kedro_datasets.spark.DeltaTableDataset

class kedro_datasets.spark.DeltaTableDataset(*, filepath, metadata=None)

DeltaTableDataset loads data into DeltaTable objects.

Example usage for the YAML API:

weather@spark:
  type: spark.SparkDataset
  filepath: data/02_intermediate/data.parquet
  file_format: "delta"

weather@delta:
  type: spark.DeltaTableDataset
  filepath: data/02_intermediate/data.parquet

Example usage for the Python API:

 from delta import DeltaTable
 from kedro_datasets.spark import DeltaTableDataset, SparkDataset
 from pyspark.sql import SparkSession
 from pyspark.sql.types import StructField, StringType, IntegerType, StructType

 schema = StructType(
...     [StructField("name", StringType(), True), StructField("age", IntegerType(), True)]
... )

 data = [("Alex", 31), ("Bob", 12), ("Clarke", 65), ("Dave", 29)]

 spark_df = SparkSession.builder.getOrCreate().createDataFrame(data, schema)
 filepath = (tmp_path / "test_data").as_posix()
 dataset = SparkDataset(filepath=filepath, file_format="delta")
 dataset.save(spark_df)
 deltatable_dataset = DeltaTableDataset(filepath=filepath)
 delta_table = deltatable_dataset.load()

 assert isinstance(delta_table, DeltaTable)

Methods

exists()	Checks whether a dataset's output already exists by calling the provided _exists() method.
from_config(name, config[, load_version, ...])	Create a dataset instance using the configuration provided.
load()	Loads data by delegation to the provided load method.
release()	Release any cached data.
save(data)	Saves data by delegation to the provided save method.
to_config()	Converts the dataset instance into a dictionary-based configuration for serialization.

__init__(*, filepath, metadata=None)

Creates a new instance of DeltaTableDataset.

Parameters:

• filepath (str) – Filepath in POSIX format to a Spark dataframe. When using Databricks and working with data written to mount path points, specify filepath``s for (versioned) ``SparkDataset``s starting with ``/dbfs/mnt.

• metadata (Optional[dict[str, Any]]) – Any arbitrary metadata. This is ignored by Kedro, but may be consumed by users or external plugins.

exists()

Checks whether a dataset’s output already exists by calling the provided _exists() method.

Return type:

bool

Returns:

Flag indicating whether the output already exists.

Raises:

DatasetError – when underlying exists method raises error.

classmethod from_config(name, config, load_version=None, save_version=None)

Create a dataset instance using the configuration provided.

Parameters:

• name (str) – Data set name.

• config (dict[str, Any]) – Data set config dictionary.

• load_version (Optional[str]) – Version string to be used for load operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

• save_version (Optional[str]) – Version string to be used for save operation if the dataset is versioned. Has no effect on the dataset if versioning was not enabled.

Return type:

AbstractDataset

Returns:

An instance of an AbstractDataset subclass.

Raises:

DatasetError – When the function fails to create the dataset from its config.

load()

Loads data by delegation to the provided load method.

Return type:

DeltaTable

Returns:

Data returned by the provided load method.

Raises:

DatasetError – When underlying load method raises error.

release()

Release any cached data.

Raises:

DatasetError – when underlying release method raises error.

Return type:

None

save(data)

Saves data by delegation to the provided save method.

Parameters:

data (None) – the value to be saved by provided save method.

Raises:

• DatasetError – when underlying save method raises error.

• FileNotFoundError – when save method got file instead of dir, on Windows.

• NotADirectoryError – when save method got file instead of dir, on Unix.

Return type:

NoReturn

to_config()

Converts the dataset instance into a dictionary-based configuration for serialization. Ensures that any subclass-specific details are handled, with additional logic for versioning and caching implemented for CachedDataset.

Adds a key for the dataset’s type using its module and class name and includes the initialization arguments.

For CachedDataset it extracts the underlying dataset’s configuration, handles the versioned flag and removes unnecessary metadata. It also ensures the embedded dataset’s configuration is appropriately flattened or transformed.

If the dataset has a version key, it sets the versioned flag in the configuration.

Removes the metadata key from the configuration if present.

Return type:

dict[str, Any]

Returns:

A dictionary containing the dataset’s type and initialization arguments.