Here's a number that should get your attention: a single hour of production downtime for a moderate-sized SaaS company can cost $100,000+ in revenue loss, engineering time, and reputation damage. A comprehensive observability solution? Around $50,000 annually. If it prevents just one major incident—or more realistically, catches dozens of smaller issues before they escalate—the math is obvious.
Yet observability remains one of the most undervalued aspects of modern engineering. It's the difference between companies that scale gracefully and companies that collapse under their own success.
the two types of engineering teams
There are teams that invest in observability proactively, and teams that wait for a catastrophic failure to act. The frustrating part? It's company money either way. The only question is whether you're spending it on prevention or disaster recovery.
Some companies never recover from a major outage—not just because of immediate revenue loss, but because of compounding effects: customer trust eroded, engineering teams burned out, technical debt accumulated. The companies that do recover? They had enough visibility to understand what went wrong and prevent it from happening again.
monitoring vs. observability
Monitoring tells you when something is wrong based on predefined metrics—CPU usage, memory, error rates. It's reactive by nature.
Observability goes deeper. It's the ability to understand your system's internal state and ask questions you didn't know you needed to ask. The three pillars: metrics (numerical data over time), logs (event records), and traces (request journeys through your system).
OpenTelemetry - the industry standard
OpenTelemetry is the open-source framework revolutionizing observability. Before it, switching monitoring tools meant re-instrumenting your entire codebase. OpenTelemetry provides:
- Vendor-neutral instrumentation: Write once, export anywhere
- Automatic instrumentation: Many frameworks supported out-of-the-box
- Standardized data models: Consistent telemetry across platforms
You can instrument with OpenTelemetry SDKs and send data to Datadog, Prometheus, Jaeger, or any backend supporting OTLP. No vendor lock-in.
Datadog and modern observability platforms
Modern observability platforms like Datadog have transformed how we approach system visibility. The key innovation is unified observability—instead of juggling separate tools for APM, logs, and infrastructure monitoring, everything is correlated in one place. When you pivot from a metric spike to the exact log line to the distributed trace, troubleshooting changes from hours to minutes.
What makes platforms like Datadog particularly valuable is their embrace of open standards like OpenTelemetry rather than forcing proprietary instrumentation. This approach benefits the entire ecosystem and gives teams flexibility.
A data pipeline story
Let me share an example that illustrates the tangible value of proper observability in data platforms.
A data processing pipeline was showing intermittent failures in staging. The error messages were cryptic—sometimes the jobs completed, sometimes they crashed with out-of-memory errors. Without distributed tracing, this would have been nearly impossible to debug.
With OpenTelemetry instrumentation feeding into their observability platform, the team traced each job execution from ingestion to processing to output. They discovered a specific combination of data characteristics that triggered excessive memory allocation in one processing step. The leak only manifested under production-like data volumes, which is why it hadn't appeared in earlier testing.
They fixed the issue in staging. Two weeks later, production scaled to handle exactly the data pattern that would have triggered the bug. Because they caught it early, they avoided what would have been a 3-hour production outage during peak business hours.
Potential cost: 3-hour outage + emergency debugging + hotfix deployment
Actual cost: 2 hours of debugging in staging
Difference: Thousands of dollars and immeasurable stress
Beyond dashboards: the power of intelligent alerting
Here's the uncomfortable truth: most monitoring setups are just analytics that nobody looks at. Dashboards are great for post-mortems, but they don't prevent incidents.
The real value of observability comes from intelligent alerting—systems that actively watch for anomalies and notify you before users are impacted. This means:
- Proactive detection: Spotting trends that indicate future problems (memory creeping up, latency gradually increasing, data freshness degrading)
- Contextual alerts: Not just "disk is full" but "disk filling rate suggests outage in 2 hours, likely caused by job X"
- Smart thresholds: Using ML-based anomaly detection instead of static limits that generate noise or miss real issues
- Alert correlation: Understanding that 50 alerts might actually be one root cause
The difference between reactive monitoring and proactive observability is whether you're woken up at 3 AM or you fix the issue at 3 PM before it becomes critical.
A cultural shift: from reactive to proactive
Implementing observability isn't just about tools—it's about changing how teams think about system reliability.
In a reactive culture, engineers wait for alerts, debug frantically, patch quickly, and move on. In a proactive culture enabled by observability, teams:
- Explore actively: Regularly dive into telemetry to understand system behavior, not just when things break
- Learn continuously: Use observability data to validate assumptions, test hypotheses, and improve architecture
- Prevent systematically: Identify patterns in near-misses and address them before they become incidents
- Share knowledge: Make dashboards visible, review trends in team meetings, document what metrics actually matter
Observability encourages curiosity. It transforms "everything looks fine" into "let's understand how things actually work." This mindset shift is what separates high-performing teams from those constantly firefighting.
Why this matters for data platforms
As someone passionate about data platform engineering, I see observability as the nervous system of modern data infrastructure. Data platforms are complex distributed systems with multiple failure modes.
Without observability, data quality issues go unnoticed, pipeline performance degrades until catastrophic failure, and debugging requires manual log diving. With proper observability, anomalies trigger investigations before SLA breaches, root cause analysis takes minutes instead of hours, and data reliability becomes measurable and improvable.
Where to start?
If you're reading this and thinking "we need to do this," you're probably wondering where to begin. The good news is you don't need to instrument everything on day one.
Start with your most critical data pipelines or services—the ones where an outage would hurt the most. Implement basic OpenTelemetry instrumentation to capture traces and key metrics. Connect it to an observability platform (there are both open-source and commercial options). Set up a few intelligent alerts based on what actually matters to your users.
The important thing is to start somewhere. Every journey toward better observability begins with making one system visible.
If you're working on observability for data platforms and want to exchange ideas or discuss implementation strategies, feel free to reach out. I'm always interested in learning how different teams approach these challenges.
This is the first article in a series on observability. In upcoming posts, I'll dive into concrete implementations, tooling comparisons, and real-world patterns for building observable data platforms.