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Data integration & engineering
Data engineering services for Microsoft Fabric and Azure
Get data from your business applications, databases, APIs and files into reporting, analytics and AI. Konsolute builds Microsoft Fabric and Azure pipelines with incremental loading, validation, failure handling and reconciliation designed around each source system.
WHAT THIS HELPS YOU ACHIEVE
Reusable data that can be traced from source to consumption
Bring ingestion, transformation and operating controls together so downstream teams can understand what the data represents and how it was produced.
Replace repeated preparation
Turn recurring extraction and transformation steps into controlled, reusable engineering assets.
Improve confidence in the result
Validate keys, rules and source-to-target evidence, with rejected records and limitations made visible.
Process change deliberately
Handle incremental updates, deletes and late data through explicit state and loading rules.
Operate beyond the first load
Build deployment, monitoring and recovery into the pipelines that keep data products current.
OUR DATA INTEGRATION & ENGINEERING SERVICES
From raw sources to maintained data products
Onboard a priority source, engineer a curated dataset or improve the reliability and performance of an existing Fabric or Azure pipeline estate.
Source assessment & onboarding
Assess databases, business applications, APIs and files for each data product. Profile schemas, volumes, quality and source limits, then agree extraction methods, access and record identifiers.
Ingestion & pipeline development
Build data movement and orchestration with Fabric Data Factory, Azure Data Factory or other Azure services. Define schedules, dependencies and landing patterns, with validation and failure paths.
SQL & Spark transformation
Implement cleansing, standardisation, joins and enrichment in SQL or Spark notebooks. Keep business rules explicit and testable, with schema handling and performance checked against the workload.
Lakehouse & warehouse data products
Build curated tables and reusable datasets for agreed consumers. Define grain, keys, relationships and history, using layered lakehouse or warehouse patterns instead of raw source complexity.
Incremental loads & data quality
Engineer change capture and loading rules for updates, deletes, duplicates and late records. Apply validation and reconciliation, with checkpoints, rejected-record handling and safe restarts.
Deployment, monitoring & recovery
Version pipelines, notebooks and transformations, and promote changes through controlled environments. Monitor freshness, completion and quality, with runbooks for reprocessing and backfill.
CAPABILITIES & DELIVERY SCOPE
The engineering between source and business use
Reliable data products need explicit contracts, transformations and operating procedures. These workstreams connect the source constraints to the meaning, quality and lifecycle of the resulting datasets.
Source contracts & connectivity
Identify the authoritative source, required fields, record identifiers and arrival pattern. Assess connectors, APIs, gateways or runtimes, and validate authentication, source limits and network access from the real execution environment.
- Source schemas and extraction constraints
- Record identity and ownership
- Connector, network and access validation
Ingestion & orchestration
Define how data is extracted, landed and scheduled, including source dependencies and partial arrivals. Parameterise repeatable patterns and record load controls, so a successful run is never mistaken for complete coverage.
- Ingestion and landing patterns
- Dependencies and pipeline configuration
- Completeness and load-control evidence
SQL, Spark & transformation logic
Choose processing tools by data shape, volume, team skills and execution needs. Implement joins, standardisation and business rules with versioned logic and representative tests, and profile performance before scaling compute.
- SQL and Spark option assessment
- Transformation rules and reusable code
- Representative tests and performance profiling
Curated tables & consumption contracts
Define the meaning and grain of each table, including keys, reference data and history. Use raw, validated and curated layers where useful, and document freshness and quality limits for reporting, analytics or AI consumers.
- Grain, keys and history requirements
- Layered lakehouse or warehouse datasets
- Consumer definitions and freshness expectations
Incremental loading & schema change
Choose source-supported CDC, change tracking or watermarks. Define how checkpoints advance and how deletes, late changes and duplicates are treated, and assess schema drift before a changed source can alter a data product.
- Change capture and checkpoint rules
- Deletes, duplicates and late arrivals
- Schema validation and compatibility
Quality tests & reconciliation
Apply required-value, key, range and reference checks. Compare source and destination counts, totals or other agreed controls, keeping rejected data and failed checks visible, with named owners who approve corrections.
- Validation and data quality checks
- Source-to-target reconciliation
- Rejected-record review and correction
Engineering lifecycle & environment control
Define source control and supported deployment for pipelines, notebooks and data assets. Keep environment settings and credentials apart from reusable logic, and test data outcomes before promotion, with rollback agreed.
- Versioned assets and supported deployment
- Environment configuration and permissions
- Release and reprocessing criteria
Freshness, capacity & operational recovery
Monitor expected arrivals, run times, quality failures and downstream availability. Use workload evidence to review compute and storage, and define safe replay and backfill that allow for data consumers are already using.
- Freshness and outcome monitoring
- Capacity and performance review
- Recovery, replay and backfill procedures
TECHNICAL DESIGN DECISIONS
Make correctness and recovery part of the implementation
Engineering decisions determine whether the dataset remains useful as sources, demand and business definitions change.
Source and consumer meaning
Agree what each record represents and which rules belong in transformation. A shared column name does not prove two sources use the same business definition.
State and change handling
Define load boundaries, checkpoints and delete behaviour. An updated timestamp alone is not assumed to identify every change or to support a safe restart.
Quality and exception ownership
Decide which failures stop publication, which records need review and who approves corrections. Keep quality limits visible to the people using the data.
Runtime and lifecycle
Choose processing, storage and deployment patterns around measured needs. Include credentials, source changes, capacity and reprocessing in operational ownership.
OUR APPROACH
How we deliver data integration and engineering
Six stages take each source from profiled and agreed to a tested, reconciled pipeline that your team can monitor and recover.
Assess the sources
Profile schemas, quality, volume, access and the constraints on extracting the required information.
Define data contracts
Agree identifiers, mappings, consumer definitions, freshness and acceptance evidence.
Design pipelines and processing
Select ingestion, transformation, storage and change-processing patterns with explicit state and failure handling.
Build and test transformations
Implement versioned pipeline and processing assets, with representative data tests.
Validate complete loads and recovery
Reconcile source and target outcomes and exercise partial loads, duplicates, changed schemas and restart scenarios.
Release and operate
Deploy through controlled environments and hand over monitoring, runbooks and improvement priorities.
Ingest.Transform.Operate.
WHERE DATA ENGINEERING CAN HELP
Start with the dataset or pipeline that needs engineering
Examples span new data products and the remediation of existing analytics pipelines, with scope established through source and consumer assessment.
- Onboard business applications, SQL sources, APIs or files into a Fabric or Azure data platform
- Replace repeated spreadsheet preparation with reusable transformations
- Build curated lakehouse or warehouse tables with agreed grain, keys and definitions
- Introduce incremental loading and explicit delete or late-arrival handling
- Repair pipelines that finish successfully but publish incomplete or inconsistent results
- Improve deployment, quality monitoring and the ability to replay or backfill data safely
See it in practice: Konsolute engineered pipelines for Nuclear Waste Services that bring data from key business systems, including Primavera P6 project data, into a medallion-architecture Azure data platform.
WHAT WE DELIVER
Deployable engineering assets and evidence of the data outcome
The agreed scope defines the datasets, code, configuration and operating information required for acceptance.
Source and consumer contracts
Schema profiles, keys, mappings, data definitions, quality rules and freshness requirements.
Pipelines and processing assets
The agreed ingestion, orchestration, SQL, notebook or transformation code and supporting configuration.
Curated data and validation
Target tables or datasets, reconciliation results, test evidence and known quality limitations.
Operational handover
Deployment guidance, monitoring, connection ownership and recovery or backfill runbooks.
FAQ
Data engineering FAQs
Fabric and Azure, SQL or Spark, incremental loads, quality and recovery.
The scope can cover source onboarding, ingestion pipelines, transformations, curated datasets, incremental loading and operational controls. We agree the data products and consumer requirements, then define the implementation and evidence needed for acceptance.
This service engineers data for reporting, analytics and AI on Microsoft Fabric and Azure. Data integration and systems connectivity keeps operational systems, such as ERP, CRM and finance, in step with each other. Many organisations need both.
Yes. We assess Fabric Data Factory, lakehouse and warehouse workloads alongside an existing Azure data estate. Connector support, transformation needs, skills, capacity and lifecycle requirements inform the choice rather than assume every asset needs to move.
We compare the data shape, transformation complexity, volume, latency and team capability. SQL, Spark and other supported transformation tools can serve different parts of the workload. The design records why each is used and how the processing is tested and maintained.
We assess supported database, API, file and connector interfaces, including network access and any gateway or runtime requirements. Vendor permissions, rate limits, extraction windows and data ownership are identified before implementation.
No. Raw, validated and curated layers can provide useful boundaries, but the number and purpose of layers should fit the workload. We define the responsibilities, data copies and processing cost rather than impose a fixed structure on every data product.
We assess source-supported CDC, change tracking, watermarks or other patterns, then define initial loading, checkpoints and safe restart. Deletes, late records and changes to previously processed data need explicit handling and source-to-target validation.
We profile the source and agree validation, matching and correction rules with owners. Some issues can be transformed deterministically; others need rejected-record review or source remediation. The implementation does not assume similarly named records can safely be merged.
We define the expected schema and assess the effect of new, missing or changed fields on transformations and consumers. Validation and controlled promotion help prevent an unnoticed source change from silently altering the published dataset.
Recovery is designed around load boundaries, checkpoints and target-write behaviour. We identify what has already been committed before deciding whether to restart, replay or backfill. Downstream consumers and repeated processing effects form part of that decision.
This service focuses on engineering the data that reporting and analytics consume. Semantic model or report delivery can be added as a defined workstream, with shared definitions and acceptance criteria agreed at the boundary.
We assess supported source control, deployment and configuration options for the assets involved. Connections, credentials and environment-specific settings are managed deliberately, with validation and known platform limitations recorded for each release.
We can assess reliability, quality, performance and deployment practices, then deliver a prioritised remediation plan. Ongoing support can include incidents, source changes, capacity review and enhancements, with coverage and responsibilities explicitly agreed.
TAKE THE NEXT STEP
Which data product needs a stronger engineering foundation?
Tell us which sources need onboarding, which transformations still require manual work or which pipeline is hard to trust. Konsolute will help define the ingestion, processing and operating controls needed to make the data usable and maintainable.