Data Engineering and Governance
One governed place for your data, with pipelines that keep it current and access rules that hold.
Why It Matters
Most institutions keep data in silos: ERP tables, core systems, spreadsheets and documents that nobody has joined up. Reports arrive late, numbers disagree, and every new project copies data again.
AI makes the gap more expensive. Models and agents need current data, documents and clear access rules, delivered fast enough to act on. A fragile platform underneath puts every answer at risk. The work fails in three places: a target designed before the questions are known, quality checks added after launch, and a migration without reconciliation.
What We Do
Data Pipelines and Real-Time Streaming
Batch and event pipelines move data between systems on a schedule or as it happens, with owners and quality checks.
Warehouses and Lakehouses
A warehouse or data lake that holds one trusted copy for reporting and AI, sized to your volumes and query patterns.
Vector and Graph Stores
Vector, graph and text stores that give search and models the data they need, with access rules applied.
Databases, Backup and Recovery
Databases that are patched, backed up and tested for recovery, with the runbooks your team needs.
Master Data, Quality and Lineage
One version of each customer, supplier and product record, with its source, owner and history.
Data Security, Masking and Retention
Access, masking, retention and deletion rules that follow the data into every copy, including the ones models read.
What Changes for You
Data as It Happens
Events arrive as they happen, so reports and services use the current record rather than last night's copy.
One Trusted Place
Analytics, reporting and AI read from one governed platform with shared definitions and owners.
Recovery You Have Tested
Backups and failover are proven on a schedule, so a recovery is a known step and not a hope.
How the Work Runs
The platform is built in slices, each with a named owner and a check that proves it. Your architects approve the design before the first source is connected.
Map the Sources
We review your systems, volumes, reports and current pain. You name the questions and models the platform must serve first, and the data owners we may contact.
Design the Target
We design the data platform, the streaming layer, the data model and the recovery targets. Your architects approve the decisions and the boundaries between teams.
Build in Slices
We bring in one domain at a time with quality checks and lineage. Your data owners confirm the definitions and sign off each slice before it goes live.
Set the Rules
We define access, masking, retention and deletion for every dataset, and record where each copy lives. Your risk and legal teams approve the rules before data is shared.
Migrate and Reconcile
We rehearse each migration on a copy and reconcile counts and totals. You approve the switch window and the rollback plan before anything old is turned off.
Run and Hand Over
We monitor pipelines, prove failover and restores, and write the runbooks. Your team runs the platform, or we do under an agreement you set.
Platforms and Technologies
The platform is built from tools your team can hire for and operate. Google Cloud, Amazon Web Services, SAP and Oracle are the platforms we connect to. Snowflake and Databricks are managed data platforms, and BigQuery and Amazon Redshift are cloud warehouses. Apache Kafka carries events, Apache Spark handles large batch work, and Apache Airflow schedules jobs. dbt builds tested transformations. SQL is the query language your analysts already use, pgvector stores the numeric patterns that search and models compare, and Elasticsearch indexes text for search.
- Languages
- SQL
- Data and analytics
- Apache Kafka
- Snowflake
- Databricks
- Apache Spark
- Apache Airflow
- dbt
- BigQuery
- Amazon Redshift
- Databases
- pgvector
- Elasticsearch
- Cloud
- Amazon Web Services
- Google Cloud
- Methods We Use
- Scheduled and change-only pipelines
- Event streaming
- Data rules and history
- One record per customer and product
- Hiding personal data and retention rules
- Quality checks and reconciliation
Naming a product does not mean a partnership with its vendor.
Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
AWS
We set up AWS accounts and guardrails, move workloads with its migration tools and keep cost and access visible.
SAP
We move SAP to S/4HANA, connect it to banks and tax platforms, and keep the core clean with work on BTP.
Oracle
We move Oracle databases and Fusion applications together, with performance tested before each cutover.
Industries
Banking and Financial Services
Banking, payments, lending, wealth, insurance and digital wallets: the platforms they run on and the connections between them.
Energy and Utilities
Smart-grid analytics, OT defence, asset maintenance and ESG reporting for energy producers, utilities and grid operators.
Healthcare
Patient apps, scheduling and billing, and clinical data integrations, engineered for health-data protection.
Media and Telecommunications
Consumer platforms, streaming, connected devices and ad-tech that stay fast at peak audience, with AI for content and personalisation.
Retail and Omnichannel Commerce
POS integration, stock and price sync, card payments, loyalty and e-commerce for retailers with many sites.
Questions
What buyers ask before they rebuild the platform under their data.
Where does data work start?
Usually with a review of the sources, the questions and the current reports. You get a written target, a costed sequence and the first slice to build. If the need is already clear, we start with the design of that slice.
Which platform should we choose?
It depends on your data, skills, where the data must stay and how you query it. We compare the managed platforms and an open data platform against your workloads, then recommend the simplest one that fits.
How do you make sure the numbers are right?
Every pipeline checks volumes, keys, totals and freshness, and a failure stops bad data from reaching reports. We reconcile key figures with your source systems and their owners.
How do you migrate without stopping the business?
We rehearse on a protected copy and reconcile counts and checksums between source and target. Systems that cannot stop run in parallel, and the switch happens in a short window with a tested rollback.
What drives the cost?
The number and size of the sources, how current the data must be and how long it is kept. Streaming and frequent refreshes cost more than daily loads, so we agree where fresh data changes a decision.
Tell Us What You Need to Run, Build or Secure.
Describe the service, the systems and the pressure you are under. The right team takes it from there.