BEGINNER • SQL Fundamentals
Sprint: increase data discoverability #27
This lesson focuses on increase data discoverability for a financial reporting environment. You will use: INSERT INTO logs VALUES (...) | pip install pandas sqlalchemy | SELECT * FROM users LIMIT 10. The content is designed for practical data engineering execution.
Code Example
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("financial reporting").getOrCreate()
df = spark.read.parquet("s3://bucket/raw/")
df.filter("event_type = 'purchase'").write.mode("overwrite").parquet("s3://bucket/processed/")
# Objective: increase data discoverabilityCommands & References
- INSERT INTO logs VALUES (...)
- pip install pandas sqlalchemy
- SELECT * FROM users LIMIT 10
Lab Steps
- Prepare environment with: INSERT INTO logs VALUES (...)
- 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.