What I like about Azure Data Factory
“Its flexibiliity in connecting diverse data sources and integration with the Azure ecosystem are standout advantages.”
You’re comparing Azure Data Factory vs Dell Boomi vs Weld. Explore how they differ on connectors, pricing, and features.


Loved by data teams from around the world
| Weld | Azure Data Factory | Dell Boomi | |
|---|---|---|---|
| Connectors | 200+ | 90+ | 200+ |
| Price | $99 / 5M Active Rows | Pay per activity run + data movement; starts ~$0.25 per DIU-hour for data flows | Subscription-based (per Atom/connection; starts ~$1000/month) |
| Free tier | |||
| Location | EU | Azure Global (multi-region) | Austin, TX, USA |
| Extract data (ETL) | |||
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | |||
| Transformations | |||
| AI Assistant | |||
| On-Premise | |||
| Orchestration | |||
| Lineage | |||
| Version control | |||
| Load to/from Excel | Yes (via REST connectors or staged files) | Yes (via ODBC/JDBC) | |
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.4 | 4.3 |
Overview
Azure Data Factory (ADF) is Microsoft’s cloud-based data integration service for creating ETL/ELT pipelines. ADF supports a drag-and-drop pipeline designer, over 90 built-in connectors for Azure, on-premises, and SaaS data sources, and can execute transformations via Azure Databricks, U-SQL, or stored procedures. It also includes features for data orchestration, monitoring, and hybrid data integration scenarios.

90+ built-in connectors (Azure SQL, Cosmos DB, SAP, Oracle, Salesforce, etc.) and support for custom REST endpoints.
Visual pipeline orchestration with debug, parameterization, and Git integration for CI/CD.
Hybrid data integration via Self-hosted Integration Runtime for on-premises sources.
Integration with Azure Synapse, Databricks, and Azure Functions for flexible transformation and compute.
Complex pricing: charges per pipeline activity, per DIU for data flows, and for data movement across regions.
UI can be slow when working with large pipelines; error messages are often generic, requiring deeper investigation.
Steeper learning curve for advanced features (e.g., mapping data flows with Spark under the hood).
Gartner Peer Review:
“Its flexibiliity in connecting diverse data sources and integration with the Azure ecosystem are standout advantages.”
“Some features are too rigid. Lack of detailed error messages can plague a workstream during setup.”
Overview
Dell Boomi AtomSphere is a cloud-native iPaaS that provides ETL, API management, B2B/EDI integration, and workflow automation via a visual “Atom” runtime architecture. It supports 200+ connectors (SaaS, on-prem, databases) and allows users to build, deploy, and manage integration processes (called Atoms) in a drag-and-drop interface. Boomi’s AtomSphere runs on a lightweight runtime engine that can be deployed in the cloud or on-premise for hybrid scenarios.

200+ connectors for SaaS, on-prem, and big data sources.
Cloud-native or on-prem Atom runtime allows hybrid deployments.
Visual process designer with drag-and-drop mapping, enriched by shape-specific logic (e.g., function, decision, loop).
Built-in error handling, version control, and CI/CD integration.
Costly licensing structure (per-connection, per-Atom), which can escalate for high throughput or many connectors.
Complex transformations sometimes still require scripting (JavaScript/Groovy), reducing low-code benefits for advanced scenarios.
Learning curve: mastering Atoms, Molecules, and hybrid architecture requires time, particularly for non-technical users.
Dell Boomi Documentation:
“Boomi’s AtomSphere makes deploying integration processes easy—Atommachines can run anywhere (cloud or on-prem), and the visual interface is intuitive for building mappings.”
“Licensing can be expensive, especially for high-volume data. Complex integrations can require coding despite the low-code promise.”
Overview
Weld is a powerful ETL platform that seamlessly integrates ELT, data transformations, reverse ETL, and AI-assisted features into one user-friendly solution. With its intuitive interface, Weld makes it easy for anyone, regardless of technical expertise, to build and manage data workflows. Known for its premium quality connectors, all built in-house, Weld ensures the highest quality and reliability for its users. It is designed to handle large datasets with near real-time data synchronization, making it ideal for modern data teams that require robust and efficient data integration solutions. Weld also leverages AI to automate repetitive tasks, optimize workflows, and enhance data transformation capabilities, ensuring maximum efficiency and productivity. Users can combine data from a wide variety of sources, including marketing platforms, CRMs, e-commerce platforms like Shopify, APIs, databases, Excel, Google Sheets, and more, providing a single source of truth for all their data.
Lineage, orchestration, and workflow features
Ability to handle large datasets and near real-time data sync
ETL + reverse ETL in one
User-friendly and easy to set up
Flat monthly pricing model
200+ connectors (Shopify, HubSpot, etc.)
AI assistant
Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses
A reviewer on G2 said:
“Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.”




Side-by-side

ADF’s UI provides a canvas for building pipelines and data flows. Basic data movement is intuitive, but advanced mapping data flows (visual Spark transformations) require understanding Spark concepts. Integration with Git makes collaboration easier.

Boomi’s Integration Builder uses a web-based canvas to create process flows. Connectors and maps are configured via dialogs. Error-handling, version control, and deployment controls are integrated. Some users find building very complex workflows cumbersome despite the visual design.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Side-by-side
ADF’s UI provides a canvas for building pipelines and data flows. Basic data movement is intuitive, but advanced mapping data flows (visual Spark transformations) require understanding Spark concepts. Integration with Git makes collaboration easier.
Boomi’s Integration Builder uses a web-based canvas to create process flows. Connectors and maps are configured via dialogs. Error-handling, version control, and deployment controls are integrated. Some users find building very complex workflows cumbersome despite the visual design.
Weld is highly praised for its user-friendly interface and intuitive design, which allows even users with minimal SQL experience to manage data workflows efficiently. This makes it an excellent choice for smaller data teams or businesses without extensive technical resources.
Side-by-side

ADF charges per pipeline activity (at least $0.25/activity), per DIU-hour for data flows, plus data movement costs (e.g., $0.25/GB). Estimating costs can be tricky due to these components, but pay-as-you-go avoids upfront fees.

Boomi’s pricing is multi-faceted—permanent Atom licenses, per-connection pricing, and usage-based charges for transactions. SMBs may need to request custom quotes to stay within budget.
Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Side-by-side
ADF charges per pipeline activity (at least $0.25/activity), per DIU-hour for data flows, plus data movement costs (e.g., $0.25/GB). Estimating costs can be tricky due to these components, but pay-as-you-go avoids upfront fees.
Boomi’s pricing is multi-faceted—permanent Atom licenses, per-connection pricing, and usage-based charges for transactions. SMBs may need to request custom quotes to stay within budget.
Weld offers a straightforward and competitive pricing model, starting at $79 for 5 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises.
Side-by-side

Features include: pipeline orchestration, mapping data flows (visual Spark jobs), hybrid integration via self-hosted runtime, triggers (schedule, event, tumbling window), monitoring & alerting, and integration with Azure Monitor. Also supports SSIS lift-and-shift for on-prem ETL workloads.

Features: ETL/ELT processes, API management, EDI/B2B integration, workflow automation, data quality, and master data management. It also offers training, community forums, and professional services.
Weld integrates ELT, data transformations, and reverse ETL all within one platform. It also provides advanced features such as data lineage, orchestration, workflow management, and an AI assistant, which helps in automating repetitive tasks and optimizing workflows.
Side-by-side
Features include: pipeline orchestration, mapping data flows (visual Spark jobs), hybrid integration via self-hosted runtime, triggers (schedule, event, tumbling window), monitoring & alerting, and integration with Azure Monitor. Also supports SSIS lift-and-shift for on-prem ETL workloads.
Features: ETL/ELT processes, API management, EDI/B2B integration, workflow automation, data quality, and master data management. It also offers training, community forums, and professional services.
Weld integrates ELT, data transformations, and reverse ETL all within one platform. It also provides advanced features such as data lineage, orchestration, workflow management, and an AI assistant, which helps in automating repetitive tasks and optimizing workflows.
Side-by-side

ADF allows custom .NET activities, Azure Functions, and Databricks notebooks within pipelines. It supports parameterized templates, branching, and custom Azure ML scoring steps. However, customization often requires familiarity with other Azure services.

Custom scripting is supported via Groovy or JavaScript for complex transforms. Atoms can be deployed virtually anywhere for hybrid use cases. However, you rely on Boomi for core engine updates; it’s not open-source.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
Side-by-side
ADF allows custom .NET activities, Azure Functions, and Databricks notebooks within pipelines. It supports parameterized templates, branching, and custom Azure ML scoring steps. However, customization often requires familiarity with other Azure services.
Custom scripting is supported via Groovy or JavaScript for complex transforms. Atoms can be deployed virtually anywhere for hybrid use cases. However, you rely on Boomi for core engine updates; it’s not open-source.
Weld offers advanced SQL modeling and transformations directly within its platform with the help of AI, providing users with unparalleled control and flexibility over their data. Leveraging its powerful AI capabilities, Weld automates repetitive tasks and optimizes data workflows, allowing teams to focus on getting value and insights. Additionally, Weld's custom connector framework enables users to build connectors to any API, making it easy to integrate new data sources and tailor data pipelines to meet specific business needs. This flexibility is particularly beneficial for teams looking to customize their data integration processes extensively and maximize the utility of their data without needing external tools.
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