Most organisations feel the pain of integration first because unreliable or slow data movement is immediately visible. It is reasonable to start there, but governance should not lag far behind. Faster data movement without access control, lineage and quality checks simply moves unreliable information around more quickly. We usually design the integration and governance layers together, then phase implementation around the most urgent business need.
Engineering trusted data for what comes next
Every dashboard, automated workflow and AI application depends on data that moves reliably and can be trusted when it arrives. Ardent’s engineers design and run the integration, governance and quality foundations that make that possible.
TRUST AND PROOF POINTS
Since 2008
Engineering data platforms and pipelines for organisations worldwide.
2m+ hours
Engineering delivered across data and software projects.
Platform agnostic
Expertise across data integration, governance and quality.
ISO 27001
Information security supported by independently certified processes.
WHY DATA ENGINEERING
Move data reliably. Trust it when it arrives.
Databricks, Snowflake, Redshift, Fabric and BigQuery are only as useful as the data reaching them. Integration engineering gets data where it needs to be, at the right time and in the right shape. Governance and quality make sure the right people can use it and the numbers are reliable.
Modern tooling has turned much of this work into repeatable engineering: Kafka and Debezium support real-time movement, Airflow coordinates workflows, Fivetran and Airbyte simplify ingestion, while dbt, Unity Catalog, Collibra, Alation, Monte Carlo and Great Expectations add modelling, governance, observability and testing.
Ardent’s role is to choose the right combination for your environment and make the pieces work together - without adding complexity simply for the sake of tooling. We build data foundations that can scale across reporting, automation and AI.
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Real-time and batch data movement
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Orchestration and change data capture
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Governance, lineage and discovery
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Automated quality and observability
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Platform-agnostic engineering
WHAT WE DELIVER
Integration engineering that keeps data moving
We design the pipelines, orchestration and transformation layers that move data reliably between systems, platforms and business workloads.
Real-time streaming
Design event-driven pipelines with Kafka, decoupling source and consuming systems so data can move reliably in real time as volumes grow.
Workflow orchestration
Build Airflow workflows with clear scheduling, dependencies, retries and monitoring, so complex pipelines run predictably across systems, tools and teams.
Change data capture
Use Debezium and Kafka to stream row-level database changes, keeping downstream platforms current without relying on slow overnight batch processing.
Open-source ELT
Implement Airbyte connectors for flexible, self-hosted ingestion, giving teams more control over how and where data movement runs.
Managed connectors
Use Fivetran for managed ingestion where speed and low operational overhead matter, reducing the need to maintain connector infrastructure internally.
Transformation and data modelling
Build version-controlled, tested transformations in dbt, turning raw ingested data into documented, analysis-ready models with clear lineage.
Governance and quality that makes data trustworthy
We combine access control, discovery, lineage, observability and testing so teams know where data came from, who can use it and whether it can be trusted.
Native lakehouse governance
Implement Unity Catalog for unified access control, lineage and audit across Databricks data and AI assets, with governance applied consistently across cloud environments.
Enterprise data governance
Build business glossaries, policy controls and stewardship workflows in platforms such as Collibra, extending governance across teams, systems and business domains.
Data discovery and cataloguing
Implement Alation and active metadata approaches that help users find, understand and confidently reuse trusted data without relying on manual support from data teams.
Data observability
Configure Monte Carlo to monitor freshness, volume and schema changes, using lineage to show where incidents originated and which downstream assets are affected.
Pipeline-embedded quality testing
Embed Great Expectations checks directly into pipelines, automatically failing known data-quality rules before bad data reaches reports, automation or AI.
EXPLORE OUR SERVICES
Choose the service that fits your goals
Our services turn strong data foundations into better reporting, reliable automation, production AI and day-to-day operational resilience.
PATTERNS WE DELIVER
Proven patterns for modern data estates
Different tools solve different problems, but the underlying delivery patterns are repeatable. We adapt them around your platforms, workloads and operating model.
Governed ELT ingestion at scale
Replace hand-written extraction scripts with managed or open-source connectors, orchestrated and monitored around your sources, volumes and governance requirements.
Enterprise-wide governance and stewardship
Create shared catalogues, business glossaries, access controls and stewardship processes across platforms, so trust does not depend on where the data happens to sit.
Automated data observability and quality
Combine anomaly detection with code-based validation, catching both unexpected changes and known rule failures before they reach business users.
HOW WE WORK WITH YOU
A clear path to value
We follow a proven three-stage approach to improve how data moves, how it is governed and how reliably it supports the rest of your business.
01
Assess and design
Audit your current pipelines, platforms, catalogues and quality issues, then define the right integration and governance architecture around your sources, workloads, priorities, risk and timeline.
02
Build and implement
Build pipelines and governance together, embedding testing, lineage and monitoring from day one, then move from legacy processes through controlled cutovers with minimal disruption.
03
Optimise and run
Tune pipeline performance, alerting and stewardship, then establish ongoing monitoring, support and continuous improvement so data remains reliable, governed and useful as requirements evolve.
DATA ENGINEERING FAQS
Frequently asked questions