As cloud estates grow, engineering leaders are asked to answer questions that intuition cannot settle. Why did spend rise by a third? Which service is consuming the error budget? Are releases getting faster and safer, or just more frequent? Is the internal platform actually improving developer productivity? Without disciplined data and analytics, these questions produce arguments instead of decisions, and teams carry waste, instability and tool sprawl.

The Data and Analytics for Cloud, DevOps, FinOps & Platform Engineering Training Course answers them over ten days. The first block covers the observability data pipeline, querying logs and metrics, data modelling for operational data and the analytical toolkit in SQL and Python. The second applies it to delivery performance, reliability engineering and incident analysis. The third addresses FinOps: cost data structure, allocation, unit economics, commitments, rightsizing and forecasting. The final block concentrates on platform engineering analytics, automation, governance and an integrated capstone.

Every day is laboratory based, using sample telemetry, billing exports and pipeline data. Participants finish with working dashboards, queries, and a prioritised improvement plan for their own cloud environment.

After completing the course, participants will be able to:

• Design an observability pipeline for metrics, logs and traces using OpenTelemetry, Prometheus and Grafana.
• Query and analyse operational data using SQL and Python with pandas.
• Calculate and interpret DORA metrics from source control and pipeline data.
• Define service level indicators, objectives and error budgets and report against them.
• Analyse cloud billing data and allocate cost through tagging and account structure.
• Identify waste and forecast spend, and apply commitment and rightsizing analysis.
• Detect anomalies and analyse incidents to find recurring causes.
• Measure platform adoption and developer experience and present a prioritised improvement plan.

The course is designed for technical professionals and leaders who run or govern cloud platforms, including:

• DevOps engineers and site reliability engineers
• Cloud architects and platform engineers
• FinOps practitioners and cloud cost analysts
• Data engineers and analytics engineers supporting IT operations
• Engineering and delivery managers
• IT operations and infrastructure managers
• Cloud centre of excellence and governance leads
• Technical finance partners working with cloud budgets

Participants take away:

• A working observability dashboard covering metrics, logs and service level objectives.
• A set of SQL and Python notebooks for operational and cost analysis.
• A DORA metrics calculation pipeline adaptable to their own tooling.
• A FinOps reporting model with allocation rules, unit costs and forecast.
• A platform scorecard template for adoption and developer experience.
• A capstone recommendation pack and prioritised roadmap for their own environment.

The course is laboratory-intensive and built on realistic cloud data. It combines:

• Daily guided labs using sample telemetry, billing exports and pipeline data
• Notebook-based exercises in SQL and Python with pandas
• Dashboard builds in Grafana and a business intelligence tool
• Scenario reviews of outages, cost spikes and delivery slowdowns
• A ten-day capstone applied to participants' own environments where possible
• Code, queries and templates provided for reuse at work

Participants who complete the ten days, including the labs and the capstone presentation, receive a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy. The certificate lists the technical areas covered.