Automate your data flows with enterprise-grade ETL/ELT pipelines that move, transform, and orchestrate data from any source to any destination, reliably and at scale, so AI agents always work from current data.
Azure Data Factory (ADF) is Microsoft's cloud-based data integration service for building automated ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines. It enables organisations to ingest data from hundreds of sources, apply transformations, and load results into data warehouses, lakehouses, or analytics platforms — all without writing code. For organisations already using Microsoft Fabric, data pipelines are built directly within the Fabric experience. Veriland Consulting designs and implements data pipelines that keep your analytics current and your decision-making data-driven.
AI agents depend on the same pipelines. An agent reconciling yesterday's bank lines needs yesterday's data loaded and validated before it starts. The fixed-price agent pilot scores data readiness in week one and maps the agent's data sources in week two.
Organisations currently moving data manually between systems using spreadsheets, exports, and imports. Companies running SSIS packages on-premises that need to migrate to the cloud. Businesses that need to consolidate data from multiple sources (ERP, CRM, databases, files, APIs) into a unified analytics platform. Any organisation where data freshness and accuracy are critical to operations and reporting.
Agents act on whatever data they are given. Late or broken loads lead straight to wrong matches and missed exceptions.
All three client results below ran across the clients' own line-of-business applications with heavy integrations. Pipelines keep the data those agents read current and consistent.
Finance agents start the day with bank and ledger data already loaded and checked, so matching runs on complete figures.
IT agents pick up failed pipeline runs, trace the cause through run history and prepare a fix for an engineer to approve.
Before a pipeline change ships, agents compare outputs between versions and flag any change in the data it produces.
Every data set an agent uses has a traceable source, and every agent action is logged against its own identity.
Finance operations · PE-backed group
£32m
Annual savings
450+ FTE of capacity released
IT operations · PE-backed business
£7m
Added to annual EBITDA
70% Leaner IT organisation
Release testing · Business with in-house applications
15+
Testing FTE released
Every Release AI-tested before go-live
Clients are anonymised at their request. See all results.
Connect to 100+ data sources with pre-built connectors. Ingest data in batch or near-real-time from databases, APIs, files, and streaming sources.
Transform data with visual Mapping Data Flows, SQL scripts, Spark notebooks, or custom code. Supports joins, aggregations, pivots, lookups, and schema drift.
Chain activities, set dependencies, handle errors with retry and fallback logic, and orchestrate complex multi-step data workflows with parameterised pipelines.
Real-time pipeline monitoring with run history, activity-level logging, performance metrics, and configurable alerts for failures and SLA breaches.
Azure Data Factory uses consumption-based pricing where you pay per pipeline run, per data movement hour, and per compute hour for transformations. There are no upfront costs or minimum commitments. This makes it cost-effective for both small-volume and enterprise-scale workloads.
| Feature | Consumption (Pay-per-Use) | Notes |
|---|---|---|
| Pipeline Orchestration | ~£0.75/1,000 runs | Per activity run |
| Data Movement | ~£0.19/DIU-hour | DIU = Data Integration Unit |
| Mapping Data Flows | ~£0.18/vCore-hour | General compute cluster |
| SSIS Integration Runtime | ~£0.80/vCore-hour | For SSIS package execution |
| Self-Hosted IR | Free (your hardware) | For on-premises connectivity |
| Azure Monitor / Alerts | Included in Azure | Basic monitoring included |
Prices are approximate Azure pay-as-you-go rates. Actual costs depend on data volume, frequency, and transformation complexity. Reserved capacity discounts available. Contact Veriland for cost modelling.
Data pipeline costs can be difficult to estimate without understanding your data volumes and refresh frequencies. Veriland provides detailed cost modelling as part of every pipeline assessment. Get a cost estimate.
The data pipeline and ETL market includes several established players. Here is how Azure Data Factory compares with other popular data integration tools.
| Feature | Capability | Azure Data Factory | Talend | Informatica PowerCenter | Fivetran | Stitch Data |
|---|---|---|---|---|---|---|
| Visual Pipeline Designer | ||||||
| Pre-built Connectors | 100+ | 1,000+ | 200+ | 300+ | 100+ | |
| Code-Free Transformations | ||||||
| Native Azure Integration | ||||||
| Fabric / Synapse Integration | ||||||
| SSIS Migration Support | ||||||
| Serverless Execution | ||||||
| On-Premises Connectivity |
Comparison based on publicly available feature information. Individual capabilities may vary by edition.
Migrate SQL Server Integration Services packages to Azure Data Factory with the SSIS Integration Runtime. Lift-and-shift existing packages or modernise to native ADF pipelines.
Replace manual data exports, spreadsheet manipulation, and imports with automated ADF pipelines that run on schedule with error handling and monitoring.
Migrate on-premises Informatica, Talend, or custom ETL scripts to Azure Data Factory. Includes pipeline redesign, connector configuration, and performance testing.
Replace fragile scheduled scripts (Python, PowerShell, SQL Agent jobs) with managed ADF pipelines with built-in monitoring, alerting, retry logic, and dependency management.
Every pipeline migration includes assessment of existing data flows, pipeline redesign for Azure, parallel testing, and performance validation to ensure your new pipelines meet or exceed existing capabilities. Discuss your pipeline migration.
Azure Data Factory connects to over 100 data sources and destinations out of the box, including databases, file systems, SaaS applications, and streaming platforms. The self-hosted integration runtime enables secure connectivity to on-premises systems.
Data pipelines power analytics and operations across every industry. Here are common pipeline use cases by sector.
Regulatory reporting pipelines, transaction data consolidation, risk data aggregation, and daily portfolio analytics refreshes.
Production data collection, supply chain data consolidation, quality metrics pipelines, and IoT sensor data ingestion from shop floor systems.
Sales data consolidation from multiple channels, inventory analytics pipelines, customer data unification, and promotional campaign reporting.
Veriland Consulting takes a structured approach to data pipeline design and implementation that ensures reliability, performance, and maintainability.
Map your existing data flows, assess source system connectivity, document transformation logic, and identify pipeline requirements.
Design the pipeline architecture including data flow patterns, scheduling strategy, error handling, partitioning, and incremental loading approaches.
Build pipelines in ADF or Fabric, configure data flows, implement transformations, and test end-to-end with production-representative data volumes.
Deploy to production with CI/CD, configure monitoring and alerting, establish runbooks for common failure scenarios, and provide ongoing support.
Azure Data Factory (ADF) is Microsoft's cloud-based data integration service for building automated ETL/ELT pipelines. It enables you to ingest data from hundreds of sources, apply transformations, and load results into data warehouses, lakehouses, or analytics platforms — all with a visual designer and no server management.
Azure Data Factory is a standalone Azure service, while Fabric Data Pipelines are the pipeline capability built into Microsoft Fabric. They share the same underlying engine and experience, but Fabric pipelines are tightly integrated with the Fabric lakehouse, warehouse, and analytics experience. Veriland helps you choose the right option.
ADF uses consumption-based pricing. You pay per pipeline run (approximately £0.75 per 1,000 runs), per data movement hour, and per compute hour for transformations. Your monthly cost depends on run volume and data movement, and Veriland provides detailed cost modelling during assessment.
Yes. ADF includes an SSIS Integration Runtime that allows you to lift-and-shift existing SSIS packages to Azure with minimal changes. Alternatively, Veriland can modernise your SSIS packages into native ADF pipelines for a fully cloud-native solution.
Yes. The self-hosted integration runtime enables secure connectivity to on-premises databases, file shares, and applications without opening inbound firewall ports. Data is transferred securely through an outbound HTTPS connection.
ETL (Extract, Transform, Load) transforms data before loading it into the destination. ELT (Extract, Load, Transform) loads raw data first, then transforms it using the destination's compute power. ELT is generally preferred for modern cloud data platforms because it leverages the scalability of the target system.
ADF supports configurable retry policies, timeout settings, and dependency conditions. Failed activities can trigger alternative paths, send alerts, or log to monitoring systems. Veriland implements comprehensive error handling and alerting as standard in every pipeline.
ADF pipelines are primarily designed for batch and micro-batch processing. For true real-time streaming, Azure Stream Analytics or Fabric Real-Time Analytics are better options. However, ADF supports event-triggered pipelines that can run within seconds of a file arriving or an event occurring.
Veriland uses CI/CD practices for pipeline deployment. ADF integrates with Azure DevOps and GitHub for version control, automated testing, and promotion between development, staging, and production environments. This ensures reliable, repeatable deployments.
A simple data pipeline (single source to destination) takes 1 to 3 weeks. A comprehensive data platform with multiple pipelines, transformations, and monitoring typically takes 6 to 14 weeks. SSIS migration projects vary based on the number and complexity of existing packages.
Wessex Searches transformed conveyancing with a new D365 Commerce ordering platform — automating search fulfilment, customer communications, and back-office operations across six data providers.
Veriland built a production property search platform for a UK water utility processing 4,000+ orders per month — then rebuilt it from scratch as VeriConvey, an AI-native SaaS product bringing agentic automation to the entire UK conveyancing industry.
Abel & Cole upgraded from Dynamics AX 2012 to D365 Finance & Operations in 15 working days under Veriland's fixed-scope delivery model — one of the fastest ERP upgrades at this scale.
Book a free Pipeline Assessment to map your data flows and design automated, reliable pipelines with Azure Data Factory.
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