Data and cloud modernization

Lakehouse + streaming + governance with FinOps discipline for measurable time‑to‑data and unit cost gains.

Modern data foundation

We design a lakehouse that supports both analytical and operational workloads with unified governance. The architecture emphasizes low‑latency ingestion, cataloging, discoverability and secure sharing. Data products are versioned and owned, with SLAs and documentation that make reuse safe. We prioritize incremental delivery: start with high‑value domains, backfill history pragmatically, and automate quality checks so producers and consumers trust the system.

Lakehouse and pipelines diagram

Streaming and event‑driven

Real‑time data unlocks new experiences—personalization, anomaly detection, operational dashboards. We implement CDC pipelines, schema registry, and contracts so downstream services evolve safely. Streaming reduces batch windows and aligns the organization around a shared event model. We measure freshness and time‑to‑data so improvements are visible and compounding over time.

Streaming and event bus illustration

Governance and privacy by design

Governance accelerates when it is embedded in tooling. We deploy catalogs, policy engines and lineage so access requests are quick, compliant and auditable. PII is protected with reversible and irreversible techniques as appropriate, and retention policies are enforced automatically. These controls reduce risk while making data easier to use.

Cloud migration factory and FinOps

We rationalize estates, map dependencies and choose migration wave plans that minimize downtime. FinOps practices—allocation, budgeting, right‑sizing—make unit costs transparent and drive responsible growth. Savings are reinvested into AI use cases, compounding ROI. Dashboards show cost per workload, performance and reliability together, enabling better trade‑offs.

Operating model and enablement

To sustain momentum, we establish product‑oriented data teams with clear ownership and paved roads for ingestion, quality and serving. Chapters for data platform, governance and SRE keep standards coherent while allowing domains to move quickly. We coach teams on data contracts, semantic layers and testing so new use cases land safely without central bottlenecks.

What we deliver

  • Lakehouse design and implementation with governance built-in
  • Streaming pipelines for fresh data and event-driven architectures
  • Data quality, lineage, privacy and policy controls
  • Cloud migration factory and FinOps cost optimization

Business case

  • Spend shift: Cloud, data and edge services continue to grow at double digits (IDC).
  • Unit cost: FinOps and platform standardization reduce run costs 20–35% that can fund AI programs.
  • Time-to-data: Streaming + governance accelerates analytics and product experimentation.

References

Case example

Lakehouse and streaming modernization: Consolidated data platforms, introduced CDC streams and governance. Results: 35% lower run cost, time-to-data down from days to hours, and product experimentation cadence doubled.

KPIs: unit cost per TB, SLA uptime, pipeline freshness, consumer satisfaction.

Proof and measures

Time-to-data, reliability, and unit costs. FinOps savings reinvested into AI workloads to compound value.