When should I use Ilum instead of Project Nessie?

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 Project Nessie instead of Ilum?

Multi-table ACID transactions and Git-like versioning for Apache Iceberg and Delta Lake tables. Teams building open lakehouse architectures needing table-level time-travel and branching. Catalog layer for Apache Iceberg that adds versioning without requiring a storage proxy

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 Project Nessie?

Branching model can be conceptually complex for teams new to table versioning concepts. Tightly coupled to Iceberg and Delta Lake — not applicable to raw file versioning use cases. Requires Spark or Iceberg configuration changes for all pipeline jobs that access versioned tables

Ilum vs Project Nessie: Key Differences for Python Data Engineering

Data Lake Management

Ilum

Data Lakehouse Platform

★ 3.9

Commercial

N/A — web application

Project Nessie

Transactional Data Lake Catalog

★ 4.3

Apache-2.0

pip install pynessie

Side-by-Side Comparison

Ilum

Project Nessie

Ilum

Project Nessie

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

✓Multi-table ACID transactions and Git-like versioning for Apache Iceberg and Delta Lake tables
✓Teams building open lakehouse architectures needing table-level time-travel and branching
✓Catalog layer for Apache Iceberg that adds versioning without requiring a storage proxy

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

✓Multi-table ACID transactions and Git-like versioning for Apache Iceberg and Delta Lake tables
✓Teams building open lakehouse architectures needing table-level time-travel and branching
✓Catalog layer for Apache Iceberg that adds versioning without requiring a storage proxy

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

•Branching model can be conceptually complex for teams new to table versioning concepts
•Tightly coupled to Iceberg and Delta Lake — not applicable to raw file versioning use cases
•Requires Spark or Iceberg configuration changes for all pipeline jobs that access versioned tables

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

•Branching model can be conceptually complex for teams new to table versioning concepts
•Tightly coupled to Iceberg and Delta Lake — not applicable to raw file versioning use cases
•Requires Spark or Iceberg configuration changes for all pipeline jobs that access versioned tables

License

Commercial

Apache-2.0

License

Commercial

Apache-2.0

Install

N/A — web application

pip install pynessie

Install

N/A — web application

pip install pynessie

Rating

★ 3.9

★ 4.3

Rating

★ 3.9

★ 4.3

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

Project Nessie

1Git-inspired catalog for Iceberg and Delta Lake tables on object storage
2Branching and tagging enables safe multi-table schema experiments
3ACID commits group changes to multiple Iceberg tables into one atomic transaction
4SQL catalog integration: Spark, Flink, and Dremio read Nessie as a catalog
5REST API and Python client for programmatic catalog management

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.

Project Nessie

Python data engineers configure PySpark to use Project Nessie as the Iceberg catalog — enabling table branching within Spark jobs. An engineer creates a Nessie branch, runs a PySpark transformation that modifies multiple Iceberg tables, validates the results, then merges the branch to main — providing atomic multi-table updates with full rollback capability.

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Individual Tool Pages

View Ilum details →View Project Nessie details →

Side-by-Side Comparison

Ilum

Project Nessie

Ilum

Project Nessie

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

✓Multi-table ACID transactions and Git-like versioning for Apache Iceberg and Delta Lake tables
✓Teams building open lakehouse architectures needing table-level time-travel and branching
✓Catalog layer for Apache Iceberg that adds versioning without requiring a storage proxy

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

✓Multi-table ACID transactions and Git-like versioning for Apache Iceberg and Delta Lake tables
✓Teams building open lakehouse architectures needing table-level time-travel and branching
✓Catalog layer for Apache Iceberg that adds versioning without requiring a storage proxy

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

•Branching model can be conceptually complex for teams new to table versioning concepts
•Tightly coupled to Iceberg and Delta Lake — not applicable to raw file versioning use cases
•Requires Spark or Iceberg configuration changes for all pipeline jobs that access versioned tables

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

•Branching model can be conceptually complex for teams new to table versioning concepts
•Tightly coupled to Iceberg and Delta Lake — not applicable to raw file versioning use cases
•Requires Spark or Iceberg configuration changes for all pipeline jobs that access versioned tables

License

Commercial

Apache-2.0

License

Commercial

Apache-2.0

Install

N/A — web application

pip install pynessie

Install

N/A — web application

pip install pynessie

Rating

★ 3.9

★ 4.3

Rating

★ 3.9

★ 4.3

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

Project Nessie

1Git-inspired catalog for Iceberg and Delta Lake tables on object storage
2Branching and tagging enables safe multi-table schema experiments
3ACID commits group changes to multiple Iceberg tables into one atomic transaction
4SQL catalog integration: Spark, Flink, and Dremio read Nessie as a catalog
5REST API and Python client for programmatic catalog management

How Python Data Engineers Use These Tools