DataOps: how Netflix and Spotify manage data at scale
TL;DR
- DataOps = DevOps principles applied to the data lifecycle
- Pillars: continuous data integration, collaboration, automation, end-to-end monitoring
- Netflix uses Apache Iceberg + Maestro; Spotify created Backstage for their data catalog
- Realistic roadmap: foundations (1-2 months) → automation → collaboration → excellence
- ROI: 80% less time on incidents, 50% fewer errors in production
If you work with data, you’ve probably lived this nightmare: a pipeline failing in production at 3 AM, nobody knows what changed, the data scientist blames the data engineer, the engineer blames the infrastructure team, and meanwhile the CEO’s dashboard shows numbers from two days ago.
DataOps exists to make this stop happening.
What DataOps is (and isn’t)
DataOps is applying DevOps principles to the data lifecycle. But it’s not simply “DevOps for data.” It’s a methodology that recognizes the unique peculiarities of working with data:
- Data doesn’t compile. You can’t “test” a dataset the same way you test code.
- Data changes without anyone touching code. A vendor modifies their API, a user enters data in an unexpected format, a source disappears.
- Data errors are silent. A code bug usually fails loudly. A data error can propagate for months before anyone notices.
That last point is something I explored in 90% of your data is garbage: the problem isn’t just having data, it’s knowing it’s correct.
The pillars of DataOps
1. Continuous data integration
Just as CI/CD automates code deployment, DataOps automates data flow. Every pipeline change must go through:
- Automatic data quality tests
- Schema validation
- Consistency checks
- Proactive anomaly alerts
This isn’t optional. It’s the foundation everything else is built on.
2. Cross-team collaboration
The traditional model where the data engineer “throws data over the wall” to the data scientist doesn’t work. DataOps requires:
- Shared repositories where everyone sees pipeline code
- Living documentation of data and its transformations
- Clear ownership: every dataset has an owner
- Direct communication channels between data producers and consumers
3. Obsessive automation
If you do it more than twice, automate it. This includes:
- Pipeline change deployment
- Historical data backfills
- Documentation generation
- Incident alerts and response
- Regression tests
4. End-to-end monitoring
Knowing the pipeline “finished” isn’t enough. You need to know:
- Did the expected data arrive?
- In the correct format?
- With the required freshness?
- Within reasonable ranges?
- Without duplicates or losses?
How the big players do it
Netflix
Netflix processes petabytes of data daily to power their recommendation system. Their DataOps approach includes:
- Apache Iceberg: open-source table format that Netflix originally developed (2017) and donated to the Apache Foundation. Enables ACID transactions on data lakes, solving the “what version of data am I looking at?” problem
- Maestro: their internal orchestrator managing thousands of data workflows
- Continuous validation: automated tests verifying data integrity at every pipeline step
Amazon
Amazon takes DataOps to the extreme with:
- Decentralized ownership: each team is responsible for their data end-to-end
- Data contracts: formal agreements between producers and consumers about what to expect from each dataset
- Automatic rollback: if a change degrades data quality, it reverts without human intervention
Spotify
Spotify democratized internal data access with:
- Backstage: open-source developer portal including data catalog
- Data mesh: architecture where business domains are responsible for their own “data products”
Implementing DataOps on your team
You don’t need to be Netflix to benefit from DataOps. Here’s a realistic roadmap:
Phase 1: Foundations (1-2 months)
- Version your pipeline code in Git (if you don’t already, start today)
- Implement basic data quality tests with Great Expectations or dbt tests
- Set up alerts for pipeline failures
Phase 2: Automation (2-4 months)
- CI/CD for your pipelines: every merge to main deploys automatically
- Automatic schema and lineage documentation
- Pipeline status dashboard
Phase 3: Collaboration (4-6 months)
- Data catalog accessible to the entire organization
- Formalized data contracts
- Explicit ownership of each dataset
Phase 4: Excellence (ongoing)
- Advanced data observability (Monte Carlo, Bigeye, Datadog)
- Mesh or fabric architecture if scale justifies it
- Data quality metrics as team KPIs
The modern DataOps tool stack
Orchestration: Airflow, Dagster, Prefect, dbt Cloud
Data quality: Great Expectations, dbt tests, Soda
Observability: Monte Carlo, Bigeye, Datadog Data Pipelines
Catalog: DataHub, Amundsen, Atlan
Versioning: DVC, LakeFS, Delta Lake
Transformation: dbt, Spark, SQL
If you’re starting out in this world, my data engineering guide gives you the necessary context.
The ROI of DataOps
Is the investment worth it? The numbers say yes:
- 80% reduction in data incident resolution time
- 50% fewer errors reaching production
- 3x faster pipeline development cycles
- Business confidence in data (hard to measure, easy to notice)
The cost of not implementing DataOps is invisible until it explodes. And when it explodes, it’s expensive.
Already using DataOps practices on your team? What tools have worked best for you?
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