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easyfabric.load_data_bronze

run​

def run(tablefile: str,
        config_manager: ConfigManager = None,
        overrides: LoadOverrides = None) -> str | None

Runs the bronze loader process for a specified table configuration and pulls files from the source, processes them, and loads them into the bronze layer.

This function initializes the table configuration based on the specified table file and checks its active state for the bronze process. If the table's configuration specifies pre-processing or post-processing notebooks, they are executed accordingly. Files from the source are pulled, processed, and loaded into the bronze layer based on their specified file types. Supported file types include CSV, JSON, XML, Parquet, and Notebook. The function also handles exceptions and ensures logs are saved correctly.

Arguments:

  • tablefile str - Path to the YAMLA simple way to write configurations. It's basically a list that computers can read easily. file representing a table's configuration.
  • config_manager ConfigManager - An instance of ConfigManager used for accessing the application's configuration settings.
  • overrides LoadOverrides - Optional per-run overrides that flip default validation/gating behaviour for this call only — skip notebook hooks, skip the stale-file check, or force a reload of unchanged source. Defaults to no overrides (use YAML / ConfigManager defaults).

Returns:

  • str - A message indicating the outcome of the loading process, such as the number of files loaded or an error message in case of failure.

Raises:

  • Exception - If the config_manager is not initialized, no active configuration can be found for the table, or the filetype is unsupported.

dataframeloader​

def dataframeloader(data_frame: DataFrame,
                    load_config: LoadConfig,
                    table_config: TableConfig,
                    config_manager: ConfigManager = None) -> str | None

Loads a DataFrame into a specified data platform table using the provided configuration and manager.

This function handles the loading operation by using detailed configurations for the DataFrame, table, and the application configuration manager. It sets up logging, ensures required parameters are initialized, and supports specific settings for different layers (e.g., bronze layer). The function handles exception logging and provides mechanisms to stop processing upon encountering errors based on configuration settings.

Arguments:

  • data_frame DataFrame - The data to be loaded into the specified table.
  • load_config LoadConfig - Contains configuration for the loading process, including destination table.
  • table_config TableConfig - Holds table-specific settings, e.g., table name identifiers and layers.
  • config_manager ConfigManager - Manages and validates application-level configurations.

Returns:

  • str - Message indicating the result of the DataFrame loading process, including the target table name and error details if applicable.

Raises:

  • Exception - If the destination table name is missing from LoadConfig.
  • Exception - If the ConfigManager is not properly initialized.

cleanup_bronze_files​

def cleanup_bronze_files(config_manager: ConfigManager = None) -> dict

Cleans up old files and partitions in the Bronze LakehouseA place where you store both "raw" data (like files) and "organized" data (like tables). It combines the best of a File Cabinet and a Database. depending on the configuration settings on the Table, Connection, or global Lakehouse level.

Arguments:

  • config_manager ConfigManager - An instance of ConfigManager.

Returns:

  • dict - A dictionary containing metrics of the cleanup: deleted_files, bytes_deleted, and skipped_objects.