You’re comparing Census vs Google Cloud Dataflow vs Weld. Explore how they differ on connectors, pricing, and features. 
Loved by data teams from around the world
| Weld | Census | Google Cloud Dataflow | |
|---|---|---|---|
| Connectors | 200+ | 130+ | 30+ |
| Price | $99 / 5M Active Rows | Free tier; Pro ~$350/mo for 2 destinations | Per vCPU-second ($0.0106/vCPU-minute) + RAM and storage; streaming pipelines incur additional costs |
| Free tier | |||
| Location | EU | US | 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 | 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 | 4.5 | 4.5 |
Overview
Census is a leading Reverse ETL platform that syncs data from your data warehouse back into SaaS tools like Salesforce, HubSpot, and Marketo. It integrates deeply with dbt and modern warehouses, enabling no-code mapping, incremental upserts, and reliable syncs to operational systems in near real time.
Warehouse-centric: works directly on tables or dbt models
No-code field mapping with live previews
Broad destination support: Salesforce, HubSpot, Slack, Google Sheets, etc.
Incremental upserts ensure data consistency with no duplicates
Deep dbt integration and “analytics-as-code” workflows (YAML config)
Flexible scheduling and API triggers for near real-time use cases
Focuses only on reverse ETL—requires a separate ELT tool for ingestion
Pricing based on rows or syncs can be expensive at very large volumes
Complex transformations must be done upstream in the warehouse/dbt
Dependent on destination API rate limits, which can slow large syncs
SaaS-only (no on-prem deployment)
Census Overview (G2):
“Census is the fastest and most reliable reverse ETL platform with 99.5% uptime and premium support for all plans. It delivers transformed data at time-of-use—fueling rapid data activation in operational tools without relying on leaky pipelines.”
Overview
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.
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.
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.
G2 Reviews:
“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”
“It is costly as compared to other solutions”
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
Census provides an intuitive UI for mapping warehouse fields to destination fields, with live previews—non-technical business users quickly adopt it for operational data syncs.
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 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
Census provides an intuitive UI for mapping warehouse fields to destination fields, with live previews—non-technical business users quickly adopt it for operational data syncs.
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 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
While there’s a free tier to get started, professional plans based on usage can add up for large enterprises. Still, ROI often justifies the investment by automating data activation in CRM/marketing tools.
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 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
While there’s a free tier to get started, professional plans based on usage can add up for large enterprises. Still, ROI often justifies the investment by automating data activation in CRM/marketing tools.
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 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
Census focuses on reverse ETL: no-code mapping, incremental upserts keyed on primary keys, scheduling, and deep dbt integration. It provides monitoring dashboards, alerting, and a CLI/API for GitOps workflows.
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 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
Census focuses on reverse ETL: no-code mapping, incremental upserts keyed on primary keys, scheduling, and deep dbt integration. It provides monitoring dashboards, alerting, and a CLI/API for GitOps workflows.
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 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
Mapping logic is as flexible as your SQL—any complex query can feed Census. Users can customize scheduling (cron or event triggers) and configure failure handling. It adapts well but relies on warehouse transformations for complex logic.
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 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
Mapping logic is as flexible as your SQL—any complex query can feed Census. Users can customize scheduling (cron or event triggers) and configure failure handling. It adapts well but relies on warehouse transformations for complex logic.
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 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.
AWARD WINNING ETL PLATFORM
Spend less time managing data and more time getting real insights.
