Microsoft Azure data integration

Azure Data Factory consulting services

Azure Data Factory (ADF) is Microsoft’s cloud-based data integration service for building scalable ELT and ETL pipelines across on-premises and cloud data sources. Solvaria’s Azure Data Factory consulting services help enterprise organizations design, implement, and manage ADF environments that are built for reliability, maintainability, and performance—delivered by engineers with deep hands-on experience on the Microsoft Azure platform.

When Azure Data Factory implementations underdeliver

Azure Data Factory is a powerful tool, but implementations built without experienced engineering frequently accumulate technical debt quickly. Pipelines without proper error handling fail silently. Transformation logic embedded in activities becomes impossible to test or version. Performance issues surface at scale that weren’t apparent during development. Organizations find themselves maintaining fragile environments that require constant intervention—undermining the reliability that data-dependent operations require.

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Solvaria’s approach to Azure Data Factory

Our Azure engineers treat ADF implementations as production software, not configuration exercises. We design pipeline architectures with modularity, testability, and operational visibility in mind—building frameworks that scale as data volumes grow and source systems change. We apply Azure best practices for linked service management, integration runtime configuration, and parameterization, producing ADF environments that are well-documented and maintainable by your team.

Engagements range from greenfield ADF implementations to audits and modernization of existing environments. We also provide ongoing management for organizations that want senior Azure engineering support without building that capability in-house.

Core capabilities

ADF pipeline design and build

Architect and implement end-to-end data integration pipelines in Azure Data Factory, covering ingestion, transformation, and loading for on-premises and cloud data sources.

Linked service and integration runtime configuration

Design and manage linked service connections and self-hosted integration runtime configurations that enable secure, reliable connectivity to on-premises and cloud source systems.

Parameterization and reusable pipeline patterns

Build parameterized, reusable pipeline templates that reduce duplication, simplify maintenance, and accelerate development of new integrations.

Error handling and monitoring

Implement comprehensive error handling, retry logic, and Azure Monitor alerting so pipeline failures are caught immediately and root causes are traceable.

Data Flow development

Design and build ADF Mapping Data Flows for complex, code-free transformations that execute at scale using Azure’s Spark-based compute.

CI/CD and DevOps integration

Implement source control, automated testing, and CI/CD pipelines for ADF using Azure DevOps, enabling reliable, repeatable deployments across development and production environments.

ADF environment audit and modernization

Assess existing ADF implementations for technical debt, undocumented logic, and performance issues, delivering a prioritized remediation plan and executing improvements.

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Technologies and platforms we work with

Our Azure engineers work within the full Microsoft data ecosystem, connecting ADF to on-premises databases via self-hosted integration runtime and to Azure Data Lake Storage, Snowflake, and Azure Synapse Analytics as targets. Our team integrates ADF with Azure DevOps for CI/CD, Azure Monitor for operational visibility, and Azure Key Vault for secrets management, and designs environments where ADF and Databricks work in concert as complementary orchestration and processing layers. Every engagement is staffed by onshore, U.S.-based senior Azure engineers with deep, hands-on platform experience.

Let’s talk about your Azure Data Factory environment

Engage our team to review your current ADF implementation or discuss a new build. We assess your environment and define a practical path to a reliable, well-engineered Azure data integration layer.