---

name: data-catalog-and-config description: "Configure, inspect, validate, and troubleshoot Kedro DataCatalog datasets credentials versioning and OmegaConfigLoader behavior." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data Catalog And Config

Use this sub-skill when the task is about Kedro data catalog entries, dataset classes, credentials, versioning, catalog factories, parameters, globals, or OmegaConfigLoader merge behavior.

Route First

• Read references/api-reference.md when you need the current DataCatalog, dataset, CatalogConfigResolver, MemoryDataset, CachedDataset, SharedMemoryDataset, or versioning APIs.
• Read references/configuration-and-catalogs.md when you need catalog.yml, credentials.yml, parameters.yml, globals.yml, config environments, runtime params, factories, or merge strategies.
• Read references/troubleshooting.md when a catalog/config import, validation, interpolation, credentials, optional dependency, versioning, or merge error appears.
• Run scripts/validate_catalog_config.py to validate a standalone catalog YAML and optional credentials YAML without printing credential values.

Boundary Rules

• Stay here for DataCatalog, AbstractDataset, AbstractVersionedDataset, MemoryDataset, CachedDataset, SharedMemoryDataset, credentials injection, dataset type resolution, versioning, load/save versions, catalog factories, lazy datasets, OmegaConfigLoader, config_patterns, runtime_params, globals, and merge strategy.
• Route pipeline graph construction, node inputs/outputs, namespaces, tags, and duplicate graph outputs to ../pipelines-and-nodes/SKILL.md.
• Route kedro run, session flags, project command setup, project discovery, and CLI command construction to ../project-cli-and-sessions/SKILL.md or ../runners-and-execution/SKILL.md.
• Route hook-based catalog mutation, external credentials hooks, custom datasets as extension design, and plugin behavior to ../hooks-and-extensions/SKILL.md.
• Return to the root router at ../../SKILL.md if the request spans multiple Kedro workflow families.

Fast Workflow

1. Identify whether the user has raw dictionaries, standalone YAML files, or a full Kedro project configuration tree.
2. For raw dictionaries or standalone YAML, validate structure with DataCatalog.from_config(catalog, credentials) before running any pipeline.
3. For a config tree, load through OmegaConfigLoader(conf_source=..., env=..., runtime_params=..., base_env="base", default_run_env="local"), then pass conf_loader["catalog"] and redacted conf_loader.get("credentials", {}) to DataCatalog.from_config().
4. For factories, inspect catalog.config_resolver.list_patterns() and use resolve_pattern(dataset_name) to explain how a dataset name is matched.
5. For optional dataset types such as pandas.CSVDataset, check that kedro-datasets and the relevant extras are installed; core Kedro 1.4.0 does not bundle most concrete dataset implementations.
6. For any kedro command used during validation, set KEDRO_DISABLE_TELEMETRY=1 or DO_NOT_TRACK=1 when a no-telemetry run is required.