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Catalogue › Data & Artificial Intelligence › Data Engineering & Integration
Data & Artificial Intelligence
Data Engineering & Integration
Design and implementation of trusted data pipelines, integration layers and governed data stores that make operational data usable across analytics, automation and AI.
- Consistent and timely data
- Reduced manual reconciliation
- Traceable transformations
- Reusable data services for analytics and AI
- Source assessment and data contracts
- Batch and streaming ingestion
- Cleaning, normalization and enrichment
- Lakehouse, warehouse or operational data store
- Metadata, lineage and quality controls
- API and event integration
- Confirmed scope, stakeholders, assumptions and acceptance criteria
- Assessment, design or implementation work products
- Decision log, risk register and issue resolution
- Testing or evidence pack appropriate to the service
- Knowledge transfer, administrator guidance and handover
- Follow-up support or managed-service transition where contracted
- Cloud-native or hybrid architecture
- Master data management
- Real-time event processing
- Managed data platform operations
- Edition, modules and user/location count
- Hosting, environments and availability target
- Data migration and integrations
- Configuration versus custom development
- Security, compliance and assurance scope
- Training, support and service level
Content on this page comes from the governed ARRIX catalogue record DAI-01; pricing is confirmed only through a reviewed quotation.
What This Is
Enterprise Data Integration & Modernization
Enterprise Data Integration connects fragmented databases, SaaS systems, APIs, ERP platforms, files and legacy applications into reliable governed data flows that analytics and AI can depend on.
The problem this answers
Data the business needs together lives in systems that were never designed to talk to each other. Somebody exports a spreadsheet every Monday. A nightly job fails quietly and nobody notices until a report looks wrong. Each new project builds its own connection to the same source, so the same integration exists four times and disagrees with itself.
Information arrives without someone fetching it
Scheduled and event-driven pipelines replace manual export, reformat and upload.
The same number means the same thing everywhere
One governed flow per source replaces several competing extracts built by different teams.
New projects start faster
An existing integration is reused rather than rebuilt, so the second and third consumer cost a fraction of the first.
Legacy systems stop blocking modernisation
Older platforms are integrated behind a stable interface instead of being replaced before the business is ready.
Failures are seen rather than discovered
Orchestration with monitoring and reconciliation surfaces a broken load at the time it breaks.
Why It Matters
Where this changes the outcome
Integration is where most of the cost and most of the fragility of a data estate actually sits. Every downstream capability - reporting, forecasting, AI - inherits the reliability of the pipelines beneath it, and manual movement is the single largest source of both delay and error.
| Organisation | Need | What ARRIX does | Outcome |
|---|---|---|---|
| A group finance team | Consolidated reporting across subsidiaries on different systems | Extraction, mapping and reconciliation into one governed model | Consolidation stops being a monthly manual exercise. |
| An organisation running a legacy core system | Modern analytics without replacing the core | Change data capture behind a stable interface | The legacy platform keeps running while its data becomes usable elsewhere. |
| A business with many SaaS applications | One view of the customer across marketing, sales, support and billing | API integration and identity mapping | Customer records reconcile instead of contradicting each other. |
| A team preparing for AI | Reliable, current data reaching a model | Governed pipelines with defined freshness | The model reads production-grade data rather than a hand-made extract. |
| An organisation migrating platforms | Moving data without losing or corrupting it | Migration with mapping, validation and reconciliation | The migration is provable rather than hoped for. |
How It Works
The path, stage by stage
- ERP
- CRM
- SaaS
- Databases
- APIs
- Files
- ARRIX INTEGRATION LAYER
- Analytics
- Data products
- AI workloads
Sources
Establish what each system can give, how often, and at what cost to it.
Source profile per system
Pattern
Select batch, incremental, change data capture or streaming per source.
Integration architecture
Map
Reconcile schemas, types, keys and business meaning between systems.
Mapping specification
Build
Implement pipelines with transformation, validation and idempotent loads.
Working pipelines
Reconcile
Prove destination matches source, and keep proving it.
Reconciliation controls
Orchestrate
Schedule, sequence, monitor and alert.
Operated data flow
Detail
For whoever has to sign it off
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What ARRIX delivers
- Source system analysis and integration pattern selection
- ETL and ELT pipeline design and build
- Change data capture where a source cannot tolerate extraction load
- API and web service integration
- Schema mapping, transformation and standardisation
- Data migration with validation and reconciliation
- Orchestration, scheduling, retry and alerting
- Documentation and handover so the pipelines are maintainable by the client
What you receive
- Integration architecture
- Built and tested pipelines
- Schema and mapping documentation
- Reconciliation and validation reports
- Orchestration configuration with monitoring and alerting
- Runbook and handover documentation
Technical benefits
- Source-appropriate extraction: batch, incremental, change data capture or event stream
- Schema mapping and transformation held in version control rather than in someone's tooling
- Reconciliation between source and destination so silent data loss is detectable
- Orchestration with dependency awareness, retry and alerting
- Idempotent loads, so a re-run repairs rather than duplicates
- Integration logic separated from business logic, so either can change without the other
Governance and security
- Source credentials held in a secret store, never in pipeline code
- Least-privilege service accounts per source rather than shared administrative access
- Personal and sensitive fields identified at mapping time and handled under the applicable policy
- Lineage recorded from source field to destination field
- Transfer encrypted in transit, and at rest in every intermediate location
Technology options
| Area | Candidates | How it is chosen |
|---|---|---|
| Movement | Batch ETL/ELT, Change data capture, API integration, Event streaming, File-based transfer | Selected per source. Most estates need several patterns, and forcing one across all sources is a common and expensive mistake. |
| Orchestration | Workflow orchestrators, Managed pipeline services, Native platform scheduling | Chosen against the team that will operate it, not against feature count. |
| Destination | Cloud data warehouse, Lakehouse, Operational data store | Follows the workload; see Big Data & Lakehouse Engineering where platform scale is the question. |
When you may not need this
A business running on one system that already reports adequately does not need an integration layer. The case begins when the same data has to be combined from more than one place, when someone is moving it by hand, or when a downstream capability needs it more current or more reliable than manual movement can be.
Common Questions
Answered plainly
Can you connect to our legacy system?
Usually, and it is one of the more common reasons organisations get in touch. The route depends on what the system exposes - a database, an API, a file drop, or change capture at the storage layer. Where a system genuinely exposes nothing, that constraint is identified during analysis rather than discovered mid-build.
Will this affect the performance of our production systems?
It can, and that is precisely why the extraction pattern is chosen per source. Change data capture and off-peak incremental extraction exist because querying a busy transactional system directly is often unacceptable. Source impact is an assessment question, not an afterthought.
Do we need real-time?
Less often than expected. Real-time costs more to build and more to operate, and the honest test is whether a decision or an automated action actually changes because the data is seconds old rather than hours old. Where it does, see Real-Time Data Engineering; where it does not, scheduled loading is cheaper and steadier.
We already have integrations. Can you work with them?
Yes, and starting from what exists is usually cheaper than replacing it. The first step is establishing what each does, whether it is reliable, and whether it is documented. Some are kept, some are consolidated because three of them read the same source, and some are replaced because nobody can safely change them.
Who owns the pipelines afterwards?
You do. Pipelines are delivered with mapping documentation, orchestration configuration and a runbook, and the technology is chosen with the team that will operate it in mind. Where you would rather ARRIX continued to operate them, that is Managed Data & AI Engineering.
Next Step
What ARRIX needs to know
These are the questions a scoped proposal answers. Bring what you can; the rest is established in the conversation.
- Which systems need to be connected, and which is the system of record for each entity?
- How current does the data need to be - daily, hourly, or as it happens?
- Can the source systems tolerate being queried, or do they need change capture?
- Are there existing integrations, and are they documented?
- Who is doing this manually today, and how long does it take them?
- Are there API limits, licensing constraints or vendor restrictions on extraction?
- What happens downstream if a load fails - who needs to know, and how fast?
- Who will maintain these pipelines after handover?