data_lakehouse_ingest

End-to-end ingestion framework for loading data into the BER Data Lakehouse using pyspark and minio.

It reads flat files (CSV, TSV, JSON, XML) from MinIO (S3-compatible), applies optional schema-based casting using SQL-style schema definitions, and writes curated Delta tables to a Spark-based Lakehouse.

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Environment and Python management

This package uses uv for Python environment and package management. See the installation instructions to set up uv on your system.

The package requires Python 3.13+. uv will download and manage Python automatically if your system Python is older.

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Installation

To install dependencies (including Python if necessary), run:

uv sync --dev

This will create a project-local virtual environment at .venv/.

Activate the environment:

uv venv
source .venv/bin/activate

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Usage

The following example demonstrates a full ingestion workflow using Spark and MinIO.

# Option 1: Load config from MinIO
cfg_path = "s3a://cdm-lake/tenant-general-warehouse/kbase/datasets/pangenome_ke-source/config-json/pangenome_ke_genome.json"

# Option 2: Inline JSON config
cfg_path = r'''
{
  "tenant": "kbase",
  "dataset": "ke_pangenome",
  "paths": {
    "data_plane": "s3a://cdm-lake/",
    "bronze_base": "s3a://cdm-lake/tenant-general-warehouse/kbase/datasets/pangenome_ke-source/bronze/",
  },
  "defaults": {
    "tsv": { "header": true, "delimiter": "\t", "inferSchema": false }
  },
  "tables": [
    {
      "name": "genome",
      "enabled": true,
      "schema_sql": "genome_id STRING, gtdb_species_clade_id STRING, gtdb_taxonomy_id STRING, ncbi_biosample_id STRING, fna_file_path_nersc STRING, faa_file_path_nersc STRING",
      "partition_by": null,
      "bronze_path": "s3a://cdm-lake/tenant-general-warehouse/kbase/datasets/pangenome_ke-source/bronze/tsv_files/table_genome_V1.1.tsv"
    }
  ]
}
'''

# Run ingestion
report = ingest(
    cfg_path,
)

print(report)

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How it works

1. ConfigLoader reads the JSON config (inline or from MinIO).
2. Schema Parsing: Determines schema from schema_sql.
3. Spark Ingestion: Reads files from the bronze layer using spark.read.
4. Validation: Applies schema casting and structural validation.
5. Delta Write: Writes curated data to the silver layer.
6. Logging: All activities are logged with contextual metadata (pipeline, schema, table).

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Supported schema_sql Data Types

The schema_sql field uses Spark SQL–compatible primitive data types for defining table schemas. Only the following data types are currently supported by the ingestion framework:

STRING
INT
INTEGER
BIGINT
LONG
DOUBLE
FLOAT
BOOLEAN
DATE
TIMESTAMP

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Jupyter Notebook integration

If you are using Jupyter in the same container:

1. Install ipykernel into the uv-managed .venv:

   uv pip install ipykernel

2. Register the environment as a Jupyter kernel:

   uv run python -m ipykernel install --user --name=data-lakehouse-ingest --display-name "Python (data-lakehouse-ingest)"

3. In Jupyter Notebook, select the kernel Python (data-lakehouse-ingest).

Now you can use:

from data_lakehouse_ingest import ingest

directly inside notebooks.

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Tests

To run tests:

uv run pytest tests/

To generate coverage:

uv run pytest --cov=src --cov-report=xml tests/

The coverage.xml file can be used in CI pipelines.

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Linting

This project uses Ruff for linting and formatting. To run checks:

uv run ruff check src tests

To auto-fix:

uv run ruff check --fix src tests

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