Capabilities/Build/Data Platform & Engineering
BuildLakehouse · Streaming · Governance

Reliable pipelines. Trustworthy data.

We design and operate data lakehouse platforms on AWS and Databricks for organisations whose business reports are delayed because pipelines cannot be relied upon, or whose analysts spend more time cleaning data than analysing it. As a Databricks Delivery Partner and AWS Partner, our recommendations are based on use case, not on vendor preference.

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When this is needed

Three signals that this is the right next step.

01 / Pipelines keep breaking Business reports are consistently delayed because data pipelines cannot be relied upon.
02 / Cleaning instead of analysing The analytics team spends more time cleaning and fixing data than analysing it.
03 / Data not ready for ML Data scientists cannot work productively because the data they need is not available in a usable format.
Reference architecture

The shape of a modern data platform.

Illustrative reference architecture. The exact tooling, scale, and integration points are tailored to your organisation’s data sources, regulatory context, and existing investments , we do not deploy a template.

01 / SOURCES

Current sources

OLTP, SaaS, files

02 / INGEST

Reliable ingest

Batch, stream, SLAs

03 / LAKEHOUSE

Medallion layers

Tested data zones

04 / SERVE

Serving layer

BI, ML, apps, ETL

05 / CONSUME

Decisions get made

Dashboards, models, ops

CROSS-CUTTING

Governance · lineage · observability · SLOs · on-call , applied to every layer above, not bolted on later

Sources & ingestTransactional databases, SaaS APIs, event streams, files. We use Apache Spark, AWS Glue, dbt, and Apache Airflow for batch and ELT; Apache Kafka and Amazon Kinesis for real-time streaming where the use case requires it.
LakehouseData lakehouse architecture on AWS and Databricks. Layered from raw to cleaned to consumable, so the business serving layer is built on tested, governed data , not on whatever the latest pipeline produced.
ServeOne layer feeding BI, machine learning, and operational integrations. Engineered for measurable reliability, not best-effort delivery.
GovernanceData cataloging, lineage tracking, quality rules, and access control , implemented incrementally, starting from the most critical data domain rather than as a big-bang initiative.
Engineering principles

The platform filter. Reliable by design, selective by default.

We merge platform scope and engineering restraint into one operating model, so every build decision improves reliability without adding unnecessary operational weight.

01 / ModelLakehouse by domain

Build the AWS or Databricks foundation around governed data domains instead of one giant table that becomes impossible to evolve.

02 / ShipPipelines as production software

ETL and ELT jobs move into version-controlled Spark, Glue, dbt, or Airflow workflows with tests, review history, and measurable dataset SLAs.

03 / ObserveReliability before scale

Monitoring, alerting, lineage, and workload profiling come before larger clusters, so performance fixes solve the cause instead of hiding it in spend.

04 / GovernControls inside the workflow

Cataloguing, lineage, quality rules, and access control are implemented incrementally in tooling, not left as documentation that changes no behaviour.

05 / ChooseLatency earns its complexity

Streaming, legacy migration, and parallel-run validation are chosen when the business SLA requires them, not because newer architecture sounds better.

Merged operating model

Every platform choice has to improve trust in the data or reduce operating risk.

We combine lakehouse design, pipeline engineering, streaming, governance, and legacy migration with the anti-patterns we deliberately avoid: paper-only governance, notebook-to-production shortcuts, oversized universal tables, unnecessary real-time systems, and brute-force compute fixes.

Case studies & outcomes

Two engagements. Both measurable.

01
Retailer · more than 500 stores

Same-day operational visibility from point of sale to dashboard.

Context
A retailer operating more than 500 stores required same-day operational visibility into store performance, but existing pipelines could not deliver data inside the operating window.
Before
Data latency from point of sale to analytics dashboard was approximately T+3 days. Store managers could not act on current-day performance.
What we delivered
Designed and implemented a streaming ingestion path from point-of-sale systems through a layered lakehouse, with measurable freshness SLAs at each stage and dashboard models recalculated continuously.
Outcome
<15 minPOS → dashboard latency · was T+3 days
Store managers gained visibility into the same day’s performance.
02
Bank · 8 disconnected source systems

One source of truth across eight systems.

Context
A bank operating eight disconnected systems needed unified visibility into transaction data for monthly close and regulatory reporting.
Before
Each system held its own definition of customer and balance. Monthly book-close was a multi-day reconciliation exercise; regulatory reporting suffered from inconsistency across systems.
What we delivered
A unified data platform with a single source of truth, integrating the eight source systems and standardising the customer and balance entities for downstream consumption.
Outcome
−60%Monthly book-close time
Regulatory reporting consistency improved significantly across systems.
What we do

Services across the platform stack.

The five services below define the scope of a Data Platform & Engineering engagement with ICS. Specific tools and platform versions are chosen per engagement based on your existing investments, regulatory context, and team capabilities.

Lakehouse
Data lakehouse architecture on AWS and DatabricksAs a Databricks Delivery Partner and AWS Partner
What this includesDesigning and implementing the lakehouse foundation appropriate for your data volumes, latency requirements, and downstream consumers.
ETL / ELT pipelines
Apache Spark, AWS Glue, dbt, Apache AirflowEngineered for measurable reliability
What this includesPipeline development and orchestration with tests, monitoring, and documented service level agreements at the dataset level.
Real-time streaming
Apache Kafka, Amazon KinesisFor operational intelligence use cases
What this includesReal-time data streaming where the use case requires it , not as a default. We assess latency requirements before recommending a streaming architecture.
Data governance
Cataloguing, lineage, quality rules, access controlImplemented incrementally
What this includesA governance framework starting with the most critical data domain, expanding from there. Not a big-bang initiative.
Legacy migration
Migration from legacy warehouses to a modern cloud-native stackWith validation and parallel-run periods
What this includesPhased migration with parallel running so accuracy is verified before legacy systems are retired.
After we hand off
You can keep ICS engaged under Managed Cloud & AI Operations for ongoing monitoring, incident response, and cost management , or your team can take over operation directly. All artifacts, documentation, and runbooks are yours from the start. The transition path is decided based on your internal capacity, not on a vendor lock-in model.
Related capabilities

Where teams go from here.

Scale

Advanced Analytics & ML

Once the platform is stable, this is where models go from notebooks to production scoring with MLOps.

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Build

AI Systems & Intelligent Automation

RAG and agentic systems that read from gold-layer data and write back through reverse-ETL.

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Operate

DevOps & Site Reliability

Same SRE practices, applied to the data plane: SLOs, error budgets, blameless post-incidents.

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Talk to us

Start with a platform assessment. Decide on the work after.

If any of the signals at the top of this page describe your situation, the next step is a structured assessment of your existing data platform , data sources, pipeline reliability, governance posture, and the gap to where the business needs the platform to be.

The assessment produces a roadmap your team can execute in-house, or that ICS can deliver. The decision is yours after the assessment, not before it.

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Platform assessment

What the assessment covers

  • Inventory of existing data sources, pipelines, and consumers
  • Reliability gaps and the cost of the most material ones
  • Governance posture against your regulatory context
  • A phased roadmap to a modern data platform, with realistic milestones
  • A platform stack recommendation based on your scale, budget, and team capabilities