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.
Business reports are consistently delayed because data pipelines cannot be relied upon.
The analytics team spends more time cleaning and fixing data than analysing it.
Data scientists cannot work productively because the data they need is not available in a usable format.
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.
OLTP, SaaS, files
Batch, stream, SLAs
Tested data zones
BI, ML, apps, ETL
BI, ML, apps, ETL
Transactional 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.
Data 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.
One layer feeding BI, machine learning, and operational integrations. Engineered for measurable reliability, not best-effort delivery.
Data cataloging, lineage tracking, quality rules, and access control , implemented incrementally, starting from the most critical data domain rather than as a big-bang initiative.
We merge platform scope and engineering restraint into one operating model, so every build decision improves reliability without adding unnecessary operational weight.
Build the AWS or Databricks foundation around governed data domains instead of one giant table that becomes impossible to evolve.
ETL and ELT jobs move into version-controlled Spark, Glue, dbt, or Airflow workflows with tests, review history, and measurable dataset SLAs.
Monitoring, alerting, lineage, and workload profiling come before larger clusters, so performance fixes solve the cause instead of hiding it in spend.
Cataloguing, lineage, quality rules, and access control are implemented incrementally in tooling, not left as documentation that changes no behaviour.
Streaming, legacy migration, and parallel-run validation are chosen when the business SLA requires them, not because newer architecture sounds better.Stack and model choices follow your data, team capacity, governance, and long-term ownership path.
Use cases are filtered by impact, data readiness, and adoption risk. The first 90 days produce a live proof, while architecture choices stay tied to your context instead of vendor lock-in.
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.
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.
Data latency from point of sale to analytics dashboard was approximately T+3 days. Store managers could not act on current-day performance.
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.
POS → dashboard latency · was T+3 days
Store managers gained visibility into the same day’s performance.
A bank operating eight disconnected systems needed unified visibility into transaction data for monthly close and regulatory reporting.
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.
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.
Monthly book-close time
Regulatory reporting consistency improved significantly across systems.
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.
As a Databricks Delivery Partner and AWS Partner
Designing and implementing the lakehouse foundation appropriate for your data volumes, latency requirements, and downstream consumers.
Including hidden costs and dependencies
Pipeline development and orchestration with tests, monitoring, and documented service level agreements at the dataset level.
For operational intelligence use cases
Real-time data streaming where the use case requires it , not as a default. We assess latency requirements before recommending a streaming architecture.
Cataloguing, lineage, quality rules, access control
A governance framework starting with the most critical data domain, expanding from there. Not a big-bang initiative.
With validation and parallel-run periods
Phased migration with parallel running so accuracy is verified before legacy systems are retired.
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.