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Data Integration & Systems Connectivity
Data integration and systems connectivity services
Keep the data in your ERP, CRM, finance, HR and project systems consistent and up to date. Konsolute engineers pipelines, synchronisation and hybrid connectivity using Microsoft Fabric, Azure Data Factory and supported source-system interfaces, so teams stop rekeying and reconciling by hand.
WHAT THIS HELPS YOU ACHIEVE
Connected information with explicit rules and accountable owners
Make data movement understandable, testable and recoverable, from the source record to the system or dataset that uses it.
Reduce repeated data handling
Replace suitable exports, rekeying and file transfers with controlled ingestion and update processes.
Improve consistency
Agree identifiers, mappings and quality rules so differences between sources can be detected and addressed.
Control data freshness
Design update frequency and change processing around the business need and the capabilities of each source.
Recover incomplete work
Track processing state and validation results, with a route to reconcile failures and restart safely.
OUR DATA INTEGRATION & SYSTEMS CONNECTIVITY SERVICES
From disconnected sources to dependable data flows
Connect a priority source, improve an unreliable exchange or establish a repeatable approach to data movement across your estate.
Source discovery & data contracts
Assess databases, business applications, APIs and file sources before designing the connection. Define authoritative records, schemas, identifiers, ownership and extraction constraints, with a clear contract for how the data will be interpreted and used.
Data pipelines & transformation
Build ingestion and transformation using Fabric Data Factory, Azure Data Factory or appropriate Azure services. Design ETL or ELT around the workload, with mappings, validation, dependencies and target loading made explicit and repeatable.
Operational data synchronisation
Connect records across systems using supported incremental reads, change tracking, events or scheduled exchange. Define update direction, conflict handling, deletion behaviour and processing checkpoints, with safe retries and reconciliation of incomplete or ambiguous changes.
Hybrid & cloud connectivity
Establish the access paths required to connect private-network, on-premises and cloud sources. Assess gateways, integration runtimes, network routes, DNS and authentication for the chosen platform, with runtime availability and credential ownership defined.
Data quality & reconciliation
Profile the source data and agree rules for required values, reference codes, duplicates and relationships. Validate transformations and destination records, separate rejected data for review and assign ownership for resolving quality issues.
Monitoring & integration lifecycle
Deploy pipeline and connectivity configuration through controlled environments. Monitor freshness, failures, volumes and reconciliation results, with runbooks for replay, backfill, source changes and connection maintenance so the integration remains operable.
CAPABILITIES & DELIVERY SCOPE
The engineering behind data that stays connected
Source behaviour, transformation rules and operating responsibilities determine whether data movement produces a useful result. These workstreams define and validate each part of the exchange.
Source profiling & interface feasibility
Review schemas, sample records, volumes and available extraction methods. Identify API limits, database access constraints and file conventions, then establish how source availability and business operating windows affect ingestion.
- Source inventory and profiling
- Supported extraction interfaces
- Volumes, limits and access windows
Ownership, identifiers & mapping rules
Define the authoritative source for each relevant record and field. Map keys, code sets and relationships, including nulls, time zones and incompatible formats. Record which transformations are allowed and which differences require a business decision.
- Record and field ownership
- Keys, formats and reference mappings
- Schema and semantic change responsibilities
Ingestion & transformation pipelines
Select the pipeline and processing approach against source, destination and volume. Separate landing, validation and transformation stages where appropriate. Define parameterisation, dependencies, target writes and the evidence required to accept a completed load.
- ETL and ELT design
- Pipeline dependencies and configuration
- Load validation and destination writes
Incremental loading & synchronisation state
Assess source-supported change tracking, CDC, watermarks or event patterns. Define checkpoints and the handling of late arrivals, duplicates and deletes. A timestamp watermark alone is not assumed to capture deleted records or establish consistent ordering across systems.
- Change capture and watermark strategy
- Deletes, late arrivals and duplicates
- Checkpoints and safe restart behaviour
Hybrid access & execution components
Choose supported gateway or runtime components for the platform and connector. Validate routing, DNS, firewall access and identity from the execution environment, with capacity, patching and availability responsibilities recorded for any customer-managed components.
- Gateway or runtime selection
- Network and authentication validation
- Component availability and maintenance
Quality controls & reconciliation
Implement agreed validation and exception rules at appropriate points in the flow. Compare record counts, keys, business totals or other control evidence as relevant. Keep rejected or uncertain records visible and define who investigates and approves correction.
- Validation and rejected-record handling
- Source-to-target control checks
- Quality ownership and correction procedures
Deployment & schema lifecycle
Version the pipeline definitions, mappings and supporting code where supported. Manage environment-specific connection details and deployment permissions. Test schema changes and source upgrades before promotion, with a plan for backfill or reprocessing when logic changes.
- Versioned integration assets
- Environment and connection configuration
- Schema-change and reprocessing controls
Freshness, telemetry & recovery
Monitor the data outcome as well as the pipeline run. Track expected arrivals, checkpoint progress, latency and reconciliation failures, with meaningful alerts. Document controlled replay and backfill, including how to avoid repeated downstream business effects.
- Freshness and processing telemetry
- Alert routing and operational evidence
- Replay, backfill and recovery runbooks
TECHNICAL DESIGN DECISIONS
Decide what consistency means for the business
The integration needs an explicit definition of correct, current and complete data, with the controls required to sustain it.
Authority and update direction
Agree the system that owns each field and whether an exchange is one-way or bidirectional. Define conflict resolution rather than allow each system to overwrite the other.
Freshness and source impact
Choose batch, incremental or event-driven movement against the business requirement. Assess source load, connector limits and the consequence of delayed data before promising update speed.
Quality and acceptance
Agree required fields, matching rules and reconciliation evidence. Moving data does not fix inconsistent definitions or establish permission to merge records automatically.
Recovery and access
Define checkpoints, safe replay and the identity used by every connection. Protect sensitive data in staging and telemetry as well as at the source and destination.
OUR APPROACH
How we deliver data integration and systems connectivity
Six stages take each data exchange from assessed sources to a reconciled, monitored connection your team can maintain.
Discover the sources
Assess source systems, interfaces, data owners and the business requirement for the destination.
Define contracts and quality
Agree identifiers, mappings, quality rules, access and the evidence of a correct and complete load.
Design the movement
Select ingestion, transformation, connectivity and synchronisation patterns, including failure handling.
Build and connect
Implement the required pipelines, connections and supporting processing through controlled changes.
Reconcile and exercise recovery
Test mappings, source-to-target results, duplicates, late data, deletions and restart behaviour where relevant.
Deploy and maintain
Validate operational monitoring and hand over ownership, connection maintenance and change procedures.
Connect.Reconcile.Maintain.
WHERE DATA INTEGRATION CAN HELP
Start with the data exchange that needs to work better
Common starting points span operational connectivity and the pipelines supplying analytics, with the exact scope established through source assessment.
- Replace recurring spreadsheet exports with scheduled, validated ingestion
- Connect customer, supplier or operational records across business systems
- Load data from private-network sources into a suitable Fabric or Azure destination
- Introduce incremental processing to reduce unnecessary full reloads
- Resolve duplicate, missing or inconsistent records using agreed quality and matching rules
- Improve unreliable pipelines with freshness monitoring, reconciliation and safe recovery
See it in practice: Konsolute built an Azure data platform for Nuclear Waste Services that ingests data from its key business systems through their APIs, including enterprise project data.
WHAT WE DELIVER
Data flows your team can trace, validate and maintain
The scope defines the data contracts, deployable assets and operational evidence required for acceptance.
Source and mapping records
Interface inventories, ownership, schemas, identifiers and agreed transformation and quality rules.
Pipelines and connectivity
The agreed ingestion, transformation, loading and connection configuration, with supporting code where required.
Validation and processing evidence
Reconciliation results, exception scenarios, checkpoint design and known limitations.
Operating procedures
Freshness monitoring, recovery and backfill runbooks, connection ownership and a prioritised improvement backlog.
FAQ
Data integration FAQs
Sources, synchronisation, incremental loads, quality and recovery.
We assess supported interfaces for databases, cloud applications, APIs, files and private-network systems. Feasibility depends on connector capabilities, vendor terms, authentication, data volumes and extraction constraints, which are recorded before implementation.
We assess Fabric Data Factory, existing Azure Data Factory estates and other appropriate Azure services against the workload and operating model. Source and destination support, transformation needs, capacity, licensing and connectivity shape the recommendation.
This page focuses on data movement, mappings, quality, synchronisation and source-to-target reconciliation. APIs can provide an interface within that solution. The separate API integration service places more emphasis on consumer contracts, gateway policies and application interactions.
Where the interfaces support it, we can design the required exchange. We define authoritative fields, update direction, conflict rules, deletes and processing state. Bidirectional synchronisation needs explicit ownership and recovery behaviour rather than simply copying every update both ways.
We assess the freshness actually needed and what the sources and platform support. Scheduled, incremental and event-driven approaches have different latency, cost and operating implications. A near-real-time objective is validated across the complete source-to-destination path.
We assess supported CDC, change tracking, watermarks or other mechanisms. The design defines initial loading, checkpoints, late changes and deletes. Timestamp-based extraction is not assumed to detect deletions, so the chosen pattern needs appropriate reconciliation or additional source support.
We profile the data and agree identifiers, matching and transformation rules with the owners. Validation can flag duplicates or incompatible reference values. Merging, rejecting or correcting records follows approved business rules rather than an assumption that similarly named records are the same.
We assess the chosen service’s supported gateway or integration-runtime pattern, network paths and authentication. Implementation includes validation from the execution environment and ownership of any customer-managed components, with availability and maintenance requirements agreed.
The design defines checkpoints, commit boundaries and source-to-target evidence so incomplete work can be identified. Recovery may involve a safe restart, replay or controlled backfill. It accounts for records already written and any downstream effects that must not be duplicated.
We define least-privilege source and destination access and appropriate credential handling. Staging, rejected records, monitoring and replay processes are included in the information-handling design, with relevant access paths tested for the agreed scope.
We can assess the current definitions, dependencies, runtime configuration and load behaviour, then agree targeted remediation or a transition plan. Compatibility, reconciliation and recovery are validated before replacing an operational exchange.
We can scope monitoring, incident investigation, connection renewal, schema changes and performance improvements. The agreement defines coverage and responsibilities across platform teams, data owners and source-system suppliers, with runbooks for recovery and reprocessing.
TAKE THE NEXT STEP
Where does your data stop flowing reliably?
Tell us which sources need to connect, where records disagree or which load still depends on manual intervention. Konsolute will help define the pipelines, connectivity and controls needed for a dependable exchange.