open-sparkdq 0.1.10

Plug-and-play Data Quality + Unit Testing for PySpark (batch & streaming) with YAML config, profiling, and optional OpenTelemetry hooks.

open-spark-dlh-dq

Open source Plug-and-play Data Quality for Apache Spark (Batch + Streaming) with YAML checks, profiling, and OpenTelemetry.

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📌 Project Overview

open-spark-dlh-dq is an open-source Python library providing a Data Quality (DQ) framework for Apache Spark.

It supports:

• ✅ Batch & Streaming DQ with declarative YAML suites
• ✅ Custom checks via Python (dq_check, unit_test)
• ✅ CLI execution for datasets in directories or Spark DataFrames
• ✅ Inline checks in PySpark scripts
• ✅ Format support: Parquet, CSV, Iceberg, Delta, JSON, ORC
• ✅ Profiler & OpenTelemetry for observability

Built on PySpark, PyDeequ, and Chispa, this library enables robust data validation pipelines.

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✅ Features

• Batch DQ: Validate static datasets using YAML or inline rules.
• Streaming DQ: Apply checks on micro-batches via foreachBatch.
• Custom Checks: Extend with Python functions in user_checks/.
• CLI Tool: Run suites via sparkdq run --yaml <suite.yml>.
• Profiler: Generate summary stats and quantiles.
• OpenTelemetry: Capture spans and traces for test cases.

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📂 Repository Structure

open-spark-dlh-dq/
├─ pyproject.toml
├─ README.md
├─ LICENSE
│
├─ sparkdq/
│  ├─ cli/main.py                          # CLI entry point
│  ├─ config/                              # YAML loader, env vars, schema binding
│  ├─ core/                                # Models, registry, Spark session, runner
│  │  └─ validators/                       # Built-in + custom validator classes
│  ├─ profiling/profiler.py                # Profiling utilities
│  ├─ resources/open_spark_dlh_dq.yml      # Default YAML suite
│  ├─ observability/otel.py                # OpenTelemetry integration
│  └─ integrations/streaming.py            # foreachBatch wrapper
│
├─ user_checks/                            # User-defined checks
│  └─ example_checks.py
│
├─ examples/                               # Usage examples
│  ├─ suites/orders_dq.yml
│  ├─ batch_example.py
│  └─ streaming_example.py
│
└─ tests/                                  # Unit tests
   ├─ test_yaml_loader.py
   ├─ test_chispa_integration.py
   ├─ test_pydeequ_integration.py
   ├─ test_runner.py
   ├─ test_validators.py
   ├─ test_validator_contracts.py
   └─ test_cli.py

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🛠 Usage

Run CLI with YAML suite

sparkdq run --yaml ./sparkdq/resources/open_spark_dlh_dq.yml --suite-name orders_dq --format text

Inline checks in PySpark

from sparkdq.core.runner import run_suite
from sparkdq.config.loader import load_yaml_suite

suite = load_yaml_suite("./sparkdq/resources/open_spark_dlh_dq.yml")
df = spark.read.parquet("./data/orders")
run_suite(df, suite)

Streaming example

python examples/streaming_example.py

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🧩 Custom Checks

Add Python methods in user_checks/example_checks.py:

from sparkdq.core.registry import dq_check, unit_test

@dq_check("amount_positive")
def amount_positive(df):
    return df.filter(df.amount > 0).count() == df.count()

Reference them in YAML:

test_cases:
  - name: amount_positive
    type: dq_check

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📊 Profiler & OpenTelemetry

Enable profiling and observability in your pipeline:

from sparkdq.profiling.profiler import profile_df
profile_df(df)

OpenTelemetry spans can be enabled via sparkdq/observability/otel.py.

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🔨 Build & Publish

Build for PyPI (Windows)

./build.ps1

Build for PyPI (Linux)

./build.sh

Example Repository to understand how to use

https://github.com/aashish72it/spark-test