Insights &
Architectures.
Long-form engineering insights, case studies, and deep-dives into the future of autonomous data infrastructure.
Showing 28 of 28 insights
The Autonomous Enterprise Architecture: Four Layers, and Why AI Programmes Stall Without Them
Most enterprise AI programmes stall on architecture, not models. The four layers of autonomous enterprise architecture, and why sequence decides results.
An Enterprise Readiness Framework for Agentic AI Systems
Score agentic AI readiness across five dimensions — data, tooling, guardrails, evaluation, and operating model — before you commit to an agent build.
The EU AI Act and Your GenAI Operating Model: A Compliance-Ready Blueprint
What the EU AI Act asks of GenAI deployers, and the operating model that satisfies it: named roles, four lifecycle gates, and evidence at each step.
Microsoft Fabric vs Databricks: An Architect's Decision Framework
A vendor-neutral framework for Microsoft Fabric vs Databricks: capacity and DBU economics, Purview and Unity Catalog, interop, and when to run both.
S/4HANA by 2027: Protecting the Analytics Estate During Conversion
An ECC to S/4HANA conversion changes the tables, extractors, and embedded analytics your reporting depends on. Here is the continuity playbook.
SAP BW to Datasphere Migration: The 2027 Decision Guide
SAP BW 7.5 mainstream maintenance ends 31 December 2027. An honest guide to the four realistic paths and how to decide between them.
Designing Production Agentic AI Systems: Architecture Patterns, Guardrails, and Evaluation
How production agentic AI is built: the agent loop, typed tool contracts, guardrail config, evaluation harnesses, and the gates that grant autonomy safely.
Data Platform FinOps: Controlling Databricks, Snowflake, and AI Compute Costs
A data platform FinOps playbook: cost attribution, right-sized compute, storage tiering, query tuning, and GPU controls for Databricks and Snowflake.
Global Beverages: replacing a 70-FTE harmonisation process with a governed lakehouse
Unolabs replaced a 70-FTE manual harmonisation process for a global beverages leader with a governed AI lakehouse, validating £3.3M in savings.
Fortune-100 Retailer: engineering the data separation behind a cloud-native demerger
Unolabs engineered the sovereign cloud platform behind a Fortune-100 retail demerger, with ≈$2.5M in migration savings validated with client finance.
Global Agri-Science: consolidating four SAP ECC instances onto one HANA platform
Unolabs consolidated four SAP ECC instances into one governed HANA platform for a global agri-science enterprise, cutting onboarding effort by 40%.
LLM Orchestration: Multi-Agent Patterns for Reliable Enterprise Workflows
Supervisor, pipeline, debate, blackboard or hierarchical? A decision guide to multi-agent LLM orchestration patterns — and when one model call still wins.
Global Beauty Leader: engineering a federated data fabric for global growth
Unolabs built a domain-aware data fabric with federated governance and an MDM hub for a global beauty leader, cutting analyst reconciliation work.
Securing Enterprise RAG: PII Masking, Token-Level Access Control, and Boundary-Aware Retrieval
How to secure enterprise RAG: PII masking, chunk-level access control, boundary-aware retrieval, and audit trails regulators can follow.
Global Automotive Leader: a multi-domain platform for connected vehicle data
Unolabs unified telematics, CRM and sales signals for a global automotive leader into one governed multi-domain platform with auditable lineage.
Industrial Engineering: benchmarking a product portfolio with explainable AI
An industrial engineering firm gained feature-level competitive priorities from an explainable benchmark of product, pricing and sentiment data.
Lakehouse Performance Tuning: Optimising Multi-Petabyte Databricks Environments
Beyond Z-Order and Liquid Clustering: how to tune a multi-petabyte Databricks lakehouse in the order that pays — layout, then skipping, then compute.
Large Automotive Organisation: explainable lead scoring for offline conversion
A large automotive organisation replaced intuition-led lead selection with explainable account scoring and a continuous sales feedback loop.
Kafka-to-Lakehouse Streaming Patterns for Global Scale
Implementation patterns for Kafka-to-lakehouse pipelines: exactly-once ingestion, schema evolution, stream processing, and millisecond serving.
Data Mesh vs. Fabric: Selecting the Right Architecture for 2026
Data mesh versus data fabric across ten dimensions: what each one fixes, how each one fails, and a decision framework for choosing or combining them.
Governance as Code: Automating Data Quality in the Cloud
Turn data quality rules, access policies, and data contracts into version-controlled artefacts that CI/CD enforces before bad data reaches consumers.
Federated Governance for Data Mesh: Ownership Without Anarchy
Federated governance for data mesh: how domain ownership, global policy standards and computational governance scale without a central bottleneck.
Adaptive Data Governance: Policy Automation That Keeps Pace With Change
Adaptive data governance in practice: automated policy enforcement, ML-driven data classification, and continuous compliance that keeps pace with change.
AI-Ready Data Foundations: The Governance Work That Comes First
AI-ready data foundations decide whether models scale: quality at source, resolved entities, lineage to prediction, and inventories a regulator can audit.
Platform Engineering and DataOps Culture: Turning Tool Sprawl into a Paved Road
How platform teams turn data tool sprawl into a self-service platform: seven pillars, six capabilities, and the operating model that makes adoption stick.
Data Observability & Quality Management
Data observability explained: five monitoring dimensions, three pipeline quality gates, a worked data contract in YAML, and a four-phase rollout plan.
Precision Carbon Intelligence for Global Enterprises
Enterprise carbon accounting as a data engineering problem: GHG Protocol scopes, CSRD and SB 253 pressure, and an audit-ready pipeline.
AI-Powered Autonomous Data Operations: What to Automate, and What to Keep Under Review
Autonomous data operations explained: six AI DataOps capabilities, five levels of autonomy, and the guardrails that decide what may run without a human.
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