1. Introduction & Overview

What is Data Observability?

Data Observability is the ability to fully understand the health, lineage, and performance of data across your infrastructure. In a DevSecOps context, it ensures that data pipelines are trustworthy, auditable, and compliant—especially critical when automating deployments, ensuring security, and meeting regulatory requirements.

Data observability incorporates:

Monitoring
Alerting
Logging
Tracing
Data lineage & quality checks

History or Background

Origin: Coined around 2020, evolving from the need to apply software observability concepts (logs, metrics, traces) to data pipelines and data assets.
Drivers: Rise in data-driven decision-making, GDPR/CCPA regulations, ML/AI use-cases, and cloud-native architectures.
Tools that popularized it: Monte Carlo, Databand (IBM), Acceldata, OpenLineage, and Apache Airflow.

Why is it Relevant in DevSecOps?

Security: Detect unauthorized data access or anomalies in real-time.
Compliance: Ensure traceability and audit trails for regulations (e.g., HIPAA, SOC 2).
Automation: Prevent deployment of broken data pipelines in CI/CD workflows.
Reliability: Early detection of broken jobs, schema changes, and failed ETLs.

2. Core Concepts & Terminology

Key Terms

Term	Definition
Data Lineage	The path that data follows as it moves through the system.
Data Quality	Accuracy, completeness, consistency, and validity of data.
Data Freshness	Timeliness of data availability.
Anomaly Detection	Identifying unusual patterns or outliers in datasets.
Schema Drift	Changes in structure of the data that can break pipelines.

How It Fits into the DevSecOps Lifecycle

DevSecOps Phase	Data Observability Role
Plan	Identify data dependencies and governance risks.
Develop	Monitor dataset versions and changes during development.
Build	Integrate checks into CI/CD pipelines (e.g., failing builds with broken data).
Test	Run automated data validation tests.
Release	Tag datasets with metadata before promotion.
Operate	Continuously monitor pipelines for freshness, lineage, and anomalies.
Monitor	Real-time alerting and dashboards for SLA/SLO compliance.

3. Architecture & How It Works

Components

Data Collectors – Agents that track metadata, logs, and metrics from pipelines (e.g., Airflow, Spark).
Metadata Store – Stores information like schema, freshness, and lineage.
Monitoring Engine – Detects anomalies, schema changes, and freshness issues.
Alerting & Dashboard Layer – Notifies users through Slack, PagerDuty, etc.
APIs / CI Integrations – Hooks for GitHub Actions, GitLab CI, Jenkins, etc.

Internal Workflow

Ingest Metadata: Collect data pipeline logs, schema snapshots, and job runtimes.
Analyze: Apply statistical models to determine data health.
Visualize: Show DAGs, lineage graphs, and health indicators.
Alert: Raise notifications for data downtime or corruption.
Remediate: Trigger rollbacks or hold CI/CD releases.

Architecture Diagram (Descriptive)

Imagine a layered architecture:

          ┌──────────────────────────┐
          │     CI/CD Pipeline       │
          └────────▲─────────────────┘
                   │
          ┌────────┴───────────────┐
          │ Data Observability API │
          └────────▲────┬──────────┘
                   │    │
        ┌──────────┘    └───────────┐
        ▼                           ▼
┌────────────┐             ┌─────────────────┐
│ Metadata DB│◄────────────│ Data Collectors │
└────────────┘             └─────────────────┘
        │                           ▲
        ▼                           │
┌────────────┐             ┌────────────────────┐
│ Monitoring │◄────────────│ Airflow/Spark Logs │
└────────────┘             └────────────────────┘
        ▼
┌────────────┐
│ Alerting UI│
└────────────┘

Integration Points with CI/CD or Cloud Tools

CI/CD Integration:
- Jenkins: Fail builds if pipeline health checks fail.
- GitHub Actions: Pre-deployment checks for schema drift.
- GitLab CI: Use custom scripts to validate datasets.
Cloud Platforms:
- AWS Glue, S3, Redshift
- GCP BigQuery, Cloud Composer
- Azure Data Factory, Synapse

4. Installation & Getting Started

Basic Setup or Prerequisites

Python 3.8+
Apache Airflow or Spark (optional)
Postgres/MySQL for metadata store
Cloud storage or warehouse (S3, BigQuery, etc.)

Hands-On: Step-by-Step Setup (Using Open Source Tools)

Step 1: Install Great Expectations

pip install great_expectations

Step 2: Initialize a Project

great_expectations init

Step 3: Create Expectations

great_expectations suite new

Follow the CLI to define:

Null checks
Range checks
Uniqueness constraints

Step 4: Integrate with CI (GitHub Actions)

# .github/workflows/data-quality.yml
name: Data Quality

on: [push]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Setup Python
      uses: actions/setup-python@v2
    - name: Install Dependencies
      run: pip install great_expectations
    - name: Run Data Quality Checks
      run: great_expectations checkpoint run my_checkpoint

5. Real-World Use Cases

Use Case 1: Preventing Deployment of Broken Pipelines

Tool: Monte Carlo + GitLab CI
Result: Blocked release when schema mismatch detected between staging and prod

Use Case 2: Compliance Reporting in Financial Sector

Tool: Acceldata
Use: Track lineage for audit logs
Compliance: SOX, GDPR

Use Case 3: Monitoring Machine Learning Feature Stores

Tool: WhyLabs
Detects drift in real-time ML input features
Ensures reliability of production ML models

Use Case 4: Healthcare Data Accuracy

Tool: Great Expectations
Use: Validate EHR datasets against compliance rules
Industry: Healthcare (HIPAA-bound)

6. Benefits & Limitations

Benefits

Early detection of data issues
Improved trust in data-driven decisions
Compliance-ready audit trails
Facilitates DevSecOps automation

Limitations

False positives in anomaly detection
Overhead in maintaining expectations
Requires domain knowledge for meaningful alerts
Tooling maturity still evolving in open-source space

7. Best Practices & Recommendations

Security Tips

Secure metadata stores with RBAC
Encrypt logs and lineage information
Audit access to observability dashboards

Performance & Maintenance

Use sampling for large datasets
Automate expectation updates with version control

Compliance Alignment

Store historical lineage for traceability
Automate compliance checks during deployment

Automation Ideas

Auto-trigger schema validation on PRs
Use observability gates in CD workflows

8. Comparison with Alternatives

Tool / Approach	Data Observability	Data Catalogs	Data Quality Frameworks
Primary Focus	Health & anomalies	Metadata	Rule-based validation
Examples	Monte Carlo, Acceldata	Collibra, Alation	Great Expectations
DevSecOps Role	CI/CD blocker, alerting	Planning stage	Pre-deployment checks
Best Use	Real-time, scalable	Governance	Test-based validation

Choose Data Observability when:

You need real-time detection
Your data pipelines are complex or mission-critical
You are running regulated or audited workloads

9. Conclusion

Data Observability is a cornerstone of modern DevSecOps practices where data reliability, integrity, and security must be integrated into every stage of the lifecycle. By embedding observability into pipelines, teams can proactively manage risks, ensure compliance, and prevent silent data failures from impacting production systems.

Future Trends

AI-powered anomaly detection
Deeper CI/CD integration
Standardization via OpenLineage and DataOps standards

Data Observability in DevSecOps