Defined scope, defined outcome, defined price

Data Warehouse Operations

The month after go-live is when data platforms quietly start to rot. Pipelines fail overnight and nobody notices until a Monday meeting. Costs creep because nobody owns the warehouse bill. Small schema changes break reports and the fix takes three days because there is no lineage.

Our operations solution puts explicit observability and accountability around a running platform — whether we built it or inherited it.

What we put in place

Metric definition

We start by agreeing what "healthy" means for your platform: pipeline freshness targets per dataset, acceptable processing windows, data quality thresholds, throughput baselines and cost budgets. Without agreed targets, monitoring produces noise rather than signal.

Observability stack

Grafana, Prometheus and cloud-native monitoring configured against those metrics, with dashboards that distinguish between "a job failed" and "the business is affected". Data quality checks are instrumented as first-class metrics alongside infrastructure health, because a pipeline that succeeds while loading wrong data is worse than one that fails loudly.

Alerting that people trust

Alerts route by severity to the right on-call channel — Slack, Teams, email or PagerDuty — with thresholds tuned to avoid the fatigue that makes teams ignore notifications. Every alert links to a runbook describing the diagnosis and the fix. Alerts that fire repeatedly without action get removed or corrected.

Continuous review

Monthly operational review covering incident trends, SLA attainment, cost movement and a prioritised improvement backlog. Quarterly architecture review to catch the structural problems that monthly firefighting never surfaces.

Typical outcomes

• Data quality incidents found by monitoring rather than by business users
• Mean time to detection reduced from days to minutes
• 15–35% reduction in platform run cost within the first two quarters
• Documented runbooks that let your own team take over operations

ETL Modernisation

Many organisations are carrying an ETL estate built over a decade by people who have since left: hundreds of jobs in a legacy tool, undocumented, with logic embedded in the GUI where nobody can diff it. Licence renewal is the usual trigger for doing something about it.

We modernise that estate into version-controlled, testable transformation code — usually dbt on top of the warehouse, orchestrated by Airflow — while keeping the business outputs identical.

What changes

From GUI to code

Transformation logic moves into SQL and Python held in git. That single change brings code review, branching, rollback, automated testing and a real audit trail of who changed what and why. It also removes the licence dependency that made the estate expensive in the first place.

From ETL to ELT

Rather than transforming data in a separate engine before loading, we land raw data in the warehouse and transform it there, using compute you are already paying for. This is faster, cheaper at scale, and keeps the raw layer available for reprocessing when business rules change.

Frameworks over one-off jobs

We build a reusable ingestion framework driven by configuration, so adding a new source is a metadata entry rather than a new pipeline. Standard patterns cover full loads, incremental loads, CDC, file ingestion and API polling.

Testing and contracts

Every model gets tests: uniqueness, referential integrity, accepted values, freshness and business-rule assertions. Schema contracts between producers and consumers mean upstream changes break a CI pipeline rather than a Monday report.

Typical outcomes

• Legacy ETL licence costs eliminated or substantially reduced
• New source onboarding time cut from weeks to days
• Complete lineage from source column to report field
• Transformation logic that a new hire can read and change safely

Real-Time & Streaming Analytics

Some decisions cannot wait for tomorrow's batch: fraud scoring on a card transaction, network fault detection, stock availability across stores, courier routing. For those, we build streaming pipelines rather than stretching a nightly load into something it was never designed to be.

We are also honest about when you do not need this. Real-time architecture costs meaningfully more to build and operate, and a large fraction of "we need real time" requirements are satisfied by a fifteen-minute micro-batch. We will tell you which situation you are in.

Architecture

Ingestion

Apache Kafka, Amazon Kinesis, Google Pub/Sub or Azure Event Hubs, with change data capture from operational databases using Debezium or equivalent tooling so transactional systems are not disturbed.

Processing

Spark Structured Streaming, Flink or cloud-native stream processors handle windowing, joins against reference data, enrichment and aggregation, with carefully chosen delivery semantics and explicit handling for late and out-of-order events.

Serving

Results land in a lakehouse for historical analysis and in a low-latency store for operational consumption, with the same governed metric definitions applied to both so streaming and batch numbers reconcile.

Operations

Streaming platforms fail differently from batch ones. We build in consumer lag monitoring, backpressure handling, replay capability from retained offsets, and dead-letter handling for malformed events.

Typical outcomes

• Sub-minute latency from source event to operational dashboard
• Fraud and anomaly detection acting on live rather than day-old data
• Batch and streaming figures that reconcile because they share definitions
• Replay capability that turns a bad deploy into a recoverable event

Data Governance & Quality

Governance programmes fail when they are run as documentation exercises. A glossary in a shared drive changes nothing. Governance works when definitions, ownership and quality rules are enforced by the same pipelines that move the data — so non-compliance produces a failed build rather than a policy breach nobody notices.

What we implement

Classification and cataloguing

Automated discovery and classification of sensitive data across the estate, propagated through lineage so a masked column stays masked in every downstream table that derives from it. Catalogue populated from code rather than maintained by hand.

Ownership that means something

Every dataset gets a named business owner and a technical steward, recorded in the catalogue and surfaced in the BI tool. When a number looks wrong, users know exactly who to ask — and owners get notified when quality checks on their data fail.

Quality as code

Business rules expressed as executable tests in the pipeline: valid ranges, referential integrity, completeness thresholds, cross-system reconciliation. Results tracked over time so quality trends are visible rather than anecdotal.

Retention and lifecycle

Retention schedules implemented as automated policies, including the deletion obligations that come with GDPR, PDPA and equivalent regional legislation — alongside the archival requirements that regulators impose in the opposite direction.

Typical outcomes

• A single certified definition for each business-critical metric
• Sensitive data known, tagged and protected consistently across the estate
• Quality trends visible to owners, with regressions caught in CI
• Audit evidence produced from the platform rather than assembled manually