spark-data-quality

Data Quality validation library for Kubernetes Spark pods. Runs Great Expectations and Trino SQL checks against any table, then persists results to a DQ Engine API.

Installation

pip install spark-data-quality

Usage

from spark_dq.quality import SparkDQAgent

agent = SparkDQAgent(
    catalog="my_catalog",
    schema="my_schema",
    table="my_table",
    data_quality_url="http://dq-engine:8000/api/v1/spark",
    trino_host="trino:443",
    trino_user="user",
    trino_pwd="pwd",
)

cfg = agent.fetch_table_config()
results = agent.execute_data_quality(df)

`fetch_table_config()`

Fetches the table's configuration from the DQ Engine, including the SQL query to load data, active test suites with their expectations, and scan limits. The config is cached after the first call.

`execute_data_quality(df)`

Runs all test suites against the provided Spark DataFrame. Expectations are split into two concurrent paths:

Trino fast-path — null checks, uniqueness, range, regex via a single SQL query
GE slow-path — all other expectation types via parallel Great Expectations validators

Suites run in parallel (up to 8 concurrently). Results are saved to the DQ Engine and per-suite statistics are returned.

If Trino credentials are not provided, all expectations run through Great Expectations only.

License

MIT

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