Microsoft Azure
Azure Data Integration Services
Connect operational systems, SaaS applications and files to your Azure data platform with pipelines that are observable, tested and easy to extend.
Integrate Data Across Your Azure Estate
Every analytics, reporting or AI initiative on Azure depends on data arriving on time, complete and in a usable shape. Yet integration is where many data platforms quietly fail: pipelines built one at a time, credentials scattered across linked services, no alerting when a source changes schema. Hanoi Technology designs integration as a product, using Azure Data Factory, Data Factory in Microsoft Fabric, Fabric mirroring, Azure Event Hubs and Azure Logic Apps according to the latency, volume and ownership each flow needs.
We standardize patterns for ingestion, change data capture, transformation and error handling, so new sources take days rather than weeks to onboard. Pipelines are versioned in Git, deployed through CI/CD and monitored with clear ownership.
The Challenge
Common Data Integration Pain Points on Azure
Most integration problems come from growth without standards.
Pipeline Sprawl
Each project builds its own Data Factory pipelines with different naming, parameters and logging. Maintenance cost grows with every source, and only the original author understands each flow.
Silent Failures and Late Data
Without centralized monitoring and data freshness checks, a failed or partial load is discovered when a business user notices a wrong number in Power BI.
Choosing Between Overlapping Services
Azure Data Factory, Fabric Data Factory, mirroring, Synapse pipelines, Databricks Lakeflow and Logic Apps overlap. Picking the wrong tool for a workload adds cost and complexity.
Secure Connectivity to Private Sources
On-premises databases and private endpoints require self-hosted or on-premises data gateways, managed virtual networks and careful secret handling in Azure Key Vault.
Business Value
Benefits of a Standardized Integration Layer
Faster Source Onboarding
Metadata-driven pipelines let new tables or sources be added through configuration instead of new code, shortening delivery for each request.
Trustworthy, Timely Data
Freshness, row count and schema checks catch problems before they reach reports, and alerts route to the team that owns the source.
Lower Operating Effort
Reusable templates, Git integration and automated deployment reduce manual fixes and make pipelines maintainable by the whole team.
Near Real-Time Where It Matters
Fabric mirroring, change data capture and Event Hubs streaming deliver low-latency data for operational dashboards without overloading source systems.
Integration Is the Foundation of Every Insight
Analysts and AI models can only be as good as the data feeding them. Investing in disciplined integration pays back every time a report is trusted, a model is retrained on current data or a new source is connected in days.
Our Services
Hanoi Technology’s End-to-End Azure Data Integration Services
Design, build and operate integration for batch, change data capture and streaming workloads.
Integration Architecture
We define which service handles which pattern, including Azure Data Factory, Fabric Data Factory pipelines and Dataflow Gen2, mirroring, Event Hubs and Logic Apps, along with naming, security and environment strategy.
- Integration reference architecture
- Tool selection matrix
- Security and networking design
Metadata-Driven Ingestion Frameworks
We build parameterized pipelines driven by control tables that handle full and incremental loads, watermarking, retries and logging for many sources at once.
- Reusable pipeline templates
- Control and audit tables
- Onboarding guide for new sources
Change Data Capture and Mirroring
We replicate operational databases such as Azure SQL, SQL Server, Cosmos DB and Snowflake into OneLake with Fabric mirroring, or implement CDC patterns where mirroring is not available.
- CDC design per source
- Mirrored databases in OneLake
- Latency and load monitoring
Streaming and Event Integration
We ingest events from applications and devices with Azure Event Hubs and Fabric Eventstream, transform them in flight and land them in Eventhouse or lakehouse tables.
- Event schema design
- Streaming pipelines
- Real-time dashboards
Application and API Integration
We connect SaaS and line-of-business applications with Azure Logic Apps, Azure Functions, Service Bus and API Management when data must move between operational systems.
- Integration workflows
- API definitions and policies
- Error queues and replay
Monitoring and DataOps
We add Azure Monitor alerts, run dashboards, data quality checks and CI/CD with Azure DevOps or GitHub Actions so pipelines are tested and promoted safely.
- Monitoring dashboard
- Alert routing
- CI/CD pipelines
Our Approach
How We Deliver Azure Data Integration
A structured path from source inventory to operated pipelines.
- 01
Inventory Sources and Requirements
We list sources, volumes, change rates, latency requirements, owners and access constraints, and agree service-level expectations for each data flow.
Deliverables: Source inventory · Data flow SLAs
- 02
Design Patterns and Platform
We select services per pattern, design networking and gateways, and define naming, parameterization and logging standards.
Deliverables: Integration architecture · Standards document
- 03
Build the Framework
We implement the reusable ingestion framework and connect the first priority sources end to end, including tests and alerts.
Deliverables: Working framework · First sources in production
- 04
Scale Source Onboarding
We onboard remaining sources through configuration, adding transformation logic and data quality rules where needed.
Deliverables: Onboarded sources · Data quality rules
- 05
Operate and Hand Over
We tune performance and cost, document runbooks and train your team, or continue running the pipelines as a managed service.
Deliverables: Runbooks · Training sessions
Use Cases
Data Integration Use Cases
Scenarios where disciplined integration on Azure makes a measurable difference.
ERP and CRM Consolidation
Finance and sales data from Dynamics 365, SAP and Salesforce lands in a single lakehouse each night, giving one version of revenue and pipeline figures.
Operational Database Replication
Production Azure SQL databases are mirrored into OneLake so analysts query near real-time copies without affecting application performance.
IoT and Telemetry Ingestion
Device telemetry flows through Event Hubs into Eventhouse, where engineers monitor equipment health and trigger alerts on anomalies.
File and Partner Data Exchange
CSV and JSON files from partners arrive in Azure Data Lake Storage, are validated automatically and rejected with clear feedback if they break the contract.
Legacy SSIS Modernization
Existing SSIS packages run in the Azure-SSIS integration runtime as a first step, then are gradually rebuilt as native pipelines.
Order-to-Fulfillment Events
Order events published to Service Bus are consumed by warehouse and billing systems through Logic Apps, with dead-letter queues for failed messages.
Technology
Azure Integration Technologies
Services we combine according to each workload’s latency and volume.
- Azure Data Factory
- Data Factory in Microsoft Fabric
- Dataflow Gen2
- Fabric Mirroring
- OneLake
- Azure Event Hubs
- Fabric Eventstream
- Azure Logic Apps
- Azure Functions
- Azure Service Bus
- Azure API Management
- Azure Data Lake Storage
- Azure Key Vault
- Azure Databricks
- Azure DevOps
Platforms
Platforms We Integrate with Azure
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Food delivery
AI dispatch and live tracking for a food delivery startup
A fast-growing urban food delivery startup was losing customers to slow deliveries at peak times. We built AI order dispatching and connected restaurants, drivers and customers in real time.
Read the Full StoryMore Success Stories
Logistics
−25%operational costsRoute optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Make Your Azure Data Flow Reliably
Book a free consultation and walk us through your sources and pain points. We will suggest the integration patterns and services that fit, and what a first delivery increment could include.
Azure Data Integration FAQ
Should we use Azure Data Factory or Data Factory in Microsoft Fabric?
Both share a similar pipeline experience, but they live in different places. Azure Data Factory is a standalone Azure service and suits organizations that are not adopting Fabric or need its mature feature set and integration runtimes. Data Factory in Fabric is natural when your analytics platform is Fabric, because pipelines and Dataflow Gen2 write directly to OneLake under Fabric capacity. We compare connectors, networking, cost model and roadmap for your case before recommending one.
When is mirroring better than a traditional pipeline?
Fabric mirroring continuously replicates supported databases into OneLake in Delta format with very little configuration, which is ideal when you need near real-time copies for analytics. Traditional pipelines remain better when you need to filter, reshape or combine data during ingestion, when the source is not supported by mirroring, or when loads must follow a business schedule. Many platforms use both.
How do you connect to on-premises sources securely?
We use self-hosted integration runtimes for Azure Data Factory or on-premises data gateways for Fabric, installed on hardened servers in your network. Traffic leaves over outbound encrypted connections, so no inbound firewall ports are needed. Credentials are stored in Azure Key Vault and accessed with managed identities where possible. For cloud sources we prefer private endpoints and managed virtual networks.
What drives the cost of data integration on Azure?
Costs come from pipeline orchestration activities, data movement hours, transformation compute, and for Fabric, the capacity that runs the workloads. Design choices matter: incremental loads instead of full reloads, appropriate scheduling, and avoiding unnecessary copies can reduce spend significantly. We estimate run costs during design and set up monitoring so you can see costs per pipeline after go-live.
How do you test data pipelines?
We test at several levels: unit tests for transformation logic, integration tests that run pipelines against sample data in a development workspace, and data quality checks for row counts, nulls, duplicates and schema changes in production. Pipelines are stored in Git and promoted between environments through CI/CD, so every change is reviewed and tested before it reaches production.
Can you take over pipelines another team built?
Yes. We start with a review of existing pipelines, linked services, runtimes, failure history and documentation. We then stabilize the most fragile flows, add monitoring and alerting, and gradually refactor toward a standard framework. You receive an assessment report with prioritized fixes before we change anything, so you can decide how far to go.