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The obvious Getting started
As a data engineer, you are likely responsible (at least in part) for your organization's data infrastructure. You build pipelines, maintain databases, ensure that data flows smoothly, and troubleshoot where things break properly. But here's the thing: How much of your day goes into manually checking Pipeline Health, verifying data loads, or monitoring program performance?
If you're honest, it's probably a big chunk of your time. Data engineers spend many hours in their work day on operational tasks – monitoring tasks, validating schemas, tracking data lists, when they can't make better plans.
This article covers five Python scripts designed specifically to address repetitive infrastructure and operational tasks that consume your engineering time.
🔗 Link to the code on GitHub
The obvious 1. Pipeline Health Monitor
Pain point: You have multiple ETL jobs running on different schedules. Some run hourly, others daily or weekly. Checking that everything completed successfully means logging into various programs, querying logs, looking at timestamps, and piecing together what's happening. By the time you see that the job is failing, the underlying processes have broken down.
What the text says: Monitors all your data pipelines in one place, tracks execution status, alerts on failures or delays, and keeps a historical log of job performance. It provides an integrated health dashboard that shows what's running, what's failed, and what's taking longer than expected.
How does this work: The script connects to your army operations program (like Air flowor read from log files), extract log metadata, compare resistance to expected times and playback times, and flag anomalies. It calculates success rates, average times, and identifies patterns in failure. You can send alerts via email or slack when issues are detected.
⏩ Get the Pipeline Health Monitor script
The obvious 2. Schema Vaiting and change the detector
Pain point: Your Upstream data sources change without warning. A column is renamed, the data type changes, or a new required field appears. Your Pipeline Breaks, the reports below fail, and you may be struggling to figure out what changed and where. Schema Drift is a relevant problem in data pipelines.
What the text says: Automatically compare the current table Schemas against the underlying definitions, detecting any change in column names, data types, constraints, or properties. Generate detailed change reports and can enforce schema contracts to prevent changes that break propagation through your system.
How does this work: The script reads the schema definitions from data sources or data files, compares them against the schemas stored in the base (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 inconsistent data.
⏩ Get the schema serving script
The obvious 3. Data Tracker
Pain point: Someone asks “where does this field come from?” or “What happens if we modify this source table?” And you don't have a good answer. Digs through SQL scripts, ETL code, and scripts (if available) trying to trace the flow of data. Understanding dependencies and impact analysis takes hours or days instead of minutes.
What the text says: Tad Lineage maps automatically by Parsing SQL Queries, ETL scripts, and Transform Logic. It shows you the complete path from the source programs to the final tables, including all the changes used. Creates dependency graphs and impact analysis reports.
How does this work: The script uses SQL passing libraries to extract tables and columns from queries, creates a directed graph of data dependencies, tracks changes used in each section, and sees the complete list. It can perform an impact analysis to show which factors have been affected by a change in any given source.
⏩ Find the text of the Fashion Lineage Tracker
The obvious 4. Database Analysis Analysis
Pain point: Questions run slower than usual. Your tables are blooming. References may be lost or unused. You suspect performance issues but identifying the root cause means performing diagnostics, analyzing query plans, looking at table statistics, and interpreting cryptic metrics. Time consuming work.
What the text says: Automatically analyzes database performance by identifying slow queries, missing indexes, table hits, unused indexes, and sunoptal configuration. It makes actionable recommendations with limited performance impact estimates and provides the exact SQL needed to implement fixes.
How does this work: Catalogs script queries database and performance views (pg_stats for postgresql, mysql_schema detailsetc.), analyze query execution statistics, identify tables with high sequential rates that indicate missing indexes, identify polished tables that need to be maintained, and generate performance recommendations based on potential impact.
⏩ Get the Database Performance Analyzer Script
The obvious 5. Framework for quality assurance
Pain point: You need to ensure data quality in your pipelines. Does the line measure up to your expectations? Are there any unexpected nulls? Are major foreign relations catching up? He writes these tests by hand on each table, spread across the text, without an outline or reporting. When the checks fail, you get vague errors out of context.
What the text says: Provides an outline of Definition of the quality of the data as a code: Calculation limits, defining constraints, reliable integrity, value ranges, and custom business rules. It automatically escapes everything, generates detailed failure reports with context, and integrates with your Pipeline Pipelines to make PIPELINE tasks fail when quality checks don't pass.
How does this work: The script uses the funny dentarax where you define quality rules in simple python or yaml. It extracts all the meaning of your data, collects the results of detailed failure information (Which lines failed, values that could not be bad), and can be integrated into Pipeline Dags to serve as standard documents in data distribution.
⏩ Get quality quality text for quality content
The obvious Wrapping up
These five scripts focus on customization 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 activities
- Schema Validator catches violating changes before they break your pipeline
- Data Lineage Tracker Maps data flow and facilitates impact analysis
- Database performance analysis identifies bottlenecks and opportunities for efficiency
- Data Quality Assurance 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 tool. So pick one script, test it in a non-production environment first, customize it with your specific settings, and slowly integrate it into your workflow.
Happy data engineering!
Count Priya C is a writer and technical writer from India. He likes to work in the field of statistical communication, programming, data science and content creation. His areas of interest and expertise include deliops, data science and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, he is working on learning and sharing his knowledge with the engineering community through tutorials, how-to guides, idea pieces, and more. Calculate and create resource views and code tutorials.
