Best Azure Data Factory(ADF) Alternatives to Consider in 2025

You might be using Azure Data Factory ETL for data integration or considering it for your next ETL workflow. It’s simple and cloud-based, but is it the right long-term fit for your growing data needs?

If you are a data engineer, analyst, or operations team member at a small to mid-sized business, you have likely encountered challenges with current data integration solutions. By using ADF, you may be struggling with unpredictable and rising costs, over-reliance on Azure ecosystems, complex debugging, and constant performance issues. 

Finding the right tool that addresses these issues for your specific requirements can be challenging.

This comprehensive guide helps you compare the top Azure Data Factory alternatives with key use cases, pros and cons, and decision-making tips. We’ll explore solutions that offer better scalability, flexibility, and value for teams looking to optimize their data workflows in 2025.

What is Azure Data Factory?

As one of Microsoft’s data integration services, Azure Data Factory (ADF) enables businesses and organisations to create, schedule, and orchestrate data workflows. ADF comes as a fully managed cloud-based service, and it also allows data teams to move data between various sources and storage without having to manage any infrastructure. 

While ADF provides a visual interface to data teams to build ETL pipelines, it is primarily designed to serve small to medium-sized organizations that mainly work with SaaS applications and cloud storage solutions. 

Why Are People Moving Away from Azure Data Factory?

Although Azure Data Factory provides good basic capabilities, numerous limitations have forced organizations to look for substitutes:

Limited Real-Time Processing Capabilities

Azure Data Factory (ADF) finds it difficult to handle real-time data processing needs, as many users pointed out. 

“ADF fails to perform complex transformation and is limited to not being able to perform more operations than Int32.” -Aman Kumar K., Software Engineer, Enterprise (Source: G2 Review  October 25, 2024)

For businesses and organizations needing instant data insights for decision-making, this restriction becomes problematic.

Complex Pricing Structure

Azure Data Factory’s pricing can become erratic and costly as data volumes increase, particularly since pricing for pipeline orchestration, data movement (per activity run), and data volume processing add up independently. These expenses can rise rapidly for large-scale or high-frequency workflows, making budgeting challenging for organizations.

A reviewer from G2 has this to say about the pricing structure

“The one aspect that could be improved is the cost management; if not carefully monitored, the expenses can add up quickly, particularly when dealing with large volumes of data or frequent pipeline runs.” — SG Sowjanya G., Digital Education Student Ambassador (Source: G2-Review September 18, 2024)

Limited Transformation Capabilities

Users often point out ADF’s transformation constraints when compared to contemporary ELT tools. These constraints include limited support for custom SQL logic, no built-in version control for data models (dbt-style workflows), lack of native reverse ETL capabilities, and restricted flexibility when managing complex transformation pipelines

One G2 user said, 

“It is difficult to write complex transformation logic in ADF.” — KS Kamaljeet S., Software Engineer, Information Technology and Services ( Source: G2-review )

These restrictions drive teams to depend on other tools, which might add more complexity and expenses to their business flow.

Steep Learning Curve

With less comprehensive documentation, many new users find ADF features a bit difficult to learn, especially the ADF’s user interface. 

A recent reviewer from G2 stated that: 

“Proper documentation of Azure Data Factory must be maintained by Microsoft so that new users can get a basic idea of it.” — Sanjana R., Analyst (Source: G2-Reviews )

“The learning curve is steeper than expected, and our team struggled with the interface complexity”. This affects time-to-value and calls for large training budgets.

5 Azure Data Factory Alternatives to Consider

Here’s a comparison of leading Azure Data Factory alternatives:

Tool	Best For	Key Advantage	Starting Price
Hevo Data	Real-time data integration, ELT, and ETL for analytics	Ease of use, broad connector support, built-in transformations	Free Tier
Fivetran	Automated data replication, pre-built connectors	Zero-maintenance, automated schema migrations	Volume-based
Stitch	ELT for data analysts, extensive integrations	Focus on data extraction and loading, open-source friendly	Volume-based
Integrate.io	Low-code/no-code ETL, reverse ETL, data governance	User-friendly interface, comprehensive features	Custom
Keboola	Data warehousing, data transformation, full data stack	End-to-end data platform, data governance	Free Tier

Top 5 Azure Data Factory(ADF) Alternatives

Hevo Data

Hevo is a no-code, zero-maintenance data pipeline that is designed for businesses and organizations that need a reliable and real-time data integration platform with little or no technical complexities. 

Unlike Azure Data Factory, Hevo places emphasis on simplicity and automation, thus suited for teams that want to do data analysis instead of pipeline maintenance. It also supports data ingestion in Azure Data Factory and over 150+ connectors, thereby enabling seamless data movement between SaaS applications and several cloud storage services.

What differentiates Hevo from Azure Data Factory is that Hevo handles schema changes automatically, performing real-time replication of data with very little configuration. The platform caters to small- and medium-scale businesses’ data teams that require fast implementation and reliable performance without dedicated engineering resources. 

Key Features:

• Real-time data replication with sub-minute latency
• Automated schema mapping and evolution handling
• Built-in data transformation capabilities
• Advanced error handling and monitoring
• No-code interface featuring drag-and-drop functionality

Customer Review:

 According to Henry E., a senior software engineer wrote on the G2 website, “What I like best about Hevo Data is its intuitive user interface, clear documentation, and responsive technical support. The platform is straightforward to navigate, even for users who are new to data migration tools. I found it easy to set up pipelines and manage data flows without needing extensive technical support.”

Pros:

• No maintenance needed once set up initially
• Best customer support with dedicated success managers
• Clear and predictable pricing based on events processed

Cons:

• It has less customization in transformations for very complex use cases
• Not as many enterprise-level security features currently when compared to larger platforms
• Smaller connector list as compared to some other tools.

Why Hevo Over Azure Data Factory?

• Faster Time to Value: A team can create pipelines in minutes rather than weeks in Hevo’s no-code environment
• Better Real-Time Aspects: ADF works on batches; Hevo offers genuine real-time data replication
• Predictable Pricing: Pay clear and simple usage-based prices, with no hidden costs.

Pricing: 

Hevo is free to try for 14 days; after that, paid plans start at $299/month, recently slashed to $239/month for 5 million events. And there is a tailor-made pricing model for critical business options.

Fivetran

Fivetran is an enterprise-oriented automated data integration platform that handles large-scale data operations with minimal manual intervention. 

The platform automatically handles schema changes and updates the data pipeline, making it an ideal fit for businesses and organizations with complex data architectures and high-volume needs.

Fivetran thus positions itself by providing automation in data pipeline management and offers a vast connector library catering primarily to mid-market and enterprise customers who require strong, scalable solutions.

Key Features:

• 700+ prebuilt connectors
• Security and compliance features at an enterprise level
• Automatic pipeline monitoring and alerting
• Column-level lineage tracking
• Advanced data governance features

Customer Review:

 A senior data engineer on  G2 was asked what he liked the most about Fivetran, and he has the following to say. 

– It has a lot of connectors that allow you to ingest data from many sources

– It is easy to use, and the implementation is very straightforward

– Their support is very efficient and offers great insights

– It also allows SDKs to create their own connector and integrate with all tools for the strange case when they don’t have a built-in connector

Pros:

• Great automation, so less emphasis on manual maintenance
• No coding required and zero configuration ETL
• The security and compliance features stand tall for an enterprise

Cons:

• This solution is maybe a bit pricier when compared to fairly simple ones 
• Lack of customization when it comes to transformations 
• It can be another layer of complexity if you are a small team with simple needs

Why Should Fivetran Be Selected Over Azure Data Factory?

• Better Automation: The automated schema management of Fivetran is far superior to Azure Data Factory’s manual configuration.
• Better Connector Reliability: Prebuilt connectors are more reliable and frequently updated than generic connectors in Azure Data Factory.

Pricing: 

Fivetran’s pricing is primarily volume-based, calculated on Monthly Active Rows (MAR). They, however, offer a free trial to evaluate their service

Stitch 

Stitch Data offers a straightforward, cloud-first solution for getting your information from where it’s created into a central hub for analysis. At its core, Stitch is an ELT service, but with a particular emphasis on highly efficient “Extract” and “Load” phases. Stitch is a part of the Talend company. Businesses and Organizations can get insights faster with their fully automated cloud data pipelines.

What truly distinguishes Stitch is its unwavering commitment to simplicity, strongly augmented by its foundation on Singer.io.  Stitch focuses on doing one thing exceptionally well: replicating data. Its “preparation” step, as they define it, is more about making data compatible with your warehouse, rather than embedding business logic; the heavy lifting of transformation is intentionally left to be done in the destination. This lean approach is powerfully complemented by its use of Singer, an open-source standard for ETL scripts (called “taps” for sources and “targets” for destinations). 

Stitch Data is ideal for startups, smaller data teams, and engineering-led organizations that prioritize speed, simplicity, and core data replication without unnecessary complexity. 

Key Features:

• Simple to set up and configure
• Connect over 140+ of your favorite data sources
• Zero-maintenance cloud data pipelines in minutes
• Reliable monitoring and alerting system

Customer Review:

 In an interview with the G2 Team, Kristiyan D., a senior Data Scientist, rated Stitch 9 out of 10 as it allows him and his team to easily manage the extract, combine, and load process in a no-code environment. He also mentioned that the user interface and Stitch in general are very easy to use. He, however, mentioned the issue of data quality and poor customer service.

Pros: 

• Extensive data integration library
• Competitive pricing for small teams
• Strong focus on data extraction and loading (ELT).

Cons:

• Limited advanced transformation functions
• Smaller connector library than that of its competitors
• Enterprise support requires premium plans 

Why choose Stitch over Azure Data Factory?

• Simplicity: The implementation and maintenance are much simpler than ADF’s interface. 
• Cost-Effectiveness: More predictable pricing structure for small to medium data volumes.

Pricing: 

Stitch has a free tier for up to 5 million rows per month, and paid plans begin at $100/month.

Integrate.io

Integrate.io is a cloud-based ETL platform that helps users organize and prepare data for advanced business intelligence, customer data, and analytical purposes. It goes beyond basic ETL, offering a powerful suite that includes ETL & Reverse ETL, ELT & Change Data Capture (CDC), and API Management and Generation. The platform is built around an intuitive, visual no-code/low-code interface.

Its integrated API management and generation capabilities enable businesses to easily expose their data or integration workflows as secure, consumable APIs, transforming data pipelines into readily available data services. The platform also highlights specialized B2B SaaS Data solutions, offering pre-built templates and focused integration flows to address common challenges.

Integrate.io is exceptionally well-suited for organizations of all sizes looking for a versatile, yet user-friendly platform to manage their end-to-end data lifecycle without requiring deep coding expertise for each distinct task. 

Key Features

• Has hundreds of prebuilt connectors and a highly customizable Universal REST API connector.
• Has over 220 low-code transformation options to deliver on any data requirement
• Data Governance Features: Includes features for data masking, hashing, and compliance, which are crucial for sensitive data

Customer Review: 

Vinitha V., a data engineer, reviewed integrate.io on the G2 Platform. He has the following to say: “I find Integrate.io easier to use compared to other ETL tools, as it is a low-code/no-code platform. People who are not proficient in programming or coding can also easily understand and use it.”

Pros:

• Powerful transformation and data manipulation features
• Flexible development options (visual and code-based)
• Supports both ETL and Reverse ETL.

Cons:

• Higher learning curve due to feature complexity
• Custom pricing can be expensive for smaller organizations
• It may be over-engineered for simple use cases

Why Choose Integrate.io Over Azure Data Factory?

• Advanced Transformations: Superior data transformation capabilities compared to ADF’s basic offerings
• Unified Platform: Combines multiple data integration approaches in one solution

Pricing: 

Integrate.io offers custom pricing based on specific requirements. Contact their sales team for detailed quotes.

Keboola 

Keboola is a Data Platform as a Service (DPaaS) designed to manage the entire data lifecycle from end to end. At its core, Keboola automates the complex operations of connecting to disparate data sources (with a library of over 250 connectors), transforming and enriching the data, and orchestrating its delivery to any destination, be it a data warehouse, a BI tool, or custom data applications.

Keboola’s unique strength lies in its highly flexible architecture. Unlike specialized tools focused on a single domain, Keboola provides a unified and collaborative environment where technical and non-technical users can work together. It supports multiple transformation languages, including SQL, Python, and R, alongside no-code options, allowing data engineers, analysts, and scientists to use the tools they know best within a single, governed platform.

If your data pipelines are brittle and siloed across different tools, if collaboration between technical and business users is a constant challenge, or if you need a robust backbone to not only automate reporting but also operationalize data into applications and ML models, Keboola is built to provide that foundational layer.

Key Features:

• Accelerate processes, maximize productivity 
• End-to-end data pipeline for insights generation 
• Secure and democratized access 
• Built-in data governance and security  

Customer Review:

 “I have to say that coming across Keboola at the beginning of my career as a Data Analyst was the best experience I could have had. The integration with Snowflake works flawlessly, making the workspaces intuitive. Keboola works even better when a company has strong Data Governance. The Keboola Academy helped me the most at the start, but I still return to it, and as I gain more experience, I continue to get even deeper context from it,” says Zbynek V, a data quality specialist, during an interview with the  G-2 Team

Pros:

• All-in-one data processing platform 
• Excellent data governance and collaboration 
• Scalable and secure infrastructure 

Cons:

• Less intuitive for non-technical users 
• Increased overall cost of ownership
• Steep learning curve

Why Go Keboola Over Azure Data Factory?

• Full Stake Data Solution: Keboola offers a more comprehensive and all-inclusive data platform. This includes tools that can be used to build and manage data products, applications, and AI agents seamlessly without compromising security.
• Enhanced Collaboration and version control: Integrated features for data governance and teamwork.

Pricing: 

Keboola provides free unlimited ETL/ELT workflows and 700+ connectors for the free tier and custom enterprise pricing that can be negotiated with the team.

Additional Alternatives Worth Considering

Matillion

This is a cloud-native platform that provides a powerful visual interface for building data transformations and orchestrating data pipelines. Matillion is a good choice for organizations that are heavily involved in cloud data warehouses like Snowflake and bigQuery and require a robust ETL solution that can fully leverage the power of these platforms.

Talend Data Fabric

This is a unified platform that enables organizations to manage all of their enterprise data within a single environment. With its user-friendly interface, Talend Data Fabric caters to both technical and non-technical users.

Pentaho Data Integration

Pentaho is more than just ETL; it is a low-code data orchestration tool that combines different datasets from various sources into a single source of truth as a basis for analysis and reporting. With a drag-and-drop graphical interface, organizations can easily track the data lineage.

AWS Glue

This is a serverless data integration service in the AWS ecosystem. It makes it easier for organizations to discover, prepare, move, and integrate data from multiple sources for analytics and application development. It integrates very well with the AWS wide range of services, making it ideal for organizations that are already using AWS infrastructure extensively.

Informatica Cloud Data Integration

This is an AI-powered ETL and ELT platform designed to easily move, transform, and synchronize data between various target systems across the cloud at scale. It is an efficient and cost-effective data ingestion and transformation tool. Some of the standout qualities include user-friendly interface, scalability, and cloud-Native Architecture.

Factors to Consider When Choosing an Azure Data Factory Alternative

When considering other options to Azure Data Factory, it is important to consider all of the factors that align with your team’s needs, budget, and long-term strategy. Below are the top 5 factors to consider.

Factor	What to Look For	Why It Matters
Ease of Use	Intuitive UI, strong documentation, low-code or drag-and-drop options	It reduces the learning curve and accelerates adoption by your team
Pricing Transparency	Simple pricing model (subscription or usage-based), no hidden fees	It helps forecast costs and manage the team’s budget  effectively
Connector Availability	Pre-built connectors for databases, SaaS, APIs; ability to create custom connectors	Ensures smooth integration with your existing systems
Scalability and Performance	Handles large volumes, real-time streams, and complex data transformations	Supports growth without sacrificing performance
Governance and Security	Features like lineage, encryption, role-based access, and compliance with GDPR/CCPA	Maintains data integrity, privacy, and regulatory compliance

Why Hevo is the Smart Choice for Teams Growing in Data Capacity

For businesses and organizations that value simplicity, reliability, and quick implementation, Hevo stands out as an ideal Azure Data Factory alternative. Unlike other complex data systems requiring a lot of technical knowledge, Hevo lets data teams concentrate on analysis instead of pipeline maintenance.

Many of the issues that drive teams away from Azure Data Factory are addressed by the automated approach of Hevo’s platform toward schema management and real-time data replication. Hevo also offers the dependability and consistency that growing businesses need with clear pricing and first-rate customer service.

Frequently Asked Questions About Azure Data Factory Alternatives