What I like about Azure Data Factory
“Its flexibiliity in connecting diverse data sources and integration with the Azure ecosystem are standout advantages.”
Comparing Azure Data Factory with StreamSets Data Collector and Weld
You’re comparing Azure Data Factory vs StreamSets Data Collector vs Weld. Explore how they differ on connectors, pricing, and features. 
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Weld vs Azure Data Factory vs StreamSets Data Collector
| Weld | Azure Data Factory | StreamSets Data Collector | |
|---|---|---|---|
| Connectors | 200+ | 90+ | 200+ |
| Price | $99 / 5M Active Rows | Pay per activity run + data movement; starts ~$0.25 per DIU-hour for data flows | Data Collector: Free (OSS); Data Ops Platform: Custom enterprise pricing |
| Free tier | |||
| Location | EU | Azure Global (multi-region) | San Francisco, CA, 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 file connectors) | |
| Load to/from Google Sheets | |||
| Two-Way Sync | |||
| dbt Core Integration | |||
| dbt Cloud Integration | |||
| OpenAPI / Developer API | |||
| G2 rating | 4.8 | 4.4 | 4.5 |
Overview
Azure Data Factory in Short
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.
Pros
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.
Cons
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).
Reviews & Quotes
Gartner Peer Review:
What I dislike about Azure Data Factory
“Some features are too rigid. Lack of detailed error messages can plague a workstream during setup.”
Overview
StreamSets Data Collector in Short
StreamSets Data Collector is an open-source data integration engine built for continuous ingestion, transformation, and delivery—often referred to as a DataOps platform. It supports both streaming (Kafka, Kinesis) and batch (JDBC, files) data sources, with a drag-and-drop canvas to design pipelines. The standout feature is Schema Drift Detection: pipelines automatically adapt to changes in incoming data schemas. Commercial editions add operational monitoring, metadata management, and lineage.
Pros
Schema Drift Detection automatically adjusts to incoming data changes, preventing many pipeline breaks.
Supports both streaming (Kafka, Kinesis, JMS) and batch (JDBC, files) in the same pipeline.
Drag-and-drop pipeline builder with over 200 connectors and transformation processors.
Open-source core (Data Collector); enterprise edition adds operational monitoring, lineage, and governance.
Cons
Open-source lacks robust monitoring and lineage features; must pay for the Data Ops Platform for full enterprise functionality.
UI performance can degrade for very large pipelines; memory usage can be significant.
Steep learning curve for advanced pipeline patterns, especially around custom scripting in Groovy or Java.
Reviews & Quotes
StreamSets Data Operations Platform:
What I like about StreamSets Data Collector
“StreamSets’ ability to automatically detect and adapt to schema changes (drift) in streaming sources greatly reduces pipeline failures.”
What I dislike about StreamSets Data Collector
“The open-source feature set is limited—monitoring, lineage, and enterprise support require the paid Data Ops Platform. Debugging complex pipelines can be tricky if not familiar with the UI.”
Overview
Weld in Short
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.
Pros
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
Cons
Requires some technical knowledge around data warehousing and SQL
Limited features for advanced data teams
Focused on cloud data warehouses
Reviews & Quotes
A reviewer on G2 said:
What I like about Weld
What I dislike about Weld
“Weld is still limited to a certain number of integrations - although the team is super interested to hear if you need custom integrations.”
Feature-by-Feature Comparison
Feature
Azure Data Factory
StreamSets Data Collector
Weld
Feature
Azure Data Factory
StreamSets Data Collector
Weld
Ease of Use & Interface
Side-by-side
Azure Data Factory
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.
StreamSets Data Collector
The Data Collector UI is a canvas where users drag origin, processor, and destination stages. Schema drift is highlighted automatically. While basic pipelines are easy to build, complex transformations may require custom scripting in Groovy/Java.
Weld
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.
Ease of Use & Interface
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.
The Data Collector UI is a canvas where users drag origin, processor, and destination stages. Schema drift is highlighted automatically. While basic pipelines are easy to build, complex transformations may require custom scripting in Groovy/Java.
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.
Pricing & Affordability
Side-by-side
Azure Data Factory
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.
StreamSets Data Collector
Data Collector is free, but enterprise features (monitoring, lineage, role-based access) require paid Data Ops Platform licenses. Pricing is custom based on number of nodes and connectors.
Weld
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.
Pricing & Affordability
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.
Data Collector is free, but enterprise features (monitoring, lineage, role-based access) require paid Data Ops Platform licenses. Pricing is custom based on number of nodes and connectors.
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.
Feature Set
Side-by-side
Azure Data Factory
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.
StreamSets Data Collector
Features: streaming & batch pipelines, schema drift detection, transformation processors (masking, joins, lookups), origin/destination connectors (Kafka, S3, HDFS, JDBC), and enterprise ops (alerting, lineage, governance) in paid edition.
Weld
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.
Feature Set
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: streaming & batch pipelines, schema drift detection, transformation processors (masking, joins, lookups), origin/destination connectors (Kafka, S3, HDFS, JDBC), and enterprise ops (alerting, lineage, governance) in paid edition.
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.
Flexibility & Customization
Side-by-side
Azure Data Factory
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.
StreamSets Data Collector
Supports custom processors in Groovy/Java for bespoke logic. Pipelines can be parameterized and deployed in containers or VMs. Integration with external schedulers (Airflow) and monitoring tools (Prometheus, Grafana).
Weld
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.
Flexibility & Customization
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.
Supports custom processors in Groovy/Java for bespoke logic. Pipelines can be parameterized and deployed in containers or VMs. Integration with external schedulers (Airflow) and monitoring tools (Prometheus, Grafana).
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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