《PaddlePaddle从入门到实战》源码

Raw data in. Hallmarked golden records out. ASSAY → UNEARTH → REFINE → UNFURL → MARK

Data Quality Observation of Data Vault layer

The script reads the dataset along the path and selects the columns in it received from the argument for the specified dates. Then it saves the report to the specified path of HDFS.

Clean, modular DQN in PyTorch with Double/Dueling options and MLP/CNN/LSTM backbones—plug-and-play for Gymnasium environments.

This project defines five data quality checks for the E-Commerce dataset to ensure reliability for analytics and reporting. Each check includes dimension classification, SQL logic, and severity level.

Polars data quality extension

pyspark-dq is a light weight metadata based data quality checker utility based in pyspark.

Dragon Quest Core Module, created in Rust.

Windows-first PySpark batch pipeline: ingest raw → bronze Parquet, run DQ checks, publish curated silver. PowerShell wrapper adds Spark hygiene, parallelism controls, and step logs.

Java program Practice

A fully‑modelled, Snowflake‑backed analytics pipeline for operational appointment intelligence across CareGrid clinics. This project builds a trustworthy, chain‑resolved no‑show metric, a canonical status model, and a data‑quality governance layer that surfaces scheduling inconsistencies instead of hiding them.