Data engineer reviewing a source-to-feature-store architecture with dbt, Iceberg and Feast on a wall-mounted screen

Data foundations for AI

An AI-ready data foundation on your Databricks or Snowflake estate in 90 days.

Lakehouse, feature store, lineage, contracts, quality gates — stood up around your first production AI use case with measurable feature-availability SLAs and a documented path off the swamp.

Book a 60-minute data estate reviewTake the 47-point AI-Ready Data Estate Diagnostic

Median GCC enterprise scores 41/100. The gap to a 75 (production-AI ready) is 6–9 months of focused engineering, not 24.

Source → feature store

Sources

SAP S/4HANA

Oracle EBS

IBM mainframe

Excel exports

Features

customer_30d_avg_balance

branch_footfall_p90

equipment_vibration_rms

claim_riskscore_v3

dbt → Iceberg → Feast

Feature lead time

14 wk → 9 days

14→9d
Feature lead time at a tier-1 GCC bank
41/100
Median GCC data estate score (n=80)
11
Source systems unified in one engagement
90 days
To first production AI use case

Why your AI roadmap is stuck at the data layer

The models are designed. The scientists are hired. The data is the problem.

Three failure patterns we have seen across more than fifty data platforms in the GCC. The roadmap is approved. The Databricks or Snowflake spend is already AED 6–18M per year. The board is asking, in writing, what business value the platform has actually produced.

Fourteen disconnected systems

SAP S/4HANA, Oracle EBS, an IBM mainframe, three S3 buckets nobody documents, half a dozen Salesforce orgs, Excel exports. No two teams agree what "active customer" means. The first AI model is stuck because the data definition is stuck.

No contracts, no lineage

The data swamp the previous CDO built is queryable but not trustable. A schema change upstream breaks five downstream pipelines on a Tuesday morning. The data engineering team spends 70 percent of its time on one-off pipelines, not platform work.

Vendor-pro-services bias

Databricks PS architects everything into Delta and Unity. Snowflake PS architects everything into Snowpark. Neither is incentivised to stay platform-pragmatic, and the mainframe layer is quietly ignored. Twelve weeks later the team rotates off and the gap reopens.

The Brocode AI-Ready Data Stack

The components we pin and what each one does.

Platform-pragmatic, not platform-loyal. Iceberg, Delta or Snowpark chosen per use case. Open-source equivalents substituted where the customer is fully sovereign. Nothing chosen because a vendor pays us to choose it.

dbt Core + Cloud

Transformation

Apache Airflow / Dagster

Orchestration

Apache Iceberg on S3 / ADLS

Open table format

Great Expectations + Soda Core

Quality gates

OpenLineage + Marquez

Lineage

DataHub / Unity Catalog

Catalogue & governance

Feast or Tecton

Feature store

Debezium + Kafka

CDC from SAP / Oracle

Apache Spark / Snowpark

Heavy compute

Precisely Connect / IBM IIDR

Mainframe CDC

The parts most consultancies skip

SAP ODP. Oracle GoldenGate alternatives. Mainframe CDC.

The cost-per-row of replicating SAP, Oracle and mainframe data sits inside three nuances: licence-safe extraction patterns, schema-evolution tolerance, and the change window your operations team will actually grant. We have built reusable extraction modules for all three.

SAP S/4HANA + ECC
ODP + Debezium against CDS views
Licence-safe. Counsel-reviewed. We hand the customer the licence-position memo at SoW signature.
Oracle EBS / Fusion
Concurrent Programs → OIC → Kafka, or Debezium against replicated CDC tables
GoldenGate alternative where licence is not in place.
IBM Z / iSeries mainframe
Precisely Connect or IBM IIDR for CDC
No mainframe code change. Replicates to Iceberg or Kafka inside the customer change window owned by the mainframe team.

The 90-day foundation sprint

One use case end-to-end. The rest of the roadmap inherits a working template.

Fixed scope. Named pod in the SoW: a Brocode principal data architect, two senior data engineers, and a delivery lead. CVs are visible before contract signature.

Week 0–2
Discovery and architecture
Source inventory, residency mapping, contract catalogue, target use case. Output: a one-page reference architecture aligned to your Databricks or Snowflake estate.
Use case scoped
Week 3–6
Bronze, silver, gold layers
Iceberg tables on your storage. dbt models with contract tests. Great Expectations gates at every layer transition. Lineage captured from raw source to feature.
Pipeline live
Week 7–10
Feature store and serving
Feature definitions registered in Feast. Point-in-time correctness for training. Online retrieval wired into the consuming model. Lineage extends from feature to prediction.
Feature lead time → days
Week 11–13
First production use case
One named AI use case live on the new foundation with monitoring, freshness SLAs and a runbook. The remaining roadmap has a working template to repeat.
90-day milestone

How we compare

Databricks PS, Snowflake PS, Big-4 and offshore ETL — honestly.

Vendor pro-services do good work inside their own platform and rotate off at week 12. Big-4 fields partner-plus-pyramid and ships slides. Offshore ETL shops move data but do not feed models. We are platform-pragmatic, senior-heavy, and ship in code.

Capability	Brocode	Databricks PS	Snowflake PS	Big-4 data practice	Offshore ETL shop
Platform-agnostic (Iceberg / Delta / Snowpark on merit)		Delta + Unity only	Snowpark only	Whatever sells	Whatever was bought
SAP ODP + Debezium extraction (licence-safe)	Reusable pattern, counsel-reviewed	Limited	Limited	Often unsafe	Unsafe by default
Mainframe CDC (Precisely / IIDR)					Ignored
Feature store with point-in-time correctness	Feast or Tecton	Databricks Feature Store only	Limited	Pipeline files, no store	Not delivered
Named senior engineers in SoW CVs visible before contract signature.					Partner-plus-pyramid
Stays after go-live	Hypercare + handover deliverable	Rotates off in 12 weeks	Rotates off in 12 weeks	Sometimes	Rotates off
First production AI use case live	90 days	Vendor-locked roadmap	Vendor-locked roadmap	6–12 months	Slides, not code

Production references

Two engagements. Two quantified outcomes.

Tier-1 GCC bank

Eleven source systems unified. Feature lead time 14 weeks to 9 days.

AED 4.2M annualised saving on duplicated ETL effort. Iceberg-on-S3 lakehouse, Feast for serving, dbt for transformation, OpenLineage end-to-end. The first AI use case (credit decisioning) live on the foundation in 90 days; six follow-on use cases live in the next twelve months on the same pattern.

UAE energy major

SAP S/4HANA + plant historian + maintenance system unified.

Azure UAE North region, sovereign-aligned. Predictive-maintenance model live on the lakehouse with a documented uplift on unplanned downtime. SAP ODP extraction pattern reviewed by SAP licence counsel before go-live.

The diagnostic

The 47-point AI-Ready Data Estate Diagnostic.

An interactive self-assessment plus a 24-page PDF generated for your answers. Covers source coverage, contract maturity, lineage completeness, feature-store readiness, governance, FinOps and team capacity. Median GCC enterprise scores 41/100. The gap to a 75 is 6–9 months of focused engineering.

Free download

AI-Ready Data Estate Diagnostic — 47 Points

A self-score against what an AI programme actually needs. Includes the scoring rubric and the median GCC benchmark by sector.

Source and ingestion coverage (12 points)
Modelling and transformation (8 points)
Quality and contracts (8 points)
Lineage and governance (7 points)
Serving and feature stores (6 points)
FinOps and team capacity (6 points)
Scoring rubric — median GCC org 41/100
Benchmark by sector (banking, energy, government, telco)

Frequently asked

What Heads of Data Platform actually want to know.

Databricks PS will architect everything into Delta and Unity. Snowflake PS will architect everything into Snowpark. Both are loyal to their platform; neither is incentivised to integrate mainframe and SAP properly, and both rotate off in 12 weeks. Brocode is platform-pragmatic — Iceberg, Delta or Snowpark per use case — and stays through hypercare. The handover is a deliverable, not a hope.

Talk to a principal data architect

A senior architect reviews your estate and your sovereignty constraints, and replies within one business day.

We will tell you which of your roadmap use cases are 90-day plays on your current platform, which are 12 months, and which are stuck because the underlying source still needs CDC, contracts, or a feature definition the rest of the business agrees with.

Continue exploring