kedro-org/kedro 1.5.0 on GitHub

Major features and improvements

Added a new HTTP Server layer which uses the KedroServiceSession to execute pipelines from HTTP requests. It offers the following endpoints:
- /run to run pipelines with optional runtime parameters.
- /health to check the server's health status.
- /snapshot to retrieve the current project snapshot.
Added a new CLI command kedro server start to run the server.
Added selective pipeline loading: when --pipelines is specified, only the requested pipeline modules are imported.

Fixed Rich logging integration so node input/output brackets render correctly in console logs and dataset colour markup does not leak into plain log handlers.
Improved the AbstractDataset.from_config() error message for custom dataset classes that are still abstract, so it no longer suggests invalid constructor arguments when required dataset methods are missing.
Fixed kedro new accepting project names whose derived package name shadows a Python standard library module or is a Python keyword (e.g. email, json, import), which silently produced a broken, unimportable project. Such names are now rejected at creation time with a clear message.
Fixed kedro pipeline create accepting Python keywords (e.g. for, import, return) as pipeline names. Such names are now rejected at creation time with a clear error message.

Documented hooks limitation when using ParallelRunner.
Updated the Amazon EMR Serverless deployment guide with a strategy-first layout, custom container images, Spark catalog configuration, and troubleshooting for common deployment issues.
Updated the AWS Step Functions deployment guide with a strategy-first layout, pipeline-level namespace grouping, Lambda container deployment, and an end-to-end Spaceflights walkthrough.
Updated the AWS Batch deployment guide with a strategy-first layout, custom AWSBatchRunner usage, namespace-based job submission, and S3-backed catalog configuration.

Many thanks to the following Kedroids for contributing PRs to this release: