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Azure Data Factory logo
  1. Capabilities
  2. Azure & Data Solutions

Azure Data Factory & Data Pipelines

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.

Book a Pipeline Assessment
  • Overview
  • How Data Pipelines Work
  • Key Benefits
  • Core Capabilities
  • Licensing & Pricing
  • ADF vs Competitors
  • Migration Paths
  • Integrations
  • Industry Use Cases
  • The Veriland Approach
  • Why Businesses Trust Us
  • Frequently Asked Questions
  • Related Case Studies
  • Get Started

Overview

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.

  • Organisations using automated data pipelines reduce manual data preparation time by up to 80%.
  • Azure Data Factory processes over 18 trillion records monthly across Microsoft's global customer base.
  • Automated pipelines reduce data errors by up to 95% compared to manual data handling processes.

Who needs Data Pipelines?

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.

How Azure Data Pipelines Work

Visual orchestration for complex data flows

  • Extract — Connect to 100+ data sources including D365, SQL Server, REST APIs, flat files, Azure Blob Storage, and SaaS applications with pre-built connectors
  • Transform — Apply data transformations using Mapping Data Flows (visual, code-free), Dataflow Gen2 in Fabric, or custom Spark/Databricks notebooks for complex logic
  • Load — Write results to Azure Synapse, Fabric Lakehouse, SQL databases, Blob Storage, or any supported destination with partitioning and incremental loading support
  • Orchestrate — Schedule pipelines with triggers (time-based, event-based, tumbling window), chain dependencies, handle errors with retry policies, and monitor execution in real time
Azure Data Factory pipeline visualisation

Key Benefits

  • Eliminate manual data handling — automate the entire data flow from extraction through transformation to loading, removing human error and freeing up staff time
  • Code-free development — build pipelines visually with Mapping Data Flows and a drag-and-drop interface, making data engineering accessible to broader teams
  • Handle any scale — process gigabytes to petabytes of data with automatic scaling and parallel execution across Azure's global infrastructure
  • Ensure data freshness — schedule pipelines to run at any frequency, from real-time event triggers to daily, weekly, or monthly batch jobs
  • Monitor everything — comprehensive monitoring with pipeline run history, activity-level logging, alerts on failure, and integration with Azure Monitor
  • Reduce infrastructure costs — serverless execution means you pay only for the compute used during pipeline runs, with no idle infrastructure costs

Core Capabilities

Data Ingestion

Connect to 100+ data sources with pre-built connectors. Ingest data in batch or near-real-time from databases, APIs, files, and streaming sources.

Data Transformation

Transform data with visual Mapping Data Flows, SQL scripts, Spark notebooks, or custom code. Supports joins, aggregations, pivots, lookups, and schema drift.

Pipeline Orchestration

Chain activities, set dependencies, handle errors with retry and fallback logic, and orchestrate complex multi-step data workflows with parameterised pipelines.

Monitoring & Alerting

Real-time pipeline monitoring with run history, activity-level logging, performance metrics, and configurable alerts for failures and SLA breaches.

Licensing & Pricing

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.

FeatureComponentConsumption (Pay-per-Use)Typical Monthly Cost (Mid-Volume)Notes
Pipeline Orchestration~£0.75/1,000 runs~£75-300Per activity run
Data Movement~£0.19/DIU-hour~£100-500DIU = Data Integration Unit
Mapping Data Flows~£0.18/vCore-hour~£200-800General compute cluster
SSIS Integration Runtime~£0.80/vCore-hour~£300-1,200For SSIS package execution
Self-Hosted IRFree (your hardware)£0For on-premises connectivity
Azure Monitor / AlertsIncluded in Azure~£10-50Basic 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.

Azure Data Factory vs Competitors

The data pipeline and ETL market includes several established players. Here is how Azure Data Factory compares with other popular data integration tools.

FeatureCapabilityAzure Data FactoryTalendInformatica PowerCenterFivetranStitch Data
Visual Pipeline Designer
Pre-built Connectors100+1,000+200+300+100+
Code-Free Transformations
Native Azure Integration
Fabric / Synapse Integration
SSIS Migration Support
Serverless Execution
On-Premises Connectivity
Total Cost of OwnershipLowerMidHigherMidLower

Comparison based on publicly available feature information. Individual capabilities may vary by edition.

Migration Paths

From SSIS Packages

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.

From Manual Data Processes

Replace manual data exports, spreadsheet manipulation, and imports with automated ADF pipelines that run on schedule with error handling and monitoring.

From On-Premises ETL

Migrate on-premises Informatica, Talend, or custom ETL scripts to Azure Data Factory. Includes pipeline redesign, connector configuration, and performance testing.

From Scheduled Scripts

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.

Integration Capabilities

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.

Azure Synapse
Azure Databricks
Power BI
Dynamics 365
SQL Server
Azure Blob Storage
REST APIs
On-Premises Systems
Third-Party SaaS

Industry Use Cases

Data pipelines power analytics and operations across every industry. Here are common pipeline use cases by sector.

Financial Services

Regulatory reporting pipelines, transaction data consolidation, risk data aggregation, and daily portfolio analytics refreshes.

Manufacturing

Production data collection, supply chain data consolidation, quality metrics pipelines, and IoT sensor data ingestion from shop floor systems.

Retail & eCommerce

Sales data consolidation from multiple channels, inventory analytics pipelines, customer data unification, and promotional campaign reporting.

The Veriland Approach

Veriland Consulting takes a structured approach to data pipeline design and implementation that ensures reliability, performance, and maintainability.

Pipeline Assessment

Map your existing data flows, assess source system connectivity, document transformation logic, and identify pipeline requirements.

Architecture Design

Design the pipeline architecture including data flow patterns, scheduling strategy, error handling, partitioning, and incremental loading approaches.

Build & Test

Build pipelines in ADF or Fabric, configure data flows, implement transformations, and test end-to-end with production-representative data volumes.

Deploy & Monitor

Deploy to production with CI/CD, configure monitoring and alerting, establish runbooks for common failure scenarios, and provide ongoing support.

Why Businesses Trust Veriland

80%
Less manual data preparation
95%
Fewer data errors
18T+
Records processed monthly (ADF global)
100+
Pre-built data connectors
Abel
ABF
AB Mauri
ACH
Air Liquide
Arrow
Browne Jacobson
Capgemini
EDAM
Eurofins
Graham & Brown
Normet
NHS Great Western Hospitals
Octavia Housing
Sea Cohort
Wessex Searches
Wessex Water
Abel
ABF
AB Mauri
ACH
Air Liquide
Arrow
Browne Jacobson
Capgemini
EDAM
Eurofins
Graham & Brown
Normet
NHS Great Western Hospitals
Octavia Housing
Sea Cohort
Wessex Searches
Wessex Water
Abel
ABF
AB Mauri
ACH
Air Liquide
Arrow
Browne Jacobson
Capgemini
EDAM
Eurofins
Graham & Brown
Normet
NHS Great Western Hospitals
Octavia Housing
Sea Cohort
Wessex Searches
Wessex Water

“Veriland replaced our fragile SSIS packages with Azure Data Factory pipelines in 8 weeks. We now have full monitoring, automated retries, and our finance team gets fresh data every morning instead of waiting until Wednesday.”

Data Engineering Manager, Data Engineering Manager at UK Manufacturing Firm

Frequently Asked Questions

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. A mid-volume workload typically costs £200 to £1,500 per month. 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.

Related Case Studies

AX 2012 to D365 Finance upgrade in just 3 weeks
Abel & Cole logo

AX 2012 to D365 Finance upgrade in just 3 weeks

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.

Dynamics 365 FO powering matter management for a top-50 law firm
Browne Jacobson logo

Dynamics 365 FO powering matter management for a top-50 law firm

Browne Jacobson deployed Dynamics 365 Finance & Operations as the backbone of their Matter Management System — giving fee earners, partners, and finance teams a unified view of matters, costs, and profitability.

HoloLens 2 and Dynamics 365 Remote Assist transforming clinical teaching during Covid
Great Western Hospitals NHS Foundation Trust logo

HoloLens 2 and Dynamics 365 Remote Assist transforming clinical teaching during Covid

Great Western Hospitals NHS Foundation Trust partnered with Veriland to deploy Microsoft HoloLens 2 with Dynamics 365 Remote Assist and the VeriLens Immersive Info IP — enabling multi-disciplinary clinical teaching, remote patient consultations, and real-time data sharing during the Covid-19 pandemic.

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