FIG. BIG-

SERVICES

Big Data & Advanced Analytics

Stage: Services

Turn sprawling data estates into a strategic asset. We design cloud-native platforms, governance, and MLOps that scale intelligence across the enterprise

12 min readLast updated: Q3 2026

Key Takeaways

Cloud Platforms
Target Architecture Modernization
Assess & Blueprint FinOps

§01Technology Stack

Cloud Platforms

AWS Azure GCP

Data & Analytics

Snowflake BigQuery Databricks

Data Engineering / Orchestration

Kafka Flink Airflow Dagster dbt Iceberg / Hudi

Infrastructure / DevOps

Terraform

Data Governance / Security

Collibra Alation Ranger Lake Formation

§02A Disciplined Approach to De-Risking Innovation

Target Architecture Modernization

Tooling rail: lakehouse • Iceberg/Hudi • dbt • Terraform/IaC Edge: Blueprints align performance with FinOps guardrails and portability.

Resilient Data Pipeline Engineering

Tooling rail: Airflow/Dagster • CDC • schema registry • observability Edge: SLA-backed pipelines with lineage and error budgets.

Governance, Security & Sovereignty

Tooling rail: ABAC/RBAC • tokenization • KMS/HSM • DLP Edge: Residency patterns for EU/KSA/India embedded in access tiers.

Analytics, ML Ops & LLM Ops

Tooling rail: MLflow/W&B • feature stores • Arize/WhyLabs Edge: Qμβrix™ sets baseline SLOs for LLM latency/cost/quality.

Executive BI & Decision Workspaces

Tooling rail: Power BI/Tableau/Looker • semantic layers • KPI maps Edge: HoloΔense™ “decision rooms” with narrative analytics.

Data Monetization & Sharing

Tooling rail: data products • contracts • marketplace connectors Edge: Governed monetization with revenue-share hooks and audit trails.

§03A Blueprint for Scalable Intelligence

Assess & Blueprint FinOps

KPIs set pre-build.

Build & Migrate ZTNA

Phased cutovers; zero-trust patterns

Operationalize & Innovate SRE for Data

Runbooks and enablement live

§04From Data Chaos to Strategic Clarity

Cloud-Agnostic Architecture Expertise
FinOps & TCO-First Thinking
Enterprise-Grade Security & Compliance
Strong Patterns for LLM-Ready Data
Repeatable Playbooks & Enablement

§05Assurance Strip

Assurance Strip

Residency: Ensuring that data, models, and workloads remain within designated geographic or regulatory boundaries
Encryption: Applying strong encryption standards for data at rest, in transit, and during processing to protect sensitive information
Lineage: Tracking the origin, transformation, and usage of data and model artifacts for full transparency and auditability
Cost Guardrails: Implementing financial thresholds and monitoring mechanisms to prevent overspending on AI initiatives

Boardroom Kit

Lakehouse Blueprint: Architectural framework defining data storage, processing, and governance patterns for scalable AI and analytics
Data Contract Spec: Formal agreements specifying schema, quality, access, and usage rules for datasets across teams
Lineage Policy: Organizational guidelines for tracking, documenting, and auditing the flow and transformation of data
FinOps Guardrails: Financial management controls and best practices for cost optimization in cloud and AI operations

§06Enterprise Readiness & IT Integration

Can you modernize in place?

Yes—retain components where sensible; replace only where value demands it.

Residency, access segmentation and encryption per jurisdiction.

Executive KPIs, role-based UX and embedded enablement

What ensures adoption?

Target KPI attainment and acceptable risk/operational cost profile.

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