BEGINNER • SQL Fundamentals
Sprint: improve data quality #17
This lesson focuses on improve data quality for a e-commerce analytics environment. You will use: pip install pandas sqlalchemy | SELECT * FROM users LIMIT 10 | INSERT INTO logs VALUES (...). The content is designed for practical data engineering execution.
Code Example
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("e-commerce analytics").getOrCreate()
df = spark.read.parquet("s3://bucket/raw/")
df.filter("event_type = 'purchase'").write.mode("overwrite").parquet("s3://bucket/processed/")
# Objective: improve data qualityCommands & References
- pip install pandas sqlalchemy
- SELECT * FROM users LIMIT 10
- INSERT INTO logs VALUES (...)
Lab Steps
- Prepare environment with: pip install pandas sqlalchemy
- Design or modify the data pipeline for the scenario.
- Validate data quality and document lineage.
- Propose one optimization for production.
Exercises
- Add one data quality check.
- Implement one incremental loading pattern.
- Write a rollback procedure for this pipeline.