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

Reliable pipelines. Trusted data.Ready for AI.

We design and operate data platforms that give teams reliable pipelines, governed datasets, and data products ready for analytics, ML, and business applications.

Built on AWS and Databricks where they fit, the platform is shaped by your sources, reliability needs, governance context, and downstream use cases, not by a fixed template.

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When this is needed

Three signals that this is the right next step.

01 / Pipelines keep breaking Business reports are delayed because data pipelines keep breaking. Teams cannot trust delivery windows, so reporting depends on manual fixes and late checks.
02 / Cleaning instead of analysing Analysts spend more time fixing data than analysing it. Data is available, but quality issues, duplicate logic, and unclear ownership slow every report.
03 / Data not ready for ML Data scientists cannot use the data in production workflows. Models need governed, tested, and discoverable datasets, not one-off extracts and notebooks.
Reference architecture

The shape of a modern data platform.

This is an illustrative architecture. Tooling, scale, latency, and governance are tailored to your sources, regulatory context, existing investments, and team capability.

01 / SOURCES

Current systems

Bring source data into view.
Transactional databases, SaaS tools, files, and event streams.

02 / INGEST

Reliable ingest

Move data with clear delivery rules.
Batch, ELT, streaming, orchestration, and dataset SLAs.

03 / LAKEHOUSE

Tested data zone

Structure data from raw to trusted.
Layered lakehouse design with governed medallion-style zones.

04 / SERVE

Serving layer

Feed analytics, ML, and apps from one trusted layer.
Curated datasets, BI models, ML features, and integrations.

05 / CONSUME

Business Use

Turn trusted data into decisions.
Dashboards, models, operational systems, and business workflows.

CROSS-CUTTING

Governance · lineage · observability · quality checks · dataset SLAs · access control, applied across the platform, not added later.

Sources & ingestConnect databases, SaaS APIs, events, and files using the right pattern: batch, ELT, or streaming when the use case requires it.
LakehouseBuild AWS or Databricks lakehouse layers from raw to cleaned to business-ready data, with tests and governance at each stage.
ServeOne trusted layer supports BI, ML, reverse-ETL, applications, and operational integrations.
GovernanceCataloguing, lineage, access control, and quality rules start with the most critical domain, then expand.
Engineering principles

The platform filter. Reliable by design.

Every build decision should improve trust in the data, reduce operating risk, or make delivery easier to maintain.

01 / ModelLakehouse by domain

Design around governed data domains instead of one giant table that becomes impossible to evolve.

02 / ShipPipelines as software

Version-controlled jobs, tests, reviews, monitoring, and dataset-level service expectations.

03 / ObserveReliability before scale

Monitoring, lineage, profiling, and alerts come before larger clusters and brute-force compute spend.

04 / GovernControls inside the workflow

Quality rules, access controls, and lineage are implemented in tooling, not left as documentation.

05 / ChooseLatency must earns its cost

Streaming and real-time paths are used when business SLAs require them, not by default.

Merged operating model

Every platform choice has to improve trust or reduce operational risk.

We combine lakehouse design, pipeline engineering, streaming, governance, and migration while avoiding common traps: paper-only governance, notebook-to-production shortcuts, oversized universal tables, unnecessary real-time systems, and compute-heavy fixes for reliability problems.

Case Studies

Two engagements. Both measurable.

These case studies highlight how ICS Compute helps organizations modernize their data estates, accelerate insight generation, and build AI-ready platforms powered by trusted data.

↗ DATA & AI PLATFORM
Consumer Enterprise • Media & Publishing

Modernizing Customer Intelligence with Databricks on AWS

ICS Compute designed and implemented a unified Data & AI platform that consolidated fragmented enterprise data into a governed analytics environment. The solution enabled self-service analytics, accelerated insight generation, and established a scalable foundation for machine learning and future AI initiatives.

Databricks AWS Lakehouse Delta Lake Data Governance AI Enablement
↗ Public Case Study
Life Sciences · Pharmaceuticals

PT Genero Pharmaceuticals

ICS Compute manages the cloud infrastructure for Genero Pharmaceuticals, supporting their manufacturing and distribution operations. The engagement includes cloud architecture governance, managed monitoring, incident response, and quarterly cost optimization reviews delivered through regular business reviews.

Amazon EC2 Amazon S3 AWS Systems Manager AWS CloudTrail FinOps
Additional Data & AI transformation engagements available. ICS Compute maintains a portfolio of enterprise Data & AI platform implementations across consumer, financial services, healthcare, manufacturing, and public sector organizations. Additional case studies and technical references are available upon request.
What we do

Services across the platform stack.

A Data Platform & Engineering engagement covers the architecture, pipelines, governance, and operating model needed to make data reliable and usable.

Lakehouse
Design the governed data foundationAWS or Databricks lakehouse architecture based on your volumes, latency needs, governance rules, and consumers.
IncludesRaw-to-curated layers, data domains, access patterns, and platform design.
ETL / ELT pipelines
Build reliable data movementPipeline development and orchestration with tests, monitoring, review history, and dataset-level expectations.
IncludesSpark, Glue, dbt, Airflow, or the right fit for your stack.
Real-time streaming
Use streaming where the business needs itStreaming paths for operational intelligence, event-driven systems, and low-latency data products.
IncludesKafka, Kinesis, stream processing, and latency validation.
Data governance
Make data control operationalCataloguing, lineage, quality rules, access control, and ownership implemented incrementally.
IncludesGovernance starting from the most critical data domain.
Legacy migration
Move from old warehouses safelyPhased migration from legacy warehouses to a modern cloud-native data stack.
IncludesValidation, parallel running, and accuracy checks before retirement.
After handoff
Operate with the right support modelYour team can operate the platform directly or keep ICS engaged under managed operations.
IncludesRunbooks, documentation, monitoring, and transition planning.
After we hand off
You can keep ICS engaged under Managed Cloud & AI Operations for monitoring, incident response, and cost management, or your team can take over directly. Artifacts, documentation, and runbooks are yours from the start.
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.

↗
Build

AI Systems & Intelligent Automation

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

↗
Operate

DevOps & Site Reliability

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

↗
Talk to us

Start with a platform assessment. Decide the build after.

If pipelines keep breaking, data quality slows every report, or ML teams cannot use the data, start with a structured platform assessment.

The output is a roadmap your team can execute internally or with ICS support.

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Platform assessment

What the assessment covers

  • Inventory of data sources, pipelines, and consumers
  • Reliability gaps and material business impact
  • Governance posture against your regulatory context
  • Phased roadmap to a modern data platform
  • Platform recommendation based on scale, budget, and team capability