Practice 03 / Data Engineering

Data you can trust.
Intelligence
you can use.

Build the data substrate for decisions, applications and AI. Engineer pipelines and governed data products on AWS, with quality and ownership designed into every layer.

Speak to a partner Explore the architecture

From the operating model to the working system.Strategy and architecture, embedded delivery, capability transfer.

REVOLEAP / DATA ENGINEERINGA connected estate / 03

Many sources.
One trusted flow.

SystemsEventsKnowledge

Separate signals. Shared meaning.

Give data a shared meaning.

Clear definitions, traceable lineage and access shaped around the people who use it.

Contracts · Catalogue · Ownership

01 / The opportunity

When the data holds the business back.

The answers don’t agree.

Give teams shared definitions and traceable data they can use to make decisions.

The pipeline needs attention.

Replace manual fixes with quality checks, recovery paths and clear ownership.

AI needs a better foundation.

Prepare enterprise knowledge with provenance, freshness and appropriate access.

Our engineering position

Trust is a property of the whole pipeline.

Start with the decision the data must support. Design meaning, quality and ownership all the way back to the source.

02 / In practice

One foundation. Three ways to use it.

Illustrative enterprise patternOrder to fulfilment

ERP & CRM recordsOrder & delivery eventsPolicies & documents

Shared business context

Orders. Customers. Fulfilment.

Agreed definitions, quality checks and traceable sources.
Access and freshness matched to each use.

Decisions

See the business clearly.

Consistent order and revenue reporting.

Analytics-ready tables

Applications

Act on what changed.

Identify orders that need attention.

Events & serving APIs

Explain with context.

Connect delivery facts with relevant policy.

Authorised retrieval

A common business model. Purpose-built serving paths.

03 / The engineering

Engineered for use.
Designed for ownership.

Business

Define what matters.

Domain owners, shared measures and the decisions each data product supports.

Engineering

Make the flow dependable.

Ingestion, modelling, quality contracts and serving paths built around actual use.

Operations

Keep trust intact.

Freshness objectives, lineage, access reviews, recovery and cost visibility.

AWS-native data

A governed analytical foundation.

Amazon S3, AWS Glue Data Catalog and Lake Formation, with Athena or other serving engines chosen for the workload.

Databricks on AWS

A lakehouse built for reuse.

Delta tables and staged refinement, with Unity Catalog for governance across data and AI assets.

AWS reference architecturesGoverned lake · Streaming · AI retrieval

REVOLEAP / DATA ENGINEERING

Inside the architecture.

Reference design / 03

01 / GOVERNED LAKEAWS reference design

STORAGEAmazon S3Raw + curated datasets

CATALOGUEAWS Glue Data CatalogTable metadata

AWS LAKE FORMATIONData access policiesConfigure permissions for supported analytics services.

Query governed tables

AMAZON ATHENAFrom curated data to usable answers

Query S3 datasets using catalogued tables, with access and workgroup settings configured for each team.

Data ownershipSchema contractsAccess policies

Illustrative architecture. Configured for each workload.AWS / DA

Databricks / from raw records to business-ready data

Bronze / retain

Preserve the source.

Keep raw records and source context for audit and reprocessing.

Silver / refine

Validate and reconcile.

Clean, deduplicate and join data into consistent representations.

Gold / serve

Model for the decision.

Shape measures and datasets around business consumption.

Unity Catalog / access control, discovery, audit and lineage.

A reference pattern. Layer boundaries and refresh schedules follow the domain’s needs.

The decisions that keep data useful in production

Fresh enough for the work.

Choose batch, change capture or streaming around decision latency. Design replay, late records and duplicate handling.

Quality with a response.

Define schema contracts and useful checks. Assign owners and recovery paths when freshness or quality falls short.

Knowledge with boundaries.

Design content refresh, metadata and application authorisation together. Evaluate retrieval against real questions.

Features without leakage.

Build reusable transformations and point-in-time datasets where model training requires them.

Architecture references: AWS governed data lake · Databricks medallion pattern · Unity Catalog

What success looks like

Agree the outcome.
Engineer the evidence.

The measures depend on your starting point. We define them with you before delivery begins.

Data people can trust

Freshness, completeness and reconciliation checks that reflect the decisions the data supports.

A useful data product

Clear ownership, documented meaning and access through the interfaces consumers need.

An operable platform

Pipeline recovery, lineage and cost visibility, with a team able to evolve the system.

04 / How we engage

Start with the constraint.
Build the capability.

Understand

Find the point of friction.

Trace a business decision back through definitions, sources and the current data estate.

A prioritised opportunity and risk brief.

Design & deliver

Build a useful data product.

Engineer the pipeline and serving layer alongside your team, against agreed quality and freshness criteria.

A working data product. An operating plan.

Retain & evolve

Make ownership last.

Transfer runbooks and engineering practices. Improve reliability, economics and reuse as needs grow.

Accountable owners and a delivery cadence.

Where to start

Let’s start with the layer
that’s holding you up.

Bring a data challenge or a decision you struggle to trust. Leave a 90-minute working session with clearer priorities and a practical next step.

Begin a conversationThe Leap Assessment / 90 minutes

Data you can trust.Intelligenceyou can use.

Many sources.One trusted flow.