#032 - Beyond Dashboards: How Orchestration-Native Observability Is Saving Data Engineering

5 tools + 1 framework that cut incident time by 60%

Hey there,

I've been asking data teams the same question for months: "How long does it take you to figure out why something broke?"

The answer is always some version of "too long."

Here's what I learned from analysing this pattern across teams, plus 5 tools and 1 framework you can start using today.

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🔥 This Week's Big Insight

The Problem: 73% of data teams monitor when things break, but have no visibility into why they break.

The Solution: Orchestration-native observability (monitoring pipelines, not just warehouses).

The Impact: Teams implementing this see 60% faster incident resolution on average.

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🛠️ 5 Tools You Should Bookmark This Week

For Task-Level Monitoring:

1. Dagster - Asset-oriented orchestration with built-in observability

   • Use case: See exactly which pipeline task failed and why

   • Quick start: Their 10-minute tutorial shows task-level visibility

2. Prefect - Modern workflow orchestration with detailed logging

   • Use case: Clear failure information and automated retries

   • Quick start: Free tier for teams under 3 users

For Data Quality Monitoring:

3. Monte Carlo - Data observability platform focusing on the "5 pillars"

   • Use case: Automated anomaly detection across freshness, volume, and schema

   • Quick start: They offer free data health assessments

4. Metaplane - Lightweight data monitoring with fast setup

   • Use case: Real-time alerts without heavy configuration

   • Quick start: Connect in under 30 minutes

For Schema Change Detection:

5. Great Expectations - Open source data validation framework

   • Use case: Catch schema drift before it breaks downstream

   • Quick start: Their schema validation tutorial takes 15 minutes

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📋 Copy This: 5-Point Readiness Checklist

Rate yourself 1-5 on each:

☑︎ Task Visibility: Can you see individual pipeline task status in real-time?
☑︎ Schema Monitoring: Do you catch schema changes before they break downstream?
☑︎ Impact Analysis: Do you know what breaks when a pipeline fails?
☑︎ Cost Attribution: Can you track compute costs per pipeline?
☑︎ SLA Tracking: Do you monitor data freshness and completeness automatically?

Score:

• 20-25: You're in the top 10% of data teams

• 15-19: Solid foundation, some gaps to fill

• 10-14: Standard setup, big improvement opportunity

• Below 10: High risk zone - prioritise this

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🎯 Quick Win: 15-Minute Task Monitoring Setup

If you're using Airflow, add this to any DAG for instant task-level visibility:

import logging
from datetime import datetime, timedelta
from airflow.models.dag import DAG

def log_task_metrics(**context):
    """Production-ready callback for task monitoring"""
    try:
        task_instance = context["task_instance"]
        start_time = task_instance.start_date
        end_time = task_instance.end_date
        
        if start_time and end_time:
            duration = (end_time - start_time).total_seconds()
            logging.info(f"Task '{task_instance.task_id}' finished. "
                        f"State: {task_instance.state}. Duration: {duration:.2f}s")
        else:
            logging.warning(f"Task '{task_instance.task_id}' finished. "
                           f"State: {task_instance.state}. No timing data.")
    except Exception as e:
        logging.error(f"Monitoring callback error: {e}")

# Apply to ALL tasks in the DAG via default_args
default_args = {
    "owner": "data_team",
    "retries": 1,
    "retry_delay": timedelta(minutes=5),
    "on_success_callback": log_task_metrics,
    "on_failure_callback": log_task_metrics,
    "on_retry_callback": log_task_metrics,  # Track retries too!
}

# Now every task gets monitoring automatically
with DAG(
    dag_id="monitored_pipeline",
    start_date=datetime(2025, 8, 9),
    schedule="@daily",
    default_args=default_args,  # This is the magic line
) as dag:
    # All tasks inherit the monitoring callbacks
    extract_task = BashOperator(
        task_id="extract_data",
        bash_command="your_extract_script.sh"
    )

Implementation time: 15 minutes
Value: Immediate visibility into task performance patterns

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📚 This Week's Reads

Must-read articles I bookmarked:

• Netflix's observability lessons - How they built tools to monitor petabytes of data

• Airbnb's data quality evolution - Their innovative DQ Score approach

• Spotify's data platform architecture - How they process 1.4 trillion data points daily

Tools/Resources:

• Data Engineering Weekly - Best curated data engineering content

• dbt's Analytics Engineering Roundup - Weekly analytics engineering insights

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💡 Industry Intel

What's trending this week:

• Snowflake just launched enhanced pipeline monitoring with built-in telemetry and better Monte Carlo integration

• Monte Carlo raised $135M Series D, pushing valuation beyond $1.6B (validates the data observability market explosion)

• Major companies migrating from Airflow to Dagster specifically for observability features (names confidential, but momentum is real)

Who's hiring data engineers with observability skills:

• Netflix (Senior Data Platform Engineers + Observability specialists)

• Stripe (Data Infrastructure Engineers with platform reliability focus)

• Databricks (Solutions Architects - observability expertise increasingly required even when not in job title)

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🚀 Next Week Preview

"The $500K Snowflake Bill: 3 Cost Optimisations That Cut Warehouse Spend by 40%"

Including:

• The warehouse query that's probably costing you $10K/month

• 5-minute optimisation checklist

• Cost monitoring dashboard template you can copy

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💬 Community Question

This week: What's your biggest pipeline monitoring blind spot right now?

Reply and tell me - I'll feature the best insights in next week's issue (with your permission).

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🎁 Subscriber Exclusive

Free resource: My "Orchestration Observability Setup Guide" (10-page Guide)

Includes:

• Tool comparison matrix with scoring framework

• Implementation timeline template

• Cost-benefit calculation spreadsheet

• 30+ monitoring queries you can copy-paste

Newsletter subscribers only - Subscribe OR if you are already subscriber comment with “Observability” and I will send you the link.

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Found this useful? Forward to a colleague who's tired of debugging data incidents at 2 AM.

Want more like this? Hit reply and let me know what data engineering topics you want me to dive into next.

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That’s it for this week. If you found this helpful, leave a comment to let me know ✊

About the Author

Khurram, founder of BigDataDig and a former Teradata Global Data Consultant, brings over 15 years of deep expertise in data integration and data processing. Leveraging this extensive background, he now specialises in organisational financial services, telecommunications, retail, and government sectors, implementing cutting-edge, AI-ready data solutions. His methodology prioritises value-driven implementations that effectively manage risk while ensuring that data is prepared, optimised, and advanced analytics.

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