10 Best Azure ETL Tools to Consider in 2025

I found it tough to manage data from different sources, keep my pipelines running smoothly, and handle growing amounts of information without constant headaches. That’s when I turned to Azure ETL tools for help. With the right tools, suddenly things got easier, data moved faster, pipelines became more stable, and scaling up wasn’t such a big problem anymore. How? Azure’s ETL tools are designed to handle complex data tasks with less effort and more reliability.

In this article, I’ll be sharing the top Azure ETL tools you could consider to make your data integration simpler, faster, and more efficient. I’ll also walk you through how to choose the right Azure ETL tool for your specific needs, so you don’t end up with something that doesn’t fit your workflow. Plus, I’ll cover some of the best practices for optimizing ETL performance in Azure, so you can avoid common mistakes and get the most out of your data pipelines.

Azure ETL Tools at a Glance: Quick Comparison Table

In-Depth Reviews: The 10 Best Azure ETL Tools for 2025

1. Hevo Data

G2 Rating: 4.3(250)
Capterra Rating: 4.7(100)

If you use Azure and need to bring data together from lots of different places like databases, SaaS apps, or APIs you need an ETL tool that just works. We built Hevo Data to make this easy for you. With us, you can connect all your data sources to Azure SQL Database or Synapse without writing any code or dealing with complicated setups.

We handle all the tough parts like: moving your business data into Azure quickly, keeping it updated in real time, and taking care of any changes in your data structure. You don’t have to worry about fixing connections or maintaining pipelines. Your Azure data warehouse always has the latest, cleanest data ready for you to use.

As your business grows, we make it easy to scale. Our pricing is simple and based on what you use with no hidden costs. With Hevo, you get fast, reliable, and automatic data flows into Azure, so you can spend your time learning from your data, not managing it.

Key Features

• No-Code Setup – We help you build and manage data pipelines without writing any code, so you save time and don’t need to be a tech expert.
• Wide Range of Connectors – We get you connected to over 150 data sources, making it easy to bring in data from wherever your business needs it.
• Real-Time Data Movement – We keep your Azure data fresh by moving it in near real-time, so your reports always show the latest information.
• Automatic Schema Handling – We take care of schema changes automatically, so you don’t have to worry about your data structure breaking your pipelines.
• Scalability – We scale with you, handling large volumes of data smoothly as your business grows.
• Monitoring and Alerts – We keep you informed with real-time monitoring and alerts, so you always know what’s happening with your data pipelines.
• Strong Security and Compliance – We protect your sensitive data with top security standards like SOC2, GDPR, and HIPAA, giving you peace of mind.

Pros

• Setup takes minutes, not days or weeks
• Handles schema changes automatically without breaking pipelines
• Transparent pricing with no hidden costs

Cons

• Limited advanced transformation features for complex data processing
• Not ideal for very large enterprise-scale Azure deployments
• Established pipelines can’t be edited and must be recreated for changes

Customer Testimonial

2. Azure Data Factory

G2 Rating: 4.6/5 (82)

Azure Data Factory helps manage complex data pipelines across both on-premises and cloud systems in large enterprises. It automates data movement between legacy databases, cloud platforms, and other sources without requiring extensive custom code.

Its visual interface makes building and scheduling workflows straightforward, even for complex setups. Azure Data Factory connects with Azure services like SQL, Synapse, and Data Lake, and its hybrid capabilities allow data to move easily between on-premises environments and the cloud.

In summary, Azure Data Factory provides the flexibility and scale needed to handle large, hybrid data landscapes, which is common in industries like finance and healthcare that work with both old and new systems.

Key Features

• 90 built-in connectors to cloud, on-premises, and SaaS data sources for unified data workflows.
  • You can quickly connect and move data from virtually anywhere without custom coding or extra licensing costs.
• Intuitive drag-and-drop interface and prebuilt templates to design, configure, and manage data pipelines.
  • Your team can build and automate complex data workflows faster, reducing development time and lowering the barrier for non-developers.
• Advanced data transformation tools, including aggregations, joins, filters, and conditional logic, all executed on scalable Spark clusters.
  • You can visually clean, enrich, and prepare data for analytics at scale, ensuring your data is always ready for business insights.

Pros

• Perfect integration with all Azure services – no compatibility issues
• Enterprise security and compliance are built into the platform
• Pay only for what you use

Cons

• Can be complex for simple data integration tasks
• Limited functionality outside the Microsoft ecosystem
• Learning curve for teams new to Azure

Customer Testimonial

3. Azure Databricks

G2 Rating: 4.5/5 (216)
Capterra Rating: 4.5/5 (22)

Azure Databricks is used for big data projects and advanced analytics in Azure. It combines Apache Spark with Azure’s cloud power, making it great for building machine learning models, running large analytics, and doing data modeling. It’s helpful when data scientists and analysts need to work together on complex projects using all the data in Azure.

What makes Azure Databricks stand out is its interactive notebook workspace, where teams can write and share code easily. It can also automatically scale Spark clusters as the work grows, so you don’t have to manage the servers. Plus, it connects well with Azure Machine Learning services, making it simple to use AI and machine learning across your organization.

With these features, Azure Databricks offers strong analytics, easy teamwork, and the ability to scale AI and machine learning projects smoothly.

Key Features

• Unified Analytics Workspace
  • Your teams can work together seamlessly, accelerating the development of data-driven solutions and insights.
• Interactive Multi-Language Notebooks for real-time collaboration and code execution.
  • Data engineers, analysts, and scientists can easily share work, visualize results, and iterate faster without switching tools.
• Automatically provisions, manages, and scales Apache Spark clusters based on workload demands.
  • You get high-performance big data processing without manual cluster management, optimizing both speed and cost for your analytics and machine learning tasks.

Pros

• Exclusive access to Azure-optimized innovations, such as the Photon engine and GPU-enabled compute.
• Streamlined billing and mission-critical support through unified Azure commerce and support channels
• Auto-scaling optimizes costs by adjusting resources automatically

Cons

• Requires Apache Spark expertise to use effectively
• Can be expensive for smaller projects
• Steep learning curve for teams new to big data

Check out our blog on Azure Data Factory vs databricks for more insights.

Customer Testimonial

4. Azure Synapse Analytics

G2 Rating: 4.5/5 (41)
Capterra Rating: 4.3/5 (32)

Azure Synapse is great for projects where you need strong analytics and easy data storage in Azure. It brings together data integration, big data processing, and data modeling all in one place. With built-in Azure data ingestion tools, you can easily bring in both structured and unstructured data from places like Azure Data Lake, Blob Storage, and SQL databases.

What makes Azure Synapse useful is that it combines data warehousing, Spark analytics, and integration tools in one workspace. You can choose to use serverless or dedicated resources depending on what your project needs. It also works well with other Azure services, so you don’t have to switch between many tools.

This means you can handle your entire data process, from bringing data in with Azure data ingestion tools to analyzing and reporting, smoothly inside the Azure Synapse architecture. It works well for big data and regular data storage projects that need data from different sources.

Key Features

• On-Demand and provisioned query processing for ad hoc analysis or dedicated resources.
  • You optimize costs and performance by matching compute resources to your specific analytics needs.
• Seamless Integration with Power BI and Azure Machine Learning
  • You can build dashboards, run machine learning models, and generate actionable insights—all within a single platform.
• Real-Time Analytics data ingestion, processing, and analytics at scale.
  • You respond to business events instantly and analyze massive datasets without delays.

Pros

The following are the Azure Synapse Analytics benefits:

• Handles enterprise-scale analysis across the entire Azure ecosystem
• Serverless options reduce costs for occasional analytical work
• Deep integration with Microsoft’s business intelligence tools

Cons

• Complex setup and management for smaller deployments
• High costs for continuous large-scale operations
• Requires expertise to optimize performance properly

Customer Testimonial

Check out our blog on Azure Synapse vs databricks for more insights.

5. Informatica

G2 Rating: 4.4/5 (85)
Capterra: 4.5/5 (42)

Informatica is a trusted ETL platform that works well with Azure. It connects to many different data sources, both on-premises and in the cloud, making it easy to move data into Azure services for analytics and reporting. Informatica is often used by organizations with complex integration needs or those wanting strong data management along with their Azure setup.

Informatica stands out because of its powerful data transformation tools, advanced data quality features, and ability to handle large, important workloads. It supports building strong data pipelines and offers good data governance. Informatica also gives you the flexibility to manage data across hybrid or multi-cloud environments, while keeping your Azure data workflows reliable and secure.

This makes Informatica a good choice for projects where you need sturdy data integration, strong control over your data, and the ability to work with both cloud and on-premises systems in your Azure environment.

Key Features

• Advanced data profiling, cleansing, validation, and error-handling features
  • You ensure trusted, high-quality data for analytics, reducing risks from data anomalies or inconsistencies.
• Parallel processing, data partitioning, and pushdown optimization to handle big data workloads efficiently.
  • You achieve faster processing times and better resource utilization, supporting growing data volumes without performance loss.
• Drag-and-drop mapping and workflow engines to ease the design, scheduling, and orchestration of ETL processes.
  • Your team can accelerate development, automate data pipelines, and reduce errors with a clear, manageable interface.

Pros

• Enterprise-grade security and governance for Azure deployments
• Comprehensive data quality and master data management
• Strong regulatory compliance support

Cons

• High cost and complexity for smaller Azure projects
• Requires significant training and expertise to implement
• Can be over-engineered for straightforward integration needs

Customer Testimonial

6. Talend

G2 Rating: 4.0/5 (63)
Capterra Rating: 4.6/5 (14)

Talend is a flexible ETL tool that works well with Azure. It lets you connect many different data sources and easily move your data into Azure Data Lake or Azure Synapse, which is helpful for analytics and data modeling.

Talend is easy to use with its drag-and-drop design and has a big library of connectors. It supports both batch and real-time data flows, so teams can set up their data pipelines just how they need. With Talend, you get an affordable and scalable way to manage your data in Azure, making your data processes simple and dependable.

Key Features

• Embedded data quality and automated checks to prevent bad data from entering your systems.
  • You maintain trustworthy, accurate data for analytics and decision-making, reducing risks from data errors.
• Change Data Capture (CDC) for real-time data replication
  • Your data stays fresh and synchronized, supporting agile business operations and timely insights.
• 900+ Connectors for universal data integration
  • You can rapidly ingest and unify data from diverse environments, ensuring comprehensive data availability without vendor lock-in.

Pros

• Flexible pricing from free open-source to enterprise licensing
• Strong community support and extensive documentation
• Comprehensive data quality and profiling tools

Cons

• Open-source version lacks enterprise features for complex deployments
• May require customization for specific Azure use cases
• Performance limitations compared to cloud-native solutions

Customer Testimonial

7. Apache NiFi

G2 Rating: 4.2/5 (24)
Capterra Rating: 4.0/5 (3)

Apache NiFi makes it easy to build and manage data flows between Azure services like Data Lake and SQL without needing a lot of coding. Its drag-and-drop interface lets users create complex pipelines quickly and also track where data comes from and goes, which is helpful for compliance.

NiFi stands out because it supports real-time data streaming, can be deployed in the cloud or on-premises, and gives detailed control over how data moves. This makes it a strong choice for complex Azure setups that need reliable and clear data integration.

Key Features

• Flow-based, visual data pipeline design
  • You can quickly build and modify complex data pipelines without coding, making data integration accessible and efficient for all skill levels.
• Guaranteed delivery with back pressure and prioritization schemes.
  • Your data always arrives safely, even under heavy loads, while you control processing order and prevent bottlenecks.
• End-to-end data provenance and security features like SSL, HTTPS, multi-tenant authorization, and role-based access control.
  • You gain full visibility and auditability of your data flows while ensuring compliance and protecting sensitive information.

Pros

• No licensing costs – completely open source
• Powerful real-time processing for Azure streaming scenarios
• Strong security features including encryption and access controls

Cons

• Requires significant technical expertise to set up and maintain
• Limited pre-built Azure-specific connectors
• Can be resource-intensive requiring careful planning

Customer Testimonial

8. Matillion

G2 Rating: 4.4/5 (82)
Capterra Rating: 4.3/5 (111)

Matillion is an easy-to-use tool made for Azure. It helps move and transform data quickly from places like Azure Data Lake into Azure Synapse or Azure SQL Database without needing much coding. Its simple drag-and-drop design makes building and managing data pipelines fast and works well even with large amounts of data. Matillion also connects smoothly with other Azure services to keep everything working together.

What makes Matillion helpful is that it lets teams keep track of their data jobs and work together easily. It fits perfectly into the Azure environment, making data flows simple to build and manage. This way, data projects run smoothly, and teams can stay productive without worrying about complicated setups.

Key Features

• Centralized pipeline orchestration, monitoring, and automation
  • You gain full control and visibility over your data workflows, making it easy to diagnose issues, automate tasks, and ensure reliability at scale.
• AI-augmented data engineering
  • Your team can handle unstructured data and advanced use cases more efficiently, freeing up time for innovation.
• Enterprise-grade security to run as SaaS or hybrid, with unlimited users and environments.
  • You can confidently scale and secure your data operations to meet enterprise requirements and compliance standards.

Pros

• Optimized specifically for Azure cloud data warehouse performance
• Leverages Azure’s compute power for efficient transformations
• Strong collaboration features with version control

Cons

• Limited to cloud data warehouse scenarios only
• Requires Azure Synapse or similar service to be effective
• Can be expensive for smaller implementations

Customer Testimonial

9. Stitch

G2 Rating: 4.4/5 (68)
Capterra Rating: 4.3/5 (4)

Stitch is a very simple tool for moving data from different sources into Azure data warehouses. It’s a good choice for small or medium businesses that want easy data syncing without complicated setup or lots of maintenance. You just set it up once and it keeps your data updated automatically.

Stitch is helpful for small teams or analysts who need SaaS data in Azure for reporting but don’t have a dedicated data engineer. It handles changes in your data structure by itself and is affordable, making it a hassle-free way to integrate data with Azure.

Key Features

• Real-time data synchronization and a scalable architecture
  • You get timely, reliable insights and can support analytics at any scale without worrying about infrastructure limits.
• Enterprise-grade security with end-to-end encryption, SOC 2 and HIPAA compliance, and secure connectivity options like SSH tunneling.
  • Your sensitive data stays protected and compliant with industry standards, reducing risk and meeting regulatory requirements.
• Open-source Singer framework to create custom connectors
  • You gain flexibility to integrate any data source, even those not natively supported, future-proofing your data strategy.

Pros

• Extremely simple setup and configuration
• Reliable automated synchronization with minimal maintenance
• Affordable pricing for small to medium businesses

Cons

• Very limited transformation capabilities – mainly replication
• Not suitable for complex integration scenarios
• Limited customization options

Customer Testimonial

10. Fivetran

G2 Rating: 4.2/5 (427)
Capterra Rating: 4.5/5 (24)

Fivetran is a fully automated tool for syncing data into Azure cloud data warehouses. It takes care of everything—moving data from over 700 sources, handling schema changes, and keeping your pipelines running without you needing to manage or fix anything. Fivetran works directly with Azure services like Synapse, Databricks, and SQL Data Warehouse, so your data is always up to date and ready for analysis.

This makes Fivetran a strong choice for analytics or BI teams who want to focus on insights instead of pipeline maintenance. It offers excellent reliability, high uptime, and a wide range of pre-built connectors, making Azure integration simple and hassle-free for any business that needs zero-maintenance data syncing. 

Key Features

• Consumption-based pricing model that is calculated per connection rather than across your entire account.
  • This usage-based model can be cost-effective for low data volumes but may become unpredictable or expensive as data sources and connections scale.
• Minimal maintenance and fully managed service
  • You save engineering time and resources, allowing your team to focus on analytics instead of infrastructure management.
• Reliable data sync and incremental updates
  • You get efficient, timely data transfers that keep analytics current without unnecessary data movement.

Pros

• Completely automated – no ongoing maintenance required
• Excellent reliability and uptime for Azure synchronization
• Pre-built connectors optimized for analytics use cases

Cons

• Limited transformation capabilities – focuses on replication only
• Higher cost per connector compared to other options
• Less flexibility for custom integration requirements

Customer Testimonial

How to Choose the Right Azure ETL Tool That Fits Your Needs

When selecting a data integration tool for Azure, look for these five key things that address common ETL challenges:

• Ease of Setup – Can your team get it running quickly without months of configuration?
• Azure Integration – Does it connect smoothly with your existing Azure services?
• Scalability – Will it grow with your business without breaking your budget?
• Maintenance Requirements – How much ongoing work does it need to keep running?
• Team Skills Match – Does it fit your team’s technical expertise level?

The right Azure ETL tool depends on your team size, budget, and complexity needs. Small teams should start with simple tools like Hevo Data or Stitch for quick wins, while growing businesses might choose Azure Data Factory or Fivetran for more power without complexity. Large enterprises with compliance needs often go with Informatica or Azure Synapse, and technical teams who want control prefer open-source options like Apache NiFi or Talend. Remember, the best tool is one your team will actually use successfully – start simple and upgrade as you grow.

Conclusion

Picking the right Azure ETL tool is key to turning your data into valuable insights quickly and reliably. Whether it’s the powerful cloud-native features of Azure Data Factory and Databricks or the automation and simplicity of Matillion and Fivetran, each tool offers unique strengths. As your data grows, choosing a solution with flexible features and transparent pricing will help you avoid surprises and keep your projects on track.

Hevo Data stands out by combining ease of use with clear, predictable costs, making it a great option for teams looking to scale smoothly. No matter which tool you decide on, you’ll be better equipped to build scalable ETL pipelines to capture the full potential of your data in Azure. 

Ready to find the perfect fit? Try a free trial or demo and take your data integration to the next level in 2025.

Frequently Asked Questions