Looking for Azure Data Factory alternatives?
This article covers top data integration tools for various needs, such as AWS integration and automated ELT processes. Find the right tool for your requirements.
Azure Data Factory is a fully managed, serverless data integration service by Microsoft Azure. It enables the creation, scheduling, and orchestration of data pipelines for moving and transforming data across various sources. With over 90 built-in connectors, it supports both ETL and ELT processes, facilitating seamless data integration from on-premises and cloud sources to Azure data services like Synapse Analytics. ADF offers a code-free UI for intuitive pipeline authoring and robust monitoring capabilities.
Choosing the right data integration tool can be tough, but we’ve narrowed down the top alternatives to Azure Data Factory for different needs:
Best for Manufacturing Data Virtualization: Factory Thread
Best for Seamless AWS Integration: Amazon Web Services Glue
Best for User-Friendly Data Integration: Talend
Best for Automated ELT Processes: Fivetran
Best for Enterprise-Grade Data Management: Informatica
Best for Robust ETL Capabilities: Oracle Data Integrator
Best for Versatile Database Management: Microsoft SQL Server 2022
Best for Real-Time Data Flow Automation: Apache NiFi
Best for Scalable Stream Processing: Google Cloud Dataflow
Best for Agile Integration Solutions: Flowgear
Factory Thread offers a manufacturing-specific approach to data integration, providing real-time virtualization across ERP, MES, CRM, and IoT systems without duplicating data. Built for operational users, it simplifies integration with a visual low-code interface and AI-powered setup.
Azure Data Factory is a flexible cloud ETL platform but isn’t tailored to specific industries. Factory Thread fills that gap for manufacturing with domain-specific connectors, edge deployment, offline runtime support, and real-time data flows between production, quality, and business systems.
Factory Thread is the ideal solution for manufacturers who need to:
Connect ERP, MES, CRM, and IoT systems in real-time
Empower operations teams with drag-and-drop workflow tools
Enable real-time alerts and monitoring across shop floor and cloud systems
Avoid data duplication with virtualized data pipelines
Deploy flexibly: on-prem, cloud, or edge environments
|
Feature / Aspect |
Factory Thread |
Azure Data Factory |
|---|---|---|
|
Primary Use Case |
General-purpose data integration and ETL |
|
|
Industry Focus |
Manufacturing-specific |
Industry-agnostic |
|
Workflow Design |
Visual low-code, AI-guided |
Visual low-code with custom coding support |
|
Integration Targets |
ERP, MES, CRM, IoT |
Broad range: databases, files, APIs |
|
Real-Time Support |
Yes – event-driven and edge-compatible |
Partial – relies on trigger-based flows |
|
Deployment Options |
Cloud, on-prem, edge (supports offline runtime) |
Primarily cloud with some hybrid options |
|
Ease of Use |
High – built for plant and operations teams |
Moderate – requires more configuration |
|
Governance |
Role-based access, version control, audit trail |
Azure security and compliance features |
|
Best Fit |
Enterprises using Microsoft stack for ETL |
Summary:
Factory Thread is purpose-built for manufacturing firms needing fast, contextualized, and virtualized data access from the shop floor to the cloud.
Azure Data Factory is better for general cloud data integration scenarios across diverse industries. For a comparison of leading data virtualization platforms like Snowflake and Amazon Redshift, check out Snowflake vs Amazon Redshift for Data Virtualization.
AWS Glue is Amazon’s fully managed, serverless data integration service. Built for the AWS ecosystem, it excels at connecting with services like S3, Redshift, and SageMaker.
Why Choose It: Ideal if your stack is AWS-first and you want tight ecosystem integration.
Key Features: Serverless Spark-based processing, Glue Data Catalog, visual Glue Studio, and automated schema discovery.
Talend Data Fabric combines open-source flexibility with enterprise-grade features. With over 1,000 connectors, it makes integration across SaaS, databases, and big data platforms accessible.
Why Choose It: Great for teams needing a balance between low-code usability and deep technical flexibility.
Key Features: Extensive connector library, hybrid deployment, built-in data quality, and governance capabilities.
Fivetran is known for its “set it and forget it” approach to ELT. It automates schema handling, incremental updates, and connector maintenance, freeing teams from repetitive ETL work.
Why Choose It: Perfect for modern cloud data stacks that prioritize speed and automation.
Key Features: 700+ pre-built connectors, automated schema drift management, and seamless integration with Snowflake, BigQuery, and Redshift.
Informatica Cloud Data Integration is part of the Intelligent Data Management Cloud (IDMC). It’s designed for enterprises that require comprehensive data governance, quality, and master data management alongside ETL.
Why Choose It: Enterprises with regulatory requirements or hybrid setups will benefit most.
Key Features: 500+ connectors, AI-powered metadata management, advanced lineage tracking, and robust compliance support.
Oracle Data Integrator (ODI) has been a cornerstone in enterprise ETL for years, supporting high-performance batch processing and complex data transformations.
Why Choose It: Best for Oracle shops or enterprises looking for mature ETL with strong on-premise + cloud options.
Key Features: Native integration with Oracle databases, high-volume performance tuning, and real-time change data capture.
SQL Server 2022 has evolved into more than a database—it’s a versatile platform with integrated data virtualization and pipeline management features.
Why Choose It: Excellent for businesses already invested in Microsoft’s ecosystem that want strong data management without full dependency on ADF.
Key Features: PolyBase for external data querying, native integration with Azure Synapse, and enhanced governance tools.
Apache NiFi is an open-source tool focused on real-time data ingestion, routing, and transformation. It’s designed for high-volume, low-latency pipelines.
Why Choose It: Perfect for IoT, streaming analytics, or scenarios where real-time movement matters most.
Key Features: Drag-and-drop flow designer, back-pressure handling, strong provenance tracking, and support for numerous protocols.
Google Cloud Dataflow, based on Apache Beam, is a fully managed stream and batch processing service. It scales seamlessly for large-scale event-driven workloads.
Why Choose It: Best fit for teams building advanced real-time analytics or ML pipelines on Google Cloud.
Key Features: Autoscaling, unified batch + stream processing, and deep integration with BigQuery and Pub/Sub.
Flowgear is an integration platform-as-a-service (iPaaS) that emphasizes speed and agility. With pre-built connectors and drag-and-drop design, it helps teams integrate quickly.
Why Choose It: A good choice for SMBs and mid-size enterprises needing quick, cost-effective integration.
Key Features: 200+ connectors, real-time monitoring, hybrid deployment options, and workflow automation.
Choosing the best Azure Data Factory alternative involves considering:
Scalability
Cost
Ease of use
Integration capabilities
For example, if your organization is heavily invested in AWS services, Amazon Web Services Glue would be a good choice as it has better integration and is deeply integrated with the multi cloud strategies of the AWS ecosystem including managed services.
Also consider the complexity of your data workflows and the need for real-time data processing. Tools like Apache NiFi, with data lake and data lakes, and Google Cloud Dataflow excel at real-time data movement and large scale big data processing within a data pipeline.
For enterprises that require extensive data governance and single view of business data, Informatica provides enterprise grade business intelligence data management solutions including a data warehouse.
Lastly, budget and specific use cases should guide your decision. While tools like Talend has user friendly interface for those without programming skills, high cost post Qlik acquisition might be a concern. Evaluate each tool based on your organization’s needs to find the best fit.
In summary, the data integration landscape offers many alternatives to Azure Data Factory, each excels in different areas. From Factory Thread’s data virtualization to Google Cloud Dataflow’s stream processing, there is a solution for every need. Understand your requirements and evaluate each tool to find the best for your organization.
AWS Glue is a good alternative to Azure Data Factory because of its seamless integration with other AWS services which is beneficial for businesses within the AWS ecosystem. This holistic integration improves data workflows and operational efficiency.
Talend is user friendly because of its graphical interface which simplifies data pipeline design and makes it accessible to users without programming skills. This way more users can engage with data integration tasks.
Fivetran automates ELT, has over 300 pre-built connectors and allows flexible data sync schedules. This ensures data management is efficient and reliable for your organization.
Google Cloud Dataflow handles large scale data processing by a fully managed service that supports both stream and batch data processing for complex workflows and big data.
When choosing Azure Data Factory alternative, consider scalability, cost, ease of use, integration capabilities and your business needs.