Definitions, scheduling and quality gates, fixed together
Layered architecture, data modelling and real-time / batch integration
Data platform problems usually come from layering and modelling rather than technology choice. We design ODS / DWD / DWS / ADS layering and lakehouse architecture, build dimensional and normalised models, develop ETL / ELT and CDC real-time pipelines, and fix metric definitions, job scheduling and quality gates in the development standard.
Our approach
One team scopes, executes and retests; conclusions are delivered as evidence, not checklists.
Architecture starts from business subject areas
The bus matrix comes before technology selection. Subject areas, conformed dimensions and metrics are settled first; storage and compute components follow.
Model review is mandatory
Every core table passes four reviews before go-live: naming, layering, dimension conformance and performance. Records are archived and the checklist ships with the deliverables.
Real-time and batch share one set of definitions
CDC and stream pipelines reuse the batch model's dimensions and metric definitions, so real-time reports and daily reports agree.
Deliverables
Data architecture design
Layering, subject areas, bus matrix, technology selection and capacity planning.
Data models and development standard
Conceptual / logical / physical models, naming conventions, review checklist.
Pipelines and scheduling
ETL / ELT jobs, CDC real-time pipelines, scheduling dependencies and quality gates.
How we deliver
Four stages, each with defined inputs, outputs and a client sign-off.
Requirements and current state
Business subjects, data sources, existing jobs and pain points.
Architecture and modelling
Layered architecture, bus matrix, core model design and review.
Development and migration
Pipeline development, historical data migration, scheduling live.
Stabilisation
Performance tuning, job monitoring, handover training.
Customer story
Reallysec Drives Intel's Security Architecture Transformation, Reshaping Its Defense System
Work with Intel's InfoSec team to rebuild the architecture, putting stream processing and machine learning to work on threat triage.
Frequently asked questions
Notes on scope, execution and delivery standards. Contact us for anything not covered here.
A lakehouse suits environments with much semi-structured data, both batch and streaming, and a need to reduce storage cost; a traditional warehouse still has the edge for structured reporting with strong consistency. The choice is made in the architecture phase by data types, query patterns and team skills.
Start from where you stand
Security, data and AI each start with a review of where you stand. The report and its findings are yours, whether or not the engagement continues.