Design and deliver reliable ETL/ELT pipelines in Microsoft Fabric - standardising ingestion, transformation, orchestration, and operational controls so data products scale safely.
Fabric’s integration experience (including Data Factory capabilities inside Microsoft Fabric) enables teams to ingest data from multiple sources and orchestrate end-to-end workflows in a unified analytics platform. It can significantly reduce time-to-value for new reporting and analytics requirements - but only when pipelines are designed with repeatability and operations in mind. Common issues include fragile ad-hoc builds, unclear error handling, uncontrolled refresh schedules, and transformations that are difficult to maintain. Over time, the platform becomes noisy and unreliable, and business users lose trust in outputs.
LW IT Solutions delivers Fabric Data Factory (ETL/ELT) Pipelines as a structured service to design, build, and operationalise ingestion and transformation patterns in Fabric. We define a delivery approach for pipelines (standards, naming, environments/workspaces, and release practices), build pipelines and transformation logic for agreed data sources and use cases, and implement monitoring and runbooks so your team can operate the solution long-term. Where required, we align pipeline design to the downstream semantic model and reporting layer (Power BI) so refresh performance and data definitions remain consistent.
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Service Overview
Highlights
- Native ETL and ELT pipelines built inside Microsoft Fabric
- Clear separation of ingestion, transformation, and orchestration concerns
- Standardised error handling, scheduling, and monitoring
- Aligned to downstream semantic models and reporting needs
- Designed for reuse as new data products are added
Business Benefits
- Provide consistent, repeatable data ingestion and transformation across Fabric workloads
- Improve data freshness and reliability for reporting and analytics consumers
- Reduce operational risk caused by ad-hoc pipelines and manual refresh processes
- Increase engineering productivity through standardised pipeline patterns
- Build confidence in data outputs with clear monitoring, logging, and ownership
Typical use cases
- Ingesting data from operational systems into Fabric for analytics
- Replacing spreadsheet-based data preparation with managed pipelines
- Standardising data refresh and transformation across multiple reports
- Preparing curated datasets for Power BI semantic models
- Establishing a repeatable ingestion pattern for new data sources
Objectives & deliverables
What Success Looks Like
- Deliver reliable ingestion and transformation for priority datasets and use cases
- Reduce manual data prep and spreadsheet-driven reporting dependencies
- Implement repeatable pipeline standards (naming, structure, error handling, and logging)
- Improve refresh reliability and data timeliness for analytics and reporting
- Enable scale: a pattern your team can reuse across additional data products
What You Get
- Pipeline design and standards pack (documented)
- Implemented Fabric pipelines for the agreed sources and transformations
- Validated outputs with defined acceptance criteria and evidence where appropriate
- Operational handover pack: runbooks, monitoring guidance, and common troubleshooting steps
- Prioritised backlog for next data products and pipeline maturity improvements
How It Works
- Scope - confirm priority data sources, target datasets, and downstream use cases
- Design - define pipeline standards, orchestration patterns, and transformation approach
- Build - implement Fabric Data Factory pipelines and dataflows for agreed sources
- Validate - test pipelines, confirm data quality, and verify refresh behaviour
- Handover - deliver runbooks, monitoring guidance, and next-step recommendations
Engagement Options
- Starter Pipelines - ingestion and transformation for 1-2 priority data sources
- Data Product Build - multiple sources with shared standards and orchestration
- Platform Foundation - pipeline standards, environments, and operational controls
- Operate - ongoing pipeline changes, monitoring refinement, and support
Common Bundles
Customers who use this service often bundle with these services
Fabric Lakehouse Design & Build
Design and build a governed Fabric lakehouse with structured OneLake storage, medallion layers, engineering standards, and controls for reliable analytics.
Fabric Data Warehouse Implementation
Design and implement Microsoft Fabric data warehouses with clear models, controlled access, and predictable performance for trusted enterprise reporting.
Power BI Dashboard Design & Integration
Power BI dashboard design and integration delivering trusted executive and operational reporting through strong data modelling, security and reliable refresh.
ETL/ELT Pipeline Design & Delivery
Design and deliver reliable ETL and ELT pipelines with batch and event-driven ingestion, monitoring, and cost-aware performance tuning.
Frequently Asked Questions
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