Unified Data
Intelligence Platform
Turn fragmented data silos into a single source of truth — powering real-time dashboards, predictive analytics, and AI across your entire organisation.
Where Modern Data Platforms Deliver Immediate Value
Three powerful use cases where unified data platforms transform decision-making across the organisation.
Operational Dashboards
Live dashboards that update as data flows — enabling sub-second decision making for trading, risk, fraud detection, and customer service.
Single Source of Truth
Consolidate ERP, CRM, marketing, operational, and financial data into one governed platform accessible to analysts and business teams.
Predictive Intelligence
Enrich your platform with ML models for demand forecasting, customer segmentation, anomaly detection, and strategic planning.
Three Practice Areas,
One Seamless Delivery
Whether you need full lakehouse architecture, domain-driven data mesh, or native zero-ETL integrations — we scope each engagement around your exact data needs and timeline.
Lakehouse
Combine data lake flexibility with data warehouse reliability using Delta Lake, Apache Iceberg, and cloud-native architectures. Eliminate the cost and complexity of maintaining separate storage tiers while gaining ACID transactions, schema enforcement, and time-travel queries across your entire data estate.
Data Mesh
Decentralise data ownership across domains while maintaining governed, discoverable, and interoperable data products. Data mesh eliminates the bottleneck of a central data team while preserving the standards needed for trusted, enterprise-wide analytics.
Zero-ETL
Eliminate complex extract-transform-load pipelines with native database integrations, change data capture, and real-time streaming. Make data available in your analytics platform in seconds rather than hours — dramatically reducing pipeline maintenance burden and data staleness.
How We Deliver Data Platforms That Scale
Data Landscape Assessment
Two-week audit of your existing systems, data quality, volume, and compliance requirements. We identify the biggest bottlenecks and highest-value opportunities before proposing any architecture.
Modern Platform Design
Blueprint for lakehouse deployment within your AWS/Azure/GCP environment, with data governance, security, cost modeling, and integration priorities.
Iterative Build & Validation
Weekly sprints delivering working data pipelines, dashboards, and analytics models. You see real data flowing and validate the approach before full rollout.
Production Deployment
Rigorous testing of data quality, performance, and governance. Full integration with existing systems, user training, and 24/7 monitoring from day one.
Continuous Optimization
Automated cost monitoring, query performance tuning, data lineage tracking, and quarterly reviews to expand analytics scope and improve ROI.
Data Platform
Expertise at Scale.
We bring open-source-first architecture, enterprise governance, and proven ROI delivery — backed by 14+ years of delivering data solutions that stick.
Open-Source-First Architecture
We build on Apache Iceberg, dbt, Airflow — giving you portability, auditability, and freedom from vendor lock-in.
Enterprise Data Governance
GDPR, HIPAA, and industry compliance built into the platform from day one — not bolted on afterwards.
Proven ROI Delivery
Our implementations consistently deliver cost savings and performance gains measured in weeks, not months — with transparent metrics from day one.
Embedded Data Enablement
We don't just build platforms — we train your analysts and engineers so you own and evolve the platform independently.
Data Platform Unifies 50+ Systems, Reduces Report Time by 90%
A global financial services firm struggled with data scattered across 50+ systems. Reporting took 3+ days. We deployed a lakehouse unifying all data sources with real-time ETL pipelines.
Frequently Asked Questions
Common questions about data platform architecture, implementation, ROI, and governance.
A data warehouse stores structured, processed data optimised for reporting and SQL queries, but can be expensive and inflexible. A data lake stores raw data cheaply in open formats but lacks governance and query performance. A data lakehouse combines the best of both: open table formats like Apache Iceberg or Delta Lake give you ACID transactions, schema enforcement, and high-performance queries on raw data in cloud storage. The result is a single platform for both real-time operational analytics and complex historical reporting — at a fraction of traditional warehouse cost.
A focused data platform project — for example, migrating a single data domain to a lakehouse or implementing a zero-ETL integration — typically takes 6–10 weeks. A comprehensive data platform covering multiple domains, governance, and self-serve analytics usually spans 3–6 months. Timeline depends heavily on data volume, source system complexity, and how much transformation logic needs to be rebuilt. We always start with a data landscape assessment to give you an accurate scope before committing to a timeline.
Data mesh is a distributed data architecture where individual business domains own and manage their own data products rather than relying on a central data team. It works best for large organisations (500+ employees) with multiple distinct business domains that have different data needs and update cadences. For smaller organisations, a well-governed centralised lakehouse is usually more practical and cost-effective. We help you assess which architecture fits your scale and organisational structure during the discovery phase.
Zero-ETL refers to direct, native integrations between source systems and your analytics platform that eliminate the need for custom extract-transform-load pipelines. Examples include Amazon Aurora zero-ETL to Redshift, Salesforce Data Cloud native sync, and change data capture (CDC) streams via Debezium or AWS DMS. By removing bespoke ETL code, you reduce pipeline maintenance costs, data latency (from hours to seconds), and the risk of silent pipeline failures — which are among the most common causes of stale or incorrect dashboards.
We implement data quality checks at every layer of the pipeline using frameworks like Great Expectations or dbt tests. Data cataloguing with Apache Atlas or DataHub provides full lineage tracking so you can trace every metric back to its source. Role-based access controls, column-level masking, and audit logging ensure compliance with GDPR, HIPAA, and internal data policies. We also build data observability dashboards that alert your team to anomalies, schema changes, and freshness issues before they reach business reports.
Yes. We have deep experience integrating SAP S/4HANA, Salesforce, Microsoft Dynamics, HubSpot, Shopify, Oracle, and hundreds of SaaS tools via native connectors, Airbyte, Fivetran, and custom CDC pipelines. Source system integration is scoped during our discovery phase and we design for resilience — ensuring that failures in individual source systems do not cascade to your entire analytics platform.
We are cloud-agnostic and work with AWS (Redshift, Glue, Athena, EMR), Azure (Synapse Analytics, Data Factory, Azure Databricks), and GCP (BigQuery, Dataflow, Dataproc). For transformation and orchestration we work with dbt, Apache Airflow, Prefect, and Dagster. For serving and analytics, we support Tableau, Power BI, Looker, Apache Superset, and custom embedded analytics. We recommend the stack that best matches your team's skills and existing cloud investment.
Ready to Unify Your
Data & Drive Insights?
Book a free, no-obligation consultation. We'll audit your data landscape, identify bottlenecks, and outline a pathway to a modern platform — at no cost and no commitment.
No obligation · Response within 1 business day · 14+ years expertise