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# Introduction

As a data engineer, you’re probably responsible (at least in part) for your organization’s data infrastructure. You build the pipelines, maintain the databases, ensure data flows smoothly, and troubleshoot when things inevitably break. But here’s the thing: how much of your day goes into manually checking pipeline health, validating data loads, or monitoring system performance?

If you’re honest, it’s probably a massive chunk of your time. Data engineers spend many hours in their workday on operational tasks — monitoring jobs, validating schemas, tracking data lineage, and responding to alerts — when they could be architecting better systems.

This article covers five Python scripts specifically designed to tackle the repetitive infrastructure and operational tasks that consume your valuable engineering time.

🔗 Link to the code on GitHub

# 1. Pipeline Health Monitor

The pain point: You have dozens of ETL jobs running across different schedules. Some run hourly, others daily or weekly. Checking if they all completed successfully means logging into various systems, querying logs, checking timestamps, and piecing together what’s actually happening. By the time you realize a job failed, downstream processes are already broken.

What the script does: Monitors all your data pipelines in one place, tracks execution status, alerts on failures or delays, and maintains a historical log of job performance. Provides a consolidated health dashboard showing what’s running, what failed, and what’s taking longer than expected.

How it works: The script connects to your job orchestration system (like Airflow, or reads from log files), extracts execution metadata, compares against expected schedules and runtimes, and flags anomalies. It calculates success rates, average runtimes, and identifies patterns in failures. Can send alerts via email or Slack when issues are detected.

⏩ Get the Pipeline Health Monitor Script

# 2. Schema Validator and Change Detector

The pain point: Your upstream data sources change without warning. A column gets renamed, a data type changes, or a new required field appears. Your pipeline breaks, downstream reports fail, and you’re probably struggling to figure out what changed and where. Schema drift is a very relevant problem in data pipelines.

What the script does: Automatically compares current table schemas against baseline definitions, detects any changes in column names, data types, constraints, or structures. Generates detailed change reports and can enforce schema contracts to prevent breaking changes from propagating through your system.

How it works: The script reads schema definitions from databases or data files, compares them against stored baseline schemas (stored as JSON), identifies additions, deletions, and modifications, and logs all changes with timestamps. It can validate incoming data against expected schemas before processing and reject data that doesn’t conform.

⏩ Get the Schema Validator Script

# 3. Data Lineage Tracker

The pain point: Someone asks “Where does this field come from?” or “What happens if we change this source table?” and you have no good answer. You dig through SQL scripts, ETL code, and documentation (if it exists) trying to trace data flow. Understanding dependencies and impact analysis takes hours or days instead of minutes.

What the script does: Automatically maps data lineage by parsing SQL queries, ETL scripts, and transformation logic. Shows you the complete path from source systems to final tables, including all transformations applied. Generates visual dependency graphs and impact analysis reports.

How it works: The script uses SQL parsing libraries to extract table and column references from queries, builds a directed graph of data dependencies, tracks transformation logic applied at each stage, and visualizes the complete lineage. It can perform impact analysis showing what downstream objects are affected by changes to any given source.

⏩ Get the Data Lineage Tracker Script

# 4. Database Performance Analyzer

The pain point: Queries are running slower than usual. Your tables are getting bloated. Indexes might be missing or unused. You suspect performance issues but identifying the root cause means manually running diagnostics, analyzing query plans, checking table statistics, and interpreting cryptic metrics. It’s time-consuming work.

What the script does: Automatically analyzes database performance by identifying slow queries, missing indexes, table bloat, unused indexes, and suboptimal configurations. Generates actionable recommendations with estimated performance impact and provides the exact SQL needed to implement fixes.

How it works: The script queries database system catalogs and performance views (pg_stats for PostgreSQL, information_schema for MySQL, etc.), analyzes query execution statistics, identifies tables with high sequential scan ratios indicating missing indexes, detects bloated tables that need maintenance, and generates optimization recommendations ranked by potential impact.

⏩ Get the Database Performance Analyzer Script

# 5. Data Quality Assertion Framework

The pain point: You need to ensure data quality across your pipelines. Are row counts what you expect? Are there unexpected nulls? Do foreign key relationships hold? You write these checks manually for each table, scattered across scripts, with no consistent framework or reporting. When checks fail, you get vague errors without context.

What the script does: Provides a framework for defining data quality assertions as code: row count thresholds, uniqueness constraints, referential integrity, value ranges, and custom business rules. Runs all assertions automatically, generates detailed failure reports with context, and integrates with your pipeline orchestration to fail jobs when quality checks don’t pass.

How it works: The script uses a declarative assertion syntax where you define quality rules in simple Python or YAML. It executes all assertions against your data, collects results with detailed failure information (which rows failed, what values were invalid), generates comprehensive reports, and can be integrated into pipeline DAGs to act as quality gates preventing bad data from propagating.

⏩ Get the Data Quality Assertion Framework Script

# Wrapping Up

These five scripts focus on the core operational challenges that data engineers run into all the time. Here’s a quick recap of what these scripts do:

• Pipeline health monitor gives you centralized visibility into all your data jobs
• Schema validator catches breaking changes before they break your pipelines
• Data lineage tracker maps data flow and simplifies impact analysis
• Database performance analyzer identifies bottlenecks and optimization opportunities
• Data quality assertion framework ensures data integrity with automated checks

As you can see, each script solves a specific pain point and can be used individually or integrated into your existing toolchain. So choose one script, test it in a non-production environment first, customize it for your specific setup, and gradually integrate it into your workflow.

Happy data engineering!

Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.