“Data is the new oil. It’s valuable, but if unrefined it cannot really be used.” – Clive Humby
Modern Data Tech and the Role of DataOps: A Deep Dive into High-Performance Data Engineering and Metadata-Driven Stacks
Introduction
As data-driven organizations scale, the pressure to deliver reliable, timely, and governed data has intensified. While the modern data stack has evolved rapidly with tools like Snowflake, dbt, and Fivetran, the need for operational excellence in data workflows has given rise to DataOps—a discipline that applies agile, DevOps, and lean principles to the end-to-end data lifecycle.
This post explores the historical evolution of DataOps and the modern data engineering stack, highlighting what effective practices look like, which pitfalls to avoid, and how tools like Great Expectations and DataHub support scalable, trustworthy data platforms.
The Evolution of the Data Stack
From ETL to ELT and Metadata-First Architectures
In the early 2000s, data engineering focused primarily on ETL pipelines managed via batch jobs and cron scripts. Data warehouses were monolithic, tightly coupled, and lacked transparency.
The rise of cloud-native data platforms and decoupled storage and compute (e.g., Snowflake, BigQuery, Databricks) enabled the ELT paradigm. Extract and load stages became standardized through tools like Fivetran and Airbyte, while transformation was deferred and modularized with frameworks like dbt, leveraging the scalability and familiarity of SQL.
This shift required better observability and governance—giving rise to metadata-driven stacks where cataloging, lineage, testing, and orchestration are critical components.
What Is DataOps?
DataOps is a set of practices and cultural philosophies aimed at improving the velocity, quality, and collaboration around data engineering and analytics pipelines.
Inspired by DevOps, the core principles of DataOps include:
- Automated testing and validation of data pipelines
- Version control for data logic and artifacts
- Continuous integration and deployment (CI/CD)
- Pipeline observability and alerting
- Collaboration across engineering, analytics, and business
- Reproducibility and lineage traceability
DataOps vs. Data Engineering
| Aspect | Data Engineering | DataOps |
| Primary Focus | Building and maintaining pipelines | Operationalizing and managing pipelines |
| Core Deliverables | Ingestion, transformation, serving | Quality, observability, governance |
| Key Practices | Data modeling, orchestration | Testing, versioning, lineage, CI/CD |
| Tools | dbt, Airflow, Kafka, Spark | Great Expectations, DataHub, Monte Carlo |
High-Performing DataOps: What Good Looks Like
1. Layered Architecture with Composable Tools
A best-in-class modern data stack often consists of:
- Ingestion: Fivetran, Airbyte, Kafka
- Transformation: dbt, Spark SQL
- Orchestration: Airflow, Dagster, Prefect
- Testing: Great Expectations, dbt tests, Deequ
- Metadata and Governance: DataHub, OpenMetadata, Amundsen
- Monitoring: Monte Carlo, Databand, Sifflet
- Serving: Snowflake, BigQuery, Redshift, Delta Lake
This modular approach enables decoupling between layers and supports fault isolation, reusability, and scalability.
2. End-to-End Observability and Metadata Management
High-functioning teams treat metadata as a first-class citizen:
- Lineage graphs trace upstream and downstream impacts
- Data contracts define expectations for producers and consumers
- Schema drift detection is automated and proactively flagged
- Owners, tags, and classifications are embedded in data catalogs
Tools like DataHub or OpenMetadata provide a unified interface to track lineage, ownership, and documentation, integrating directly with orchestrators and transformation tools.
3. Integrated Quality Gates and Testing
Automated data testing frameworks such as Great Expectations and Soda Core are embedded into pipelines to validate:
- Row counts
- Nullability
- Schema changes
- Value distributions
- Freshness and latency
CI/CD pipelines (via GitHub Actions, GitLab CI, etc.) are configured to fail builds if tests do not pass. Quality becomes part of the development lifecycle, not a reactive concern.
4. Version Control and Deployment Automation
All pipeline logic—including SQL transformations, Airflow DAGs, and expectations—is stored in Git repositories and deployed using CI/CD tooling. Infrastructure is provisioned using Terraform or Pulumi, enabling consistent environments across dev, staging, and production.
Metadata Platforms and Data Dictionaries
Modern data dictionaries extend far beyond column descriptions. They form the backbone of discoverability, auditability, and trust.
Great Expectations
- Expectations serve as unit tests for data
- Generates human-readable documentation
- Integrates with batch and streaming pipelines
- Supports checkpoints, validation stores, and CI/CD hooks
DataHub
- Built for large-scale metadata collection
- Ingests lineage from Airflow, dbt, Kafka, Snowflake, etc.
- Provides search, access control, usage statistics, and schema history
- Enables data product ownership and SLA visibility
When implemented correctly, these platforms reduce cognitive load, minimize tribal knowledge, and empower self-service analytics.
What Poor DataOps Looks Like
Organizations that fail to invest in DataOps typically exhibit the following symptoms:
| Symptom | Consequence |
| No data ownership | Pipeline failures take days to resolve |
| Lack of testing and validation | Broken dashboards and incorrect insights |
| No observability or monitoring | Data issues detected by end users instead of alerts |
| Out-of-date documentation | Analysts rely on Slack threads to understand data usage |
| Inconsistent definitions | Metrics vary across teams, eroding trust |
These anti-patterns stem not from lack of tools, but from lack of process and cultural buy-in. Simply deploying Airflow or dbt does not create a resilient data platform.
Patterns for Scaling DataOps and Engineering Together
To mature both data engineering and DataOps simultaneously:
- Embed observability and testing into development workflows
- Assign data product owners to critical datasets and establish SLAs
- Adopt lineage-aware tools to enforce downstream impact analysis
- Define and enforce contracts between producers and consumers
- Measure success with KPIs such as pipeline reliability, incident MTTR, and SLA adherence
Wrapping up…
The modern data tech stack is no longer just about storing and querying data. It is about building robust, observable, and governed data products that can scale with business needs.
DataOps is the operational muscle that transforms raw pipelines into reliable infrastructure. Data engineering provides the creative and technical execution. Together, supported by metadata platforms like Great Expectations and DataHub, they form the foundation of data excellence in any modern organization.As organizations move toward data mesh architectures and federated ownership models, these capabilities are not optional—they are foundational.
