Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
Check Null Rate Threshold in PySpark DataFrame
This PySpark code checks the null rate of specified DataFrame columns against a threshold and returns violations.
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, count
def check_null_rate(df, threshold=0.2, columns=None):
"""
Check null rate for specified columns (or all) against a threshold.
Returns columns that exceed the threshold.
"""
cols = columns or df.columns
total…How to Validate Fact Table Grain Row Counts in Python
Validate fact table grain by checking dimension key references, unique grain combinations, duplicate rows, and dimension cardinality from a CSV file.
import csv
import hashlib
from pathlib import Path
def validate_fact_grain(fact_file: Path, expected_dim_keys: dict[str, set[str]]) -> dict:
"""
Validate fact table grain by checking each row's dimension keys exist
in expected dimension tables and row count consistency.
"""
dim_references = {}
…Browse by section
Each section groups closely related Python snippets.
Data pipelines & processing — Python code examples
What you will find here
This page collects data pipelines & processing snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.