Comparing Azure Data Factory with Google Cloud Dataflow and Weld

You’re comparing Azure Data Factory vs Google Cloud Dataflow vs Weld. Explore how they differ on connectors, pricing, and features.

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Weld vs Azure Data Factory vs Google Cloud Dataflow

	Weld	Azure Data Factory	Google Cloud Dataflow
Connectors	200+	90+	30+
Price	$99 / 5M Active Rows	Pay per activity run + data movement; starts ~$0.25 per DIU-hour for data flows	Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs
Free tier
Location	EU	Azure Global (multi-region)	GCP Global (multi-region)
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)	Via CSV in Cloud Storage
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 like about Azure Data Factory

“Its flexibiliity in connecting diverse data sources and integration with the Azure ecosystem are standout advantages.”

What I dislike about Azure Data Factory

“Some features are too rigid. Lack of detailed error messages can plague a workstream during setup.”

Read full review

Overview

Google Cloud Dataflow in Short

Google Cloud Dataflow is a fully managed stream and batch processing service based on Apache Beam. It enables users to write ETL pipelines in Java or Python, which Dataflow executes on Google’s serverless infrastructure with autoscaling. It integrates natively with Pub/Sub, BigQuery, Cloud Storage, and other GCP services for end-to-end data processing.

Pros

Unified batch + streaming model via Apache Beam SDK (Java/Python).
Serverless autoscaling with dynamic work rebalancing for cost and performance optimization.
First-class integration with GCP services: Pub/Sub, BigQuery I/O connectors, Cloud Storage, Spanner, etc.
Built-in exactly-once processing semantics and windowing capabilities for streaming ETL.

Cons

Steep learning curve if unfamiliar with Apache Beam’s abstractions (PCollections, DoFns, pipelines).
Monitoring and debugging streaming pipelines can be complex—metrics and logs often require cross-referencing.
Cost can rise quickly for large-scale streaming (billed per vCPU-second and memory). Efficient pipeline tuning is critical.

Reviews & Quotes

G2 Reviews:

What I like about Google Cloud Dataflow

“Google cloud dataflow is automatically optimize and manages resources for you this platform supports multiple programming languages including Python, java and SQL and makes it easy for developers to focus on writing codes”

What I dislike about Google Cloud Dataflow

“It is costly as compared to other solutions”

Read full review

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

“First and foremost, Weld is incredibly user-friendly. The graphical interface is intuitive, which makes it easy to build data workflows quickly and efficiently. Even with little experience in SQL and pipeline management, we found that Weld was straightforward and easy to use. What really impressed me, however, was Weld's flexibility. It was able to handle data from a wide variety of sources, including SQL databases, Google Sheets, and even APIs. The solution also allowed us to customize my data transformations in a way that best suited my needs. Whether I needed to clean data, join tables, or aggregate data, Weld had the necessary tools to accomplish the task. Weld's performance was also exceptional. I was able to run large-scale ETL jobs quickly and efficiently, with minimal downtime via a Snowflake instance and visualization via own-hosted Metabase. The solution's scalability meant that I could process more data without any issues. Another standout feature of Weld was its support. I never felt lost or unsure about how to use a particular feature, as the support team was always quick to respond to any questions or concerns that I had. Overall, I highly recommend Weld as an ETL solution. Its user-friendliness, flexibility, performance, and support make it an excellent choice for anyone looking to streamline their data integration processes. I will definitely be using Weld for all my ETL needs going forward.”

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.”

Read full review

Feature-by-Feature Comparison

Azure Data Factory

Google Cloud Dataflow

Weld

Feature

Azure Data Factory

Google Cloud Dataflow

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.

Google Cloud Dataflow

Dataflow pipelines are defined programmatically in Java or Python (Apache Beam). There is no drag-and-drop UI; developers use the Cloud Console or CLI to monitor, but pipeline creation and debugging happen in code and SDKs.

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

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.

Google Cloud Dataflow

Charges for each pipeline based on vCPU-second, memory, and persistent disk usage. Streaming jobs are billed continuously. Without careful optimization (autoscaling, batching), costs can escalate. However, for high-throughput workloads, serverless autoscaling can be cost-effective versus self-managed clusters.

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

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.

Google Cloud Dataflow

Features include: Batch & streaming unified model, windowing & triggers, exactly-once semantics, dynamic work rebalancing, and data-driven autoscaling. Supports FlexRS (spot pricing for batch) and integration with Dataflow SQL for SQL-based pipelines.

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

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.

Google Cloud Dataflow

Users write custom transforms (ParDo, Map, GroupBy), can integrate UDFs, and use side inputs. Complex workloads requiring custom logic (stateful processing, custom connectors) are fully supported via Beam SDK. Cloud features like VPC, IAM, and KMS integrate security.

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.