Analytics Engineering: Trusted Metrics from Raw Data
Analytics, Data Science & Data Governance: leave with a star-schema model, semantic-layer metric definitions, lineage and ownership notes.
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- Bring your own work — code, campaign, report or plan
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What this session would cover
Proposed outline — the mentor finalises the agenda once this topic is scheduled.
- 1Why analytics, data science & data governance matters — the common problem: Every team computes revenue differently, so dashboards disagree.
- 2Core concepts in plain language: Exploratory analysis, business intelligence, dashboards, dimensional modeling, data warehouses, data lakes
- 3Going further: lakehouses, semantic layers, data catalogs, lineage, data quality, ownership
- 4Framework walkthrough: Normalization, Dimensional Modeling, ELT Pipelines, Data Quality Dimensions
- 5Practical workflow, built live: A star-schema model, semantic-layer metric definitions, lineage and ownership notes.
- 6How to measure it: Query latency, Pipeline freshness, Data-quality check pass rate, Failed job rate
- 7An illustrative case (a fictional example, not a client result), then live Q&A on your own situation
Who it's for
- • Students and freshers entering tech
- • Working developers and engineers
- • Tech leads and architects
You'd leave with
- A star-schema model, semantic-layer metric definitions, lineage and ownership notes.
- A working understanding of Normalization and Dimensional Modeling
- A short list of measures to track: Query latency, Pipeline freshness, Data-quality check pass rate
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