Top Azure data factory Alternatives & Competitors

Revanth Periyasamy
July 24, 2025

Top Azure data factory Alternatives & Competitors in 2025

As organizations scale their data operations, many CTOs reassess whether a traditional ETL tool like Azure Data Factory (ADF) remains the best fit. Azure Data Factory is Microsoft’s cloud-based ETL/ELT service, offering 90+ built-in connectors and deep integration with the Azure ecosystem for orchestrating data pipelines.

However, its consumption-based pricing (billed per activity and data movement) can lead to unpredictable costs, and complex pipelines can require significant engineering effort. ADF is also primarily a batch-oriented tool with limited built-in transformation features or downstream “activation” (e.g. reverse ETL) out of the box.

As a result, over two-thirds of organizations report evaluating alternatives to their data integration platforms due to cost, complexity, and feature needs. For example, teams often seek more predictable pricing, easier management, or integrated analytics that ADF alone doesn’t provide.

Why consider alternatives to Azure Data Factory?

Azure Data Factory enables cloud-scale ETL and ELT workflows across on-premises and cloud data sources. It provides a visual pipeline designer, scheduling, and supports many data sources (e.g. SQL Server, Salesforce, SAP). However, growing data teams sometimes find ADF’s model limiting. Its usage-based billing (per activity run and data volume) can make budget planning difficult. Complex data transformations typically require external tools (like SSIS or Databricks) rather than being handled natively.

Moreover, Azure Data Factory focuses on moving and transforming data, but lacks the broader “data activation” and analytics features (like embedded visualization or built-in warehousing) offered by some modern platforms.

In practice, this means teams may invest in multiple Azure services or custom code to fill gaps. In short, cost concerns, operational overhead, and the desire for richer features drive many CTOs to explore alternatives that promise simpler pricing, turnkey transformations, and unified data/analytics capabilities.

ADF competitors & alternatives in 2025

Below are ten leading alternatives to Azure Data Factory, including both commercial platforms and open-source projects.

Each entry summarizes what the tool is, key capabilities, and why it might be chosen (or not) compared to ADF. We include key features, typical use cases, and both advantages and drawbacks to help you make an informed choice.

Azure Data Factory Alternatives: Top 10

1. Peliqan – All-in-One Modern Data Stack for Agile Teams

Peliqan is the #1 Azure Data Factory alternative, delivering an all-in-one, no-code/low-code modern data stack. Founded to eliminate complexity, Peliqan unifies data integration, warehousing, transformation, orchestration, and activation in a single platform designed for agile teams and CTOs seeking rapid deployment and simplicity.

Core Capabilities and Differentiators

Fully integrated data stack: ETL, ELT, reverse ETL, BI, and activation
250+ connectors to databases, APIs, SaaS tools, and files
Built-in PostgreSQL warehouse or connect your own
Federated query engine powered by Trino
Visual workflows + SQL + AI-based transformation

Transparent Pricing

Starter: $380/month
Pro: $550/month

Technical Architecture

Cloud-native SaaS with SOC 2 Type II compliance
Real-time federated queries across cloud and on-prem sources
Data activation: Reverse ETL, webhooks, API publishing
Multi-cloud support (AWS, Azure, GCP)

Why choose Peliqan: Rapid deployment, all-in-one functionality, transparent pricing, and enterprise-grade security make it ideal for modern data teams replacing Azure Data Factory.

2. Fivetran – Zero-Maintenance Enterprise ETL

Fivetran is an enterprise-grade platform offering fully automated data pipelines for organizations prioritizing reliability and minimal maintenance. With 500+ connectors, it’s ideal for teams looking to outsource schema management and updates.

Core Capabilities

Automated schema evolution and real-time syncing
Change data capture (CDC) support
Comprehensive connector library for enterprise data sources

Pros

Extremely low maintenance with auto-recovery and alerting
Industry-leading uptime and support

Cons

Premium pricing
Limited custom transformations compared to ADF or Peliqan

3. Stitch – Lightweight ETL for Startups

Stitch, owned by Talend, is a developer-friendly ETL tool ideal for small to medium-sized businesses. It focuses on simplicity and ease of use, offering quick connector setups and basic data pipelines.

Core Capabilities

Prebuilt connectors for SaaS apps and databases
Batch data loads to warehouses
Basic monitoring and retry logic

Pros

Quick onboarding and setup
Affordable pricing

Cons

Limited data transformation features
Not ideal for large-scale enterprise use

4. Airbyte – The Open Source Data Integration Leader

Airbyte is an open-source platform offering 600+ connectors with a strong developer community. It provides excellent flexibility for organizations wanting control over their data integration infrastructure.

Core Capabilities

Open-source connector framework
Self-hosted and cloud deployment options
Support for dbt-based transformations

Pros

Highly customizable and extensible
Cost-effective for engineering-driven teams

Cons

Requires DevOps and maintenance effort
Inconsistent quality in community-built connectors

5. Matillion – ELT for Cloud Data Warehouses

Matillion is a purpose-built ELT platform designed to optimize data processing in modern cloud data warehouses like Snowflake, BigQuery, and Redshift.

Core Capabilities

Visual ETL/ELT builder with SQL transformation support
Push-down ELT execution model
Integration with cloud platforms

Pros

Warehouse-native performance
Intuitive UI with visual orchestration

Cons

Limited reverse ETL capabilities
Can be expensive for scaling teams

6. Talend – The Legacy ETL Powerhouse

Talend offers robust enterprise-grade data integration, transformation, and governance capabilities. It’s known for its legacy presence and compliance-oriented features.

Core Capabilities

Visual ETL designer
Data quality, governance, and lineage tools
Broad connector support

Pros

Trusted by large enterprises
Strong governance features

Cons

Legacy interface and slower iteration pace
High complexity and licensing costs

7. Informatica – Comprehensive Data Management Platform

Informatica delivers an enterprise-grade platform for data integration, quality, governance, and master data management. It’s best suited for highly regulated industries.

Core Capabilities

Wide range of data services including MDM
AI-powered automation
Extensive connector ecosystem

Pros

Highly scalable and reliable
Strong security and compliance features

Cons

Expensive and complex to implement
Not optimized for agile data teams

8. Keboola – Unified Platform for Data Ops

Keboola is a data operations platform that offers ETL/ELT, orchestration, and transformation with built-in versioning and automation support for modern engineering workflows.

Core Capabilities

Component-based integration model
Built-in orchestration and Git integration
Data sharing and pipeline collaboration

Pros

Strong developer tooling and automation support
Flexible deployment and pricing

Cons

Not as intuitive for non-engineers
Smaller connector catalog vs. others

9. Apache NiFi – Visual Flow-Based Programming

Apache NiFi is an open-source tool that provides visual programming for building real-time data flows, ideal for on-prem or hybrid environments.

Core Capabilities

Drag-and-drop flow builder
Built-in provenance tracking
Support for IoT and edge data processing

Pros

Real-time stream and batch processing
Open-source and extensible

Cons

Manual scaling and performance tuning required
Requires infrastructure and operational support

10. Tray.ai – Low-Code Workflow Automation

Tray.ai is a general-purpose automation and integration platform with strong support for business users and low-code data flow creation.

Core Capabilities

Low-code builder with logic-based flow controls
Pre-built connectors for SaaS apps and APIs
Integration with CRMs, marketing tools, and analytics stacks

Pros

Fast prototyping for non-engineers
Good for operational workflows

Cons

Limited support for complex data pipelines
Not designed for large-scale warehousing tasks

Azure Data Factory Alternatives Comparison

Azure Data Factory Alternatives	Key Features	Strengths	Pricing Model	Limitations
Peliqan	– Unified platform with 250+ data connectors – Built-in cloud data warehouse – Reverse ETL and real-time API publishing – Low-code Python and AI-assisted SQL	– Combines ETL, warehousing, BI, and reverse ETL – 10x faster setup for data teams – Transparent, startup-friendly pricing	Starts at $350/month with unlimited users and connectors	– Newer platform still growing enterprise presence
Fivetran	– 500+ automated connectors – Schema drift handling – Real-time sync and data governance – Managed pipelines with minimal maintenance	– Enterprise-grade automation – Proven reliability and scale – Wide integration ecosystem	MAR-based pricing starting at ~$120/month	– Limited transformation flexibility – Higher cost at scale
Stitch	– 130+ SaaS data sources – ELT with dbt integration – Simple UI and fast onboarding – Part of Talend ecosystem	– Easy to get started – Cost-effective for smaller teams – Scales with Talend for enterprise use	Free tier, paid plans from $100/month	– Limited connectors compared to larger players – No built-in transformation engine
Airbyte	– 600+ open-source connectors – AI connector builder – Self-hosted and cloud options – Reverse ETL and vector DB integration	– Customizable and extensible – Developer-first experience – Open-source transparency	Free self-hosted, cloud starts at $2.50/credit	– Steep learning curve – Connector quality varies
Matillion	– Visual ELT with pushdown compute – Supports Snowflake, BigQuery, Redshift – Advanced transformation pipelines – 100+ connectors	– Cloud-native ELT – No infrastructure to manage – Integrated with modern data warehouses	Credit-based model ~$2/credit	– Locked into specific cloud warehouses – Complex pricing
Talend	– Comprehensive data integration suite – Data quality and governance – API and master data management – Cloud, hybrid, and on-prem deployment	– Enterprise-scale platform – End-to-end data lifecycle coverage – Strong governance capabilities	Enterprise licenses starting ~$1,170/month	– Higher setup complexity – Requires dedicated data teams
Informatica	– 500+ connectors – Master data management and quality – AI-powered metadata intelligence – Cloud-native and on-prem options	– Trusted by Fortune 500s – Mature ecosystem – Full-stack data management	Starts around $100k/year for enterprise	– Expensive for mid-market – Lengthy implementation
Keboola	– Modular data operations platform – Built-in transformation and orchestration – Collaboration and governance – API-first architecture	– All-in-one with great usability – Developer and analyst-friendly – Transparent usage-based pricing	Free tier, paid starts at ~$250/month	– Less visibility compared to larger vendors – Smaller connector ecosystem
Apache NiFi	– Flow-based programming UI – 200+ built-in processors – Real-time and batch processing – Strong security and audit features	– Highly flexible and visual – Open-source and extensible – Proven enterprise deployments	Free OSS, infra cost varies ($300–$3,000+)	– Manual infrastructure required – Requires engineering support
Tray.ai	– Low-code integration platform – Workflow automation – 600+ app connectors – Designed for business and IT users	– Ideal for SaaS workflow automation – User-friendly interface – Rapid deployment	Quote-based enterprise pricing	– Not designed for large-scale data pipelines – Limited data transformation capabilities

Conclusion

In summary, each of these alternatives brings a different mix of capabilities that can address the gaps left by Azure Data Factory. Some (Peliqan, Matillion, Keboola) aim to provide end-to-end data platforms, while others (Fivetran, Stitch, Airbyte) focus on streamlined data ingestion.

Enterprise tools like Informatica and Talend offer depth and governance, and lightweight platforms like Tray.io and NiFi serve specific use cases. Peliqan (ranked #1 above) is especially notable for unifying data pipelines, storage, transformation, and analytics into one cohesive platform with transparent pricing.

Explore your options based on your team’s technical skills, data stack, and integration needs. The right platform can save hours of engineering time and accelerate insights across your organization.

FAQs

1. What is the replacement of Azure Data Factory in AWS?

AWS Glue is considered the closest replacement for Azure Data Factory in the Amazon Web Services ecosystem. It is a fully managed ETL (Extract, Transform, Load) service that automates data preparation for analytics and machine learning. AWS Glue offers similar capabilities, such as data cataloging, job orchestration, and serverless execution.

2. Which is better, Azure Data Factory or Synapse?

Azure Data Factory (ADF) is primarily a data integration and pipeline orchestration tool, while Azure Synapse Analytics is an end-to-end analytics service that combines big data and data warehousing. If you’re focused on ETL/ELT and data movement, ADF is more specialized. However, Synapse may be better for advanced analytics, SQL-based exploration, and unified data workflows across big data and traditional data warehouses.

3. Which is better, Azure Data Factory or Databricks?

Azure Data Factory excels in orchestrating data workflows with low-code/no-code interfaces, especially when you need to move and transform data across systems. Databricks, on the other hand, is a more advanced analytics platform built around Apache Spark, suited for complex data science, machine learning, and big data processing. For simple ETL use cases, ADF is easier; for large-scale data engineering, Databricks is more powerful.

4. Does Azure have an ETL tool?

Yes, Azure provides Azure Data Factory as its primary ETL (Extract, Transform, Load) tool. ADF supports data ingestion from over 90 sources, data transformation using code-free and code-based options (such as Data Flows or custom Spark), and seamless integration with Azure services like Synapse, Databricks, and Azure SQL Database. It’s designed to support both hybrid and cloud-native data movement.

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Revanth Periyasamy

Revanth Periyasamy is a process-driven marketing leader with over 5+ years of full-funnel expertise. As Peliqan's Senior Marketing Manager, he spearheads martech, demand generation, product marketing, SEO, and branding initiatives. With a data-driven mindset and hands-on approach, Revanth consistently drives exceptional results.

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