EEADataLakehouse

Two main areas of concern and class domains for the EEA data lakehouse:

• eea_datalakehouse.data_preparation — data acquisition, vocabulary acquisition, data exploration, and transformation to parquet. Each stage is an abstract base class; concrete per-dataset pipelines subclass them since the actual logic varies by dataset and data flow.
• eea_datalakehouse.dds_ingestion — notebook-side client for the Dremio Document Service (DDS) Ingest API (DI-8.4/8.5). FolderIngest transfers a folder of data files to S3 (via DDS-issued presigned URLs only — never an S3 SDK or S3 credentials) and registers it as a Dremio table, coordinated entirely through the DDS REST API.

Install

Install directly from GitHub, pinned to a released tag:

pip install "git+https://github.com/eeadata/[email protected]"

Usage

from pathlib import Path

from eea_datalakehouse.data_preparation import DataValidationError, ParquetTransformer


class MyDatasetTransformer(ParquetTransformer):
    def to_parquet(self, records, destination: Path) -> Path:
        ...  # dataset-specific parquet write


transformer = MyDatasetTransformer(required_fields=["id", "value"])

raw_source = [{"id": 1, "value": "a"}, {"id": 2, "value": "b"}]
records = transformer.prepare(raw_source)

try:
    transformer.validate(records)
except DataValidationError as exc:
    print(f"Invalid data: {exc}")

Dremio credentials are read from the injected kernel env vars _DREMIO_USER / _DREMIO_PWD, and the service URL from DDS_BASE_URL. None of these are ever logged or printed.

from eea_datalakehouse.dds_ingestion import FolderIngest

outcome = FolderIngest(
    folder="./my_data",
    target_catalog_path="biodiversity.uploads",
    data_format="parquet",      # one of parquet | csv | json
    intent="read_only",         # or "editable"
    conflict_mode="fail",
    parallelism=4,               # concurrent uploads (default 4)
).run()

print(outcome.commit.table_path, outcome.commit.record_count)

run() performs begin → upload(all files) → commit. Re-running the same session resumes by skipping files the server reports as already uploaded.

• prepare(source) normalizes an iterable of raw records into a list of plain dicts.
• validate(records) checks that every record contains the configured required_fields, raising DataValidationError on the first invalid record.
• to_parquet(records, destination) is where a concrete subclass writes its own dataset's schema out to parquet.

Layout

Path	Purpose
data_preparation/acquisition.py	DataAcquirer — fetch a dataset's raw source data
data_preparation/vocabulary.py	VocabularyLoader — load the controlled vocabulary to validate against
data_preparation/exploration.py	Explorer — inspect acquired data before transformation
data_preparation/transformation.py	ParquetTransformer — prepare/validate/write to parquet
dds_ingestion/credentials.py	env-var creds + redacted DremioCreds
dds_ingestion/models.py	typed request/response models for the DDS ingest contract
dds_ingestion/client.py	thin, unit-testable HTTP client (IngestClient)
dds_ingestion/progress.py	tqdm progress bar with graceful fallback
dds_ingestion/folder.py	FolderIngest orchestration (scan/parallel/resume)

Development

pip install -e ".[dev]"
pytest

Releasing a new version

1. Update version in pyproject.toml (e.g. 0.2.0).

2. Commit and push to main.

3. The Release GitHub Actions workflow runs on every push to main: it reads the version from pyproject.toml, builds the package, and publishes a v0.2.0 GitHub Release (creating the matching tag automatically), with notes auto-generated from merged PRs since the previous release (categorized via .github/release.yml).

   Pushing to main without bumping the version re-publishes the release for the current version with the latest build artifacts.

Install in JupyterLab

Run this in a notebook cell, pinned to the release tag you want:

%pip install "git+https://github.com/eeadata/[email protected]"

Use the %pip magic rather than !pip — it installs into the kernel the notebook is actually running on, instead of whatever pip happens to be first on PATH. After installing, restart the kernel (Kernel > Restart Kernel...) so the import below picks up the newly installed package:

from eea_datalakehouse.data_preparation import ParquetTransformer

Alternatively, download the wheel attached to the GitHub Release page for that tag and install the local file instead of pulling from git:

%pip install /path/to/EEADataLakehouse-0.1.0-py3-none-any.whl