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Turn raw data into decisions that move your business

Production-grade pipelines, a Redshift data warehouse that actually performs, and analytics your business users can act on — all on AWS.

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The challenge

Data that can't be trusted can't be used

Most organisations have data. Few have data they can rely on. Inconsistent pipelines, schema drift, missing audit trails, and reports that contradict each other — these are the symptoms of an analytics stack that was built reactively, not designed.

We build data platforms from the ground up on AWS — with Amazon Redshift as the analytical core, DMS CDC pipelines that capture every change from source systems, and a governance layer that makes your data trustworthy by default.

The result: your analysts stop spending 60% of their time questioning the data and start spending it on insights that drive revenue.

Data engineering and analytics infrastructure

What we deliver

From raw source data to self-serve analytics

Amazon Redshift data warehouse

End-to-end Redshift implementation — node sizing, distribution keys, sort keys, WLM queues, materialized views, and stored procedures. Built to perform under betting-scale query loads.

CDC pipelines with AWS DMS

Change Data Capture from SQL Server, Oracle, PostgreSQL, and MySQL into S3 and Redshift. Every insert, update, and delete captured — zero data loss, near-zero latency.

Real-time streaming

Amazon Kinesis and Apache Flink for event-driven architectures — live odds ingestion, real-time player activity monitoring, and sub-second latency for operational dashboards.

Data lake on S3

Raw, curated, and consumption layers in S3 with AWS Glue crawlers, Lake Formation tag-based access control, and Athena for ad-hoc queries — without touching Redshift.

BI layer & self-service analytics

Amazon QuickSight dashboards and Power BI integration — semantic layer design, row-level security, and scheduled reports so business users get answers without writing SQL.

Data quality & governance

Schema validation, data contracts, anomaly detection on pipeline outputs, and audit trails — so when a number looks wrong, you can trace it back to the source in minutes.

Architecture patterns

Battle-tested patterns we deploy across clients

Analytical pipeline Most common

Source DB → DMS CDC → S3 Parquet → Redshift COPY → QuickSight

Scheduled batch ingestion for operational reporting. Ideal for daily/hourly analytics on transactional data from SQL Server or Oracle sources.

Real-time ingestion

Event source → Kinesis → Flink / Lambda → Redshift

Sub-second event ingestion for live betting activity, odds updates, and real-time player monitoring dashboards.

Zero-ETL integration

Aurora / DynamoDB → Zero-ETL → Redshift

Native AWS integration eliminating custom pipeline code for Aurora PostgreSQL and DynamoDB sources. Near-real-time with automatic schema sync.

Federated query layer

RDS / Aurora → Redshift Spectrum → Unified query

Query live operational data and historical warehouse data in a single SQL statement — no replication needed for low-volume reference data.