Automate delivery
Apply CI/CD and testing to data pipelines, not just application code.
Architecture Path · Reliability · Step 5
Data Operations
The operational discipline that keeps data platforms reliable, observable, and trusted. Learn to automate, monitor, and optimize modern data pipelines with reliability engineering, observability, lineage, CI/CD, incident response, and cost control, the practices a platform is judged on in production.
8
Modules
40
Chapters
Prod
Reliability
Capstone
Observability + runbook
The journey
Eight modules, one arc
The eight modules build cumulatively toward a real capstone. Watch the work move, and the value compound, at every stage.
Each module builds the capability the next one depends on, ending in a portfolio-ready capstone.
Outcomes
What you'll be able to do
Observe everything
Instrument freshness, completeness, latency, and failures across the platform.
Trace lineage
Follow data from source to consumption and understand downstream impact.
Run to SLOs
Define and defend service levels for data products and pipelines.
Respond to incidents
Detect, triage, and resolve data incidents with a clear runbook.
Control cost
Apply FinOps to keep platform economics healthy as usage grows.
Curriculum
8 modules, 40 chapters, ending in a capstone
Eight modules of five chapters each, sequenced so the material builds cumulatively to a real, portfolio-ready capstone. Expand any module for its focus and lessons.
01 From Pipelines to DataOps
Understand why data needs its own operational discipline.
- What DataOps is and why pipelines fail in production
- The data reliability problem, borrowed from SRE
- The lifecycle of a pipeline and where it breaks
- Principles: automation, observability, and fast feedback
- Assessing the operational maturity of a data platform
02 Automation and CI/CD for Data
Bring software engineering discipline to data pipelines.
- Version control and environments for data code
- Testing data: unit, integration, and data tests
- CI/CD pipelines for data transformations
- Deploying changes safely with review and rollback
- Automating a pipeline deployment end to end
03 Data Observability
Instrument the platform so problems surface before consumers notice.
- The pillars: freshness, volume, schema, quality, lineage
- Metrics, logs, and traces for data pipelines
- Detecting anomalies in data and in pipeline behavior
- Dashboards that show platform health at a glance
- Instrumenting a pipeline for full observability
04 Lineage and Impact Analysis
Trace data end to end and reason about change.
- Technical and business lineage, and why both matter
- Capturing lineage automatically across the stack
- Impact analysis: what breaks if this changes
- Using lineage for debugging and for governance
- Building a lineage view for a real pipeline
05 Reliability, SLOs and Error Budgets
Define and defend service levels for data.
- SLIs and SLOs for data products and pipelines
- Error budgets and how they drive decisions
- Freshness, completeness, and correctness targets
- Alerting that is actionable, not noisy
- Setting SLOs for a data product with stakeholders
06 Incident Response for Data
Handle data incidents calmly and learn from them.
- Detecting and triaging a data incident
- Roles, communication, and severity during an incident
- Root-cause analysis and blameless postmortems
- Building and maintaining runbooks
- Running a simulated data-incident response
07 Performance and FinOps
Keep the platform fast and its economics healthy.
- Diagnosing and fixing pipeline performance problems
- Cost visibility and attribution across the platform
- Chargeback, showback, and accountability for spend
- Guardrails and forecasting tied to business drivers
- Optimizing a workload for both speed and cost
08 Capstone: Operate a Data ProductCapstone
Put a data product into a production-ready operational state.
- Instrument a pipeline for observability and lineage
- Define SLOs, alerts, and an error budget
- Write the runbook for its likely incidents
- Add a cost view and optimization plan
- Capstone: present an operability package, dashboards, SLOs, and runbook, as a portfolio artifact
Who it's for
Built for Engineers and leads making data pipelines reliable and observable
Data and platform engineers
Engineers responsible for pipelines running reliably in production.
DataOps and reliability engineers
Those building the observability and reliability practice.
Data platform leads
Leads accountable for platform health, cost, and trust.
Architects
Architects who must design for operability, not just correctness.
Program formats
How you learn
Self-paced
Work through it at your own pace, with lifetime access to every module and the capstone.
Mentor-led cohort
A guided cohort with live sessions, reviews, and a peer group working the same path.
Private corporate
A closed cohort for your team, tailored to your platforms, domains, and priorities.
Portfolio-building
Every module produces an artifact; the capstone assembles them into a portfolio deliverable.
For teams
Bring it to your team
Run Data Operations (DataOps) as a private, closed cohort tailored to your platforms, domains, and priorities, as part of building the architecture capability your organization needs.
Related programs
Explore the path
FAQ
Data Operations (DataOps) - answered
Who is Data Operations for?
Data and platform engineers, DataOps and reliability engineers, platform leads, and architects who must keep data pipelines reliable, observable, and cost-effective in production.
How is it different from general DevOps?
It applies reliability, CI/CD, and observability specifically to data, where freshness, schema drift, lineage, and data quality create failure modes application DevOps does not cover.
Do I need to code?
You should be comfortable with pipelines and some scripting. The focus is on operational practice and design rather than a single language or tool.
What is the capstone?
A production-ready operability package for a data product: observability instrumentation, SLOs, alerting, a runbook, and a cost plan, assembled as a portfolio artifact.
Where does it sit in the path?
It follows the architecture operating model: once you can design domains and data products, DataOps is how you keep them trustworthy in production.
Self-paced or cohort?
Both, plus private corporate cohorts tailored to your stack.
Take the next step on the path
Enrol, enquire, or explore the full IC-to-Head of Data Architecture path.