SolutionDatabase & Data Systems

Platforms & Data

Operational and analytical data layers with clear ownership

Schemas, ingestion, and serving paths are designed so applications and analytics consume consistent entities - not parallel copies with silent drift.

Source analysis with honest callouts when governance must come first.

Batch, micro-batch, or streaming pipelines matched to freshness needs.

Documentation so engineers and analysts know which table to trust for which question.

On this page

Overview

We reduce redundant copies of the same business entities across databases, files, and spreadsheets by making ingestion, modelling, and access explicit.

Work is sequenced with dependent initiatives (for example reporting or integrations); we flag prerequisites early.

Core services

Components we combine and sequence based on your constraints and timeline.

Modelling

Conceptual and physical models, keys, and slowly-changing strategies.

Ingestion

ELT/ETL, CDC, APIs, files - idempotent loads and data contracts.

Serving

Access patterns for apps, reverse ETL, and analyst tools.

Quality

Checks, alerting, lineage basics, and ownership metadata.

Typical flow

A reference sequence; we adapt depth and gates to your organisation.

#StageWhat happens
01Inventory

Sources & consumers

Systems, SLAs, and today’s pain points with data.
02Blueprint

Architecture

Warehouse vs lakehouse, security zones, and cost guardrails.
03Build

Pipelines

Incremental delivery of domains (e.g. customers, orders).
04Govern

Operate

On-call expectations, change control, and roadmap for new sources.

Who we work with

Companies modernising data sprawl - from SMEs consolidating stacks to enterprises unifying divisions.

Infrastructure

We implement on AWS, Azure, GCP, Snowflake, Databricks, and self-managed stacks as your policies require.

Deliverables

Concrete outputs, documented and handed over with the build.

  • Source analysis and modelling
  • Pipelines batch or near-real-time as required
  • Access patterns for apps and analysts
  • Basic data quality checks

Engagement model

Partnership patterns we document in the SOW or master agreement.

  • -Often paired with analytics or dashboard projects
  • -Governance workshops with your data owner

Commercial model

Source count, latency requirements, governance, and consumption patterns set scope. We quote after discovery.

We start with a focused discovery (paid or unpaid, depending on complexity). You receive a written scope or SOW: milestones, acceptance tests, and a defined change process. NDAs and your procurement steps are routine.

Fixed scope

Documented requirements, milestones, and acceptance criteria. Delivery targets an agreed release or go-live.

When it applies

A bounded domain or source set with clear freshness and access rules.

Phased programme

Successive increments with checkpoints, integrations, and change control as scope evolves.

When it applies

Many pipelines, strict SLAs, or cross-functional data products.

Ongoing partnership

Retained monthly capacity for maintenance, incremental features, releases, and operational support.

When it applies

Operating pipelines, onboarding new sources, and quality remediation over time.

Fees are quoted per engagement after discovery. Third-party cloud, licensing, and usage charges are usually billed to your accounts unless we agree otherwise.

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