BEGINNER • SQL Fundamentals
Sprint: reduce pipeline latency #2
This lesson focuses on reduce pipeline latency for a user behavior tracking environment. You will use: CREATE TABLE events (id SERIAL PRIMARY KEY) | python -m venv venv | python etl_script.py. The content is designed for practical data engineering execution.
Code Example
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("user behavior tracking").getOrCreate()
df = spark.read.parquet("s3://bucket/raw/")
df.filter("event_type = 'purchase'").write.mode("overwrite").parquet("s3://bucket/processed/")
# Objective: reduce pipeline latencyCommands & References
- CREATE TABLE events (id SERIAL PRIMARY KEY)
- python -m venv venv
- python etl_script.py
Lab Steps
- Prepare environment with: CREATE TABLE events (id SERIAL PRIMARY KEY)
- 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.