark platform with a web UI for job submission and monitoring
2Multi-cluster management enabling different Spark versions and configs per team
3Built-in Jupyter notebook integration for interactive Spark development
4REST API for programmatic job submission and cluster management
5Role-based access control for managing multi-team Spark environments

lakeFS

1Git-like branching and versioning for data lake objects on S3, GCS, or ADLS
2Branch data to experiment with transformations without affecting production
3Atomic commits group multiple object changes into a single consistent snapshot
4CI/CD hooks run data quality checks before merging data branches
5API and Python SDK for programmatic branch and merge operations

How Python Data Engineers Use These Tools

Ilum

Python data engineers use Ilum to submit and manage PySpark jobs without managing Spark cluster infrastructure directly. Ilum's REST API enables Python orchestration tools like Airflow to trigger Spark jobs programmatically as pipeline steps. It is used in organisations that need a self-hosted alternative to managed services like AWS EMR or Databricks, providing a control plane for Spark workloads running on Kubernetes or bare metal.

lakeFS

Python data engineers use lakeFS to apply software engineering practices to data lake management. A pipeline writes to a lakeFS branch, data quality tests run against the branch, and the Python SDK merges the branch to main only on test success. This prevents bad pipeline outputs from reaching production consumers — the same guarantee that Git branches provide for code changes.

More Data Lake Management Comparisons

Data Lake Management

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Data Lake Management

Ilum vs Project Nessie

Data Lake Management

FlightPath Data vs Project Nessie

Data Lake Management

FlightPath Data vs Ilum

Individual Tool Pages

View Ilum details →View lakeFS details →

Side-by-Side Comparison

Ilum

lakeFS

Ilum

lakeFS

Best For

✓Managed Spark job orchestration and monitoring UI for teams running Spark on Kubernetes
✓Organizations wanting a visual management plane over Spark scheduling without custom tooling
✓Teams who need a self-service interface for data engineers to submit and monitor Spark jobs

✓Git-like branching, commits, and rollback for data lake objects on S3, GCS, or ADLS
✓Isolated data environments for CI/CD testing of data pipelines without duplicating data
✓Teams wanting atomic commits and reproducible data states across ETL pipeline runs

Best For

✓Managed Spark job orchestration and monitoring UI for teams running Spark on Kubernetes
✓Organizations wanting a visual management plane over Spark scheduling without custom tooling
✓Teams who need a self-service interface for data engineers to submit and monitor Spark jobs

✓Git-like branching, commits, and rollback for data lake objects on S3, GCS, or ADLS
✓Isolated data environments for CI/CD testing of data pipelines without duplicating data
✓Teams wanting atomic commits and reproducible data states across ETL pipeline runs

Weaknesses

•Commercial product; full features require a paid license beyond the evaluation tier
•Niche use case — most teams use Databricks or EMR for managed Spark and don't need this layer
•Limited community resources and third-party integrations outside the vendor's own materials

•Adds an API proxy layer in front of object storage, introducing latency on all data access
•Requires teams to adopt lakeFS CLI and API instead of direct S3 or GCS access patterns
•Metadata server is an additional stateful component to operate, back up, and maintain

Weaknesses

•Commercial product; full features require a paid license beyond the evaluation tier
•Niche use case — most teams use Databricks or EMR for managed Spark and don't need this layer
•Limited community resources and third-party integrations outside the vendor's own materials

•Adds an API proxy layer in front of object storage, introducing latency on all data access
•Requires teams to adopt lakeFS CLI and API instead of direct S3 or GCS access patterns
•Metadata server is an additional stateful component to operate, back up, and maintain

License

Commercial

Apache-2.0

License

Commercial

Apache-2.0

Install

N/A — web application

pip install lakefs

Install

N/A — web application

pip install lakefs

Rating

★ 3.9

★ 4.5

Rating

★ 3.9

★ 4.5

Key Features

Ilum

1Managed Apache Spark platform with a web UI for job submission and monitoring
2Multi-cluster management enabling different Spark versions and configs per team
3Built-in Jupyter notebook integration for interactive Spark development
4REST API for programmatic job submission and cluster management
5Role-based access control for managing multi-team Spark environments

lakeFS

1Git-like branching and versioning for data lake objects on S3, GCS, or ADLS
2Branch data to experiment with transformations without affecting production
3Atomic commits group multiple object changes into a single consistent snapshot
4CI/CD hooks run data quality checks before merging data branches
5API and Python SDK for programmatic branch and merge operations

How Python Data Engineers Use These Tools