> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Geneva Jobs

> Learn about Geneva's job execution framework for distributed feature engineering workflows.

Geneva provides a comprehensive job execution framework for distributed feature engineering workflows. This section covers the different types of jobs and execution contexts available in Geneva.

## Job Types

### [Backfilling](/geneva/jobs/backfilling/)

Trigger distributed jobs to populate column values in your LanceDB table using UDFs. Learn about filtered backfills and incremental updates.

### [Materialized Views](/geneva/jobs/materialized-views/)

Create declarative materialized views to manage batch updates of expensive operations. Optimize data layouts for training and simplify orchestration.

### [Startup Optimizations](/geneva/jobs/startup/)

Optimize job and session startup times for faster interactive development and production workflows. Learn about caching, pre-warming, and performance tuning.

## Execution Contexts

### [Advanced Execution Contexts](/geneva/jobs/contexts/)

Understand how Geneva automatically packages and deploys your Python execution environment to worker nodes for distributed execution.

### [Geneva Console](/geneva/jobs/console/)

Set up and access the Geneva Console for monitoring and managing Geneva jobs, clusters, and execution contexts.

## Key Features

* **Distributed Processing**: Scale feature computation across multiple nodes
* **Checkpointing**: Resume jobs from failures without losing progress
* **Incremental Updates**: Only process new or modified data
* **Distributed Execution**: Run jobs on Kubernetes or standalone compute clusters
* **Environment Management**: Automatic dependency packaging and deployment

## Getting Started

1. **Choose your execution context** based on your infrastructure
2. **Define your UDFs** for feature computation
3. **Trigger backfill operations** to populate your data
4. **Monitor performance** and optimize based on usage patterns

For detailed information about each job type and execution context, explore the documentation in this section.

## API Reference

* [Table](https://lancedb.github.io/geneva/api/table/) — `backfill()`, `backfill_async()`, `refresh()`, `plan_backfill()`, `plan_refresh()`, and other job-triggering methods
* [Connection](https://lancedb.github.io/geneva/api/connection/) — `get_job()`, `list_jobs()`, `context()`, and `local_ray_context()`
* [Cluster](https://lancedb.github.io/geneva/api/cluster/) — configure KubeRay and local Ray execution backends
* [Manifest](https://lancedb.github.io/geneva/api/manifest/) — package Python environments for remote workers