Interactive Demo | Data Engineering

Data Platform Control Tower

A portfolio-grade control plane for monitoring ingestion, freshness, lineage, data quality, backfills, and stakeholder-ready metrics across a modern analytics platform.

Fresh data assets 0

Assets that cleared freshness checks in the selected window.

Quality pass rate 0%

dbt-style checks passing across row counts, uniqueness, nulls, and accepted values.

Blocked dashboards 0

Business-facing reports held back because upstream quality checks failed.

Backfill hours 0h

Estimated recovery time for active replay and repair jobs.

Orchestration

Pipeline run states.

Lineage View

From source APIs to trusted marts.

The control tower shows where data is coming from, what transformation layer it is in, and whether the resulting metrics are safe for stakeholders.

Sources

Stripe API PayPal API GA4 Export SQL Server CRM Product DB

Raw Landing

S3 raw files API payload archive Event partitions

Bronze

append-only ingestion schema drift capture source row counts

Silver

deduped records normalized keys currency and timezone rules

Gold

revenue mart marketing attribution mart customer health mart

Consumers

BI dashboards AI reporting layer finance exports

Data Quality

Checks that decide whether data ships.

Each check has an owner, severity, affected asset, and action. In production this maps to Airflow/Dagster run state, dbt tests, warehouse audit tables, and alert routing.

Implementation Pattern

Code-level signals.

This is the kind of deterministic engineering layer that makes dashboards, AI summaries, and stakeholder reports reliable.

Airflow/Dagster-style asset gate

if freshness_minutes > sla_minutes:
    block_asset("gold_revenue_mart")
    notify(owner="data-platform")

if row_delta_pct > 12:
    create_incident(
        severity="high",
        check="source_row_count_delta"
    )

SQL reconciliation check

select
  source_day,
  sum(source_amount) as source_total,
  sum(mart_amount) as mart_total,
  abs(sum(source_amount) - sum(mart_amount)) as variance
from reconciliation.daily_payments
group by source_day
having variance > 100;