Dashboards & Analytics
Turn your data into clear, usable decisions.
Dashboards and analytics that make your data visible, trustworthy and easy to act on.
Under the hood
A real-time view of how this is engineered, run and kept honest in production.
Implementation stack
ETL orchestration (dbt / Airflow) for scheduled and incremental loads
Kafka for real-time event streaming into the warehouse
SQL data warehouse with columnar storage for fast aggregation
BI toolset for governed dashboards and self-serve exploration
Semantic layer so metrics mean the same thing everywhere
Schema versioning and data-contract checks in the pipeline
Alerting on data freshness and anomaly detection
Live handling loop
Capture
Events and transaction data are pulled or streamed from sources.
Transform
ETL stages clean, type and model data into fact and dimension tables.
Stream
High-velocity data flows through Kafka into near-real-time views.
Model
Metrics are defined once in the semantic layer to stay consistent.
Visualize
Dashboards surface decisions; self-serve lets teams explore safely.
Alert
Freshness and anomaly checks page the right people automatically.
Production integrity
Idempotent transforms — reruns produce identical results, never duplicates.
Schema versioning so a pipeline change cannot break downstream screens.
Row-level data-quality checks before anything marks a dashboard 'ready'.
Access control on the semantic layer, not just the screen.
Real-world use
A representative deployment of this service in practice.
Who it was built for
A retailer whose sales data lived in six disconnected systems (anonymised).
The problem
Store managers reconciled three separate exports every morning; finance and marketing compared different numbers for the same metric, and the monthly close meant days of spreadsheet archaeology.
The deployment
CoveScreen unified the sources through an ETL pipeline into a columnar warehouse, added Kafka for live checkout events, and rebuilt reports on a single semantic layer.
The mechanism
A nightly incremental load plus a streaming path for checkout events lands in dimension tables keyed by SKU, store and till; dashboards query the semantic layer so both finance and stores see the same definition of 'revenue'.
Operational insight
Benchmark targets this is engineered to hold. Stats are framed as targets and SLAs — not fabricated outcomes.
- < 5 min
- Data freshness for near-real-time views
- ~30%
- Report build time saved by governed self-serve
- 99%+
- Row-level quality checks passing before publish
Discuss your dashboards & analytics needs
Tell us about your goals and we will help you understand the right next step — with no obligation.