NCompas builds modern data lakehouses, real-time streaming pipelines, observable orchestration, and AI-ready data infrastructure on Databricks, Microsoft Fabric, Snowflake, dbt, and Kafka — so your organisation stops drowning in data and starts deciding with it.
Why NCompas
The best data engineering is invisible — until something breaks, and the self-healing kicks in before anyone notices. That's what we build.
We don't sell a platform licence. We design the right architecture for your data volume, latency requirements, budget, and team — whether that's Databricks, Fabric, Snowflake, or a best-of-breed open-source stack.
Quality checks, PII detection, and lineage tracking are designed into every pipeline from sprint one — not added after the data team loses trust in the output. Governance is architecture, not policy.
We build data platforms that are designed for AI workloads — feature stores, vector databases, training pipelines, and ML monitoring — so adding AI capabilities doesn't require re-engineering the data layer.
Every pipeline we ship has monitoring, alerting, lineage, and SLA tracking from day one. When something breaks at 3am, you know before your business does — and the self-healing logic kicks in automatically.
We start every engagement by mapping pipeline outputs to business decisions, not technical requirements. Every table we build has a documented business owner, SLA, and downstream consumer — so your data has purpose, not just volume.
We don't create dependency. Every engagement includes documentation, dbt model explanations, runbook creation, and paired working sessions so your team owns the platform when we leave — and can extend it without us.
Six core capabilities
Every capability is production-grade, documented, and designed to be owned by your team after we deliver it — not maintained by a consultant on retainer.
Implement the modern lakehouse pattern — Delta Lake, Apache Iceberg, or Apache Hudi — on Microsoft Fabric, Databricks, or AWS. A single architecture that serves BI, SQL analytics, and ML workloads without the cost and complexity of separate systems.
We design and build batch, micro-batch, and streaming data pipelines that are observable, resilient, and self-healing. From raw ingestion to curated gold layers — orchestrated with Apache Airflow, Prefect, or Azure Data Factory and monitored with end-to-end lineage.
Implement the modern lakehouse pattern — Delta Lake, Apache Iceberg, or Apache Hudi — on Microsoft Fabric, Databricks, or AWS. A single architecture that serves BI, SQL analytics, and ML workloads without the cost and complexity of separate systems.
Build event-driven data architectures that deliver sub-second latency for operational analytics, fraud detection, and live dashboards. Apache Kafka, Azure Event Hubs, AWS Kinesis — with stateful processing in Apache Flink or Spark Structured Streaming.
Data that analysts don't trust gets ignored. We embed data quality checks using Great Expectations or dbt tests at every pipeline stage, instrument data observability with Monte Carlo or Azure Purview, and implement governance policies that scale.
Migrate from SQL Server, Oracle, Teradata, or on-premises warehouses to Snowflake, Azure Synapse, or BigQuery. We run schema translation, data migration, and query optimisation — with a validated cutover that keeps your business running throughout.
ML models are only as good as the data they learn from. We build the data infrastructure that powers your AI programmes — feature stores, training data pipelines, vector databases for RAG, and data versioning systems that make models reproducible and auditable.
Platforms & tools we deploy
How we work
Six phases that take you from legacy pipelines to a governed, AI-ready data platform — with a production data product delivered every two-week sprint.
We map your current sources, schemas, pipelines, and data consumers. We identify gaps in quality, governance, and observability — and baseline the metrics that the engagement will move.
We design the target state — lakehouse tiers, pipeline patterns, orchestration topology, and governance model. Platform selection is driven by your workload profile, budget, and team capability, not vendor preference.
Two-week sprint to build the core infrastructure: lakehouse storage, ingestion framework, orchestration, CI/CD for data pipelines, and the first medallion layer. Proves the architecture works in your environment before we scale.
Iterative sprints building out source connectors, transformation models, data quality tests, and documentation. Each sprint delivers a business-valuable data product — not just infrastructure. Stakeholders see results every two weeks.
Implement the data catalogue, lineage tracking, PII discovery, access controls, and data quality dashboards. Governance is the last layer of the stack — but it's designed from the first day of the engagement.
Production cutover with runbooks, dashboards, and alerting configured. Three-week hypercare period. Knowledge transfer sessions for your team. Monthly health checks for six months post-launch.
Industry results
Four industries, four distinct data engineering challenges, four transformations. Production metrics — not projected outcomes.
The Challenge
Risk models running on T+1 batch data. Fraud detection accuracy limited by stale features. Regulatory reporting taking 3 weeks per quarter with manual reconciliation and no auditable lineage.
What We Built
Real-time CDC pipelines from core banking systems into a streaming feature store. Fraud signals updated every 15 seconds. Automated regulatory report generation with full lineage from source to report cell, auditable in Microsoft Purview.
Risk models running on T+1 batch data. Fraud detection accuracy limited by stale features. Regulatory reporting taking 3 weeks per quarter with manual reconciliation and no auditable lineage.
Patient data siloed across EMR, labs, imaging, and billing systems. Clinical analytics team spending 70% of time on data prep. HIPAA compliance managed manually with no systematic PII discovery.
200M+ daily events from web, app, POS, and supply chain. Personalisation models running on 48-hour-old data. Inventory analytics in Excel. No single customer view across channels.
IoT sensor data from 400 machines stored in time-series DBs with no connection to ERP, quality, or maintenance systems. Predictive maintenance a PoC only. Reporting done manually in Excel.
of data created globally every day — yet most organisations are making decisions from less than 1% of it. The gap between data collected and data used is where competitive advantage hides.
of enterprises have reached data engineering maturity — meaning 68% are still running on fragile, manual, undocumented pipelines that can't support the AI workloads coming in the next 18 months (TDWI, 2024).
average annual cost of poor data quality for enterprise organisations — in wasted analyst time, incorrect decisions, and failed AI models trained on bad data (Gartner, 2024).
the year 80% of enterprise ML models will require real-time feature stores and streaming data infrastructure — organisations building batch-only pipelines today will face a full rebuild in under 24 months (Gartner).
Insights from real-world engineering, cloud, and AI leaders - helping you make better decisions, faster.
We're putting the finishing touches on this. Check back soon for in-depth insights.
Start with a free Data Engineering Assessment — we'll audit your current pipelines, identify the top three quality, observability, and governance gaps, and show you what a modern lakehouse architecture would look like for your workloads.