A leading international IT company asked us to replace its fragmented reporting routines with a modern analytics stack. In four months, we delivered an end-to-end solution that automates data ingestion, centralizes storage, and turns raw numbers into real-time insights.
• 6 Data Analysts
• 2 System Analysts
• 2 DWH Architects
• 1 DevOps Engineer
• Give every business unit instant, self-service access to accurate data.
• Eliminate manual preparation errors and accelerate decision-making.
• Upskill employees to use the new analytics toolkit confidently.
• Design a future-proof DWH able to scale with data growth.
• Automate extraction and transformation of dozens of heterogeneous sources.
• Protect sensitive data via granular, role-based access.
• Keep query latency low even at terabyte scale.
Our Approach
1. Discovery & Architecture (3 weeks)
• Benchmarked peer-group infrastructures and best-in-class tooling.
• Defined target state, data domain map, and SLAs; issued a detailed technical specification for the DevOps team.
• Technology stack selected:
– Greenplum for petabyte-scale historical storage.
– ClickHouse for sub-second operational analytics.
– Apache Airflow for orchestration.
– Superset for BI dashboards.
2. ETL & DataOps (6 weeks)
• Built 15+ production-grade Airflow DAGs, each encapsulating extraction, transformation (Python + Pandas), and loading logic.
• Implemented automated monitoring, retries, and alerting to guarantee data freshness and zero manual-processing errors.
3. Analytics & Visualization (4 weeks)
• Modeled subject-area data marts and optimized SQL for high-volume queries.
• Delivered a library of interactive Superset dashboards covering product, finance, and customer success KPIs.
• Ensured sub-second response times by routing real-time workloads to ClickHouse.
4. Roll-out & Enablement (2 weeks)
• Configured RBAC, single sign-on, and environment isolation via Docker & k9s.
• Ran four enablement workshops for >20 sales, marketing, and operations specialists.
• Established GitLab-based CI/CD for pipeline and dashboard versioning.
• 100 % elimination of manual data-handling errors.
• 15+ automated Airflow workflows running on schedule, freeing analysts’ time.
• Query execution accelerated by 3–5× thanks to ClickHouse optimizations.
• Single source of truth adopted across departments; decision cycles shortened from days to minutes.
Deliverables
✓ End-to-end DWH architecture (Greenplum + ClickHouse)
✓ Fully automated ETL pipelines (Airflow, Python, Pandas)
✓ Role-based security model and CI/CD setup (GitLab)
✓ Production-ready Docker/Kubernetes deployment (k9s)
✓ Suite of Superset dashboards with drill-downs and alerts
✓ User training, playbooks, and ongoing support framework
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