You’re comparing Weld vs Informatica PowerCenter vs StreamSets Data Collector. Explore how they differ on connectors, pricing, and features. 
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| Feature | Weld | Informatica PowerCenter | StreamSets Data Collector |
|---|---|---|---|
| Core Platform | |||
| Price | $79 / 5M Active Rows | Enterprise licensing (typically six-figure annual contracts) | Free OSS Data Collector; enterprise DataOps Platform is custom-priced |
| Free tier | No | No | Yes |
| Location | DK, (EU) | US | San Francisco, CA, USA |
| Connectors & Sync | |||
| Connectors | 200+ | 200+ | 200+ |
| Extract data (ETL) | Yes | Yes | Yes |
| Sync to HubSpot, Salesforce, Klaviyo, Excel (reverse ETL) | Yes | No | No |
| Two-Way Sync | Yes | No | No |
| Transformations & AI | |||
| Transformations | Yes | Yes | Yes |
| AI Assistant | Yes | No | No |
| dbt Core Integration | Yes | No | No |
| dbt Cloud Integration | Yes | No | No |
| Governance & DevOps | |||
| Orchestration | Yes | Yes | Yes |
| Lineage | Yes | Yes | Yes |
| Version control | Yes | Yes | Yes |
| On-Premise | No | Yes | Yes |
| OpenAPI / Developer API | Yes | Yes | No |
| Integrations | |||
| Load to/from Excel | Yes | Yes (flat file imports) | Yes (via file connectors) |
| Load to/from Google Sheets | Yes | No | No |
| Ratings | |||
| G2 rating | 4.8 | 4.3 | 4.5 |
Overview
Weld is a unified ELT and data activation platform that combines ingestion, modeling, transformations, orchestration, lineage, and reverse ETL in a single SaaS interface. With premium in-house–built connectors, an intuitive UI, and near real-time syncs, Weld enables both technical and non-technical users to create and manage data workflows efficiently. Weld also includes an AI assistant to support SQL modeling, generate transformations, and streamline repetitive tasks. Teams can ingest data from a wide range of sources—including marketing platforms, CRMs, databases, Google Sheets, Excel, and APIs—into their cloud data warehouse and activate it back into business tools.
Lineage, orchestration, and workflow features included by default
Handles large datasets and near real-time data sync
ELT and reverse ETL in one platform
User-friendly interface with minimal setup required
Flat, predictable monthly pricing model
200+ in-house–built, high-quality connectors
AI assistant for modeling and transformations
Some SQL knowledge is useful for advanced modeling
Optimized for cloud-warehouse workflows (Snowflake, BigQuery, Redshift, etc.)
Feature set is streamlined for modern ELT/activation use cases
A reviewer on G2 said:
Overview
Informatica PowerCenter is an enterprise-grade ETL platform used for large-scale, governed data integration. It provides a visual mapping designer, extensive transformation capabilities, metadata management, and workflow orchestration. PowerCenter supports heterogeneous data sources including mainframes, relational databases, ERP systems, and cloud platforms. It is commonly adopted by enterprises with complex data governance requirements and long-lived ETL pipelines.
Highly scalable ETL engine with parallel processing and pushdown optimization.
Comprehensive transformation library with data quality and profiling features.
Strong metadata management and governance capabilities.
Robust workflow orchestration with detailed logging and recovery options.
Supports heterogeneous sources including mainframe, SAP, cloud, and on-prem databases.
High total cost of ownership, including licensing, infrastructure, and specialized expertise.
Dated user interface compared to modern cloud-native ETL tools.
Steep learning curve and significant onboarding time for new developers.
Less agile for rapidly evolving data needs; upgrades and patches can be time-consuming.
G2 Reviews:
“Informatica PowerCenter has been a classic, with decades in the industry. It offers every possible connector and provides robust mapping tools.”
“The UI looks dated, and handling large mappings can be difficult without significant experience.”
Overview
StreamSets Data Collector is an open-source data integration engine designed for continuous ingestion, transformation, and delivery. It supports both streaming systems such as Kafka and Kinesis, and batch sources including JDBC and file systems. Pipelines are built using a drag-and-drop canvas, and a key differentiator is Schema Drift Detection, which helps pipelines adapt automatically as input schemas evolve. Commercial editions extend the platform with enterprise monitoring, governance, metadata, and lineage features.
Schema Drift Detection adjusts dynamically to changes in incoming data schemas.
Supports streaming and batch ingestion within the same pipeline.
Visual pipeline builder with 200+ processors and connectors.
Open-source core available; enterprise offering adds monitoring, lineage, and governance.
Open-source version lacks enterprise monitoring, lineage, and governance.
UI performance can degrade with very large or complex pipelines.
Advanced pipeline logic often requires Groovy or Java scripting.
StreamSets Data Operations Platform:
“StreamSets’ ability to automatically detect and adapt to schema changes (drift) in streaming sources greatly reduces pipeline failures.”
“The open-source feature set is limited—monitoring, lineage, and enterprise support require the paid DataOps Platform. Debugging complex pipelines can be tricky if not familiar with the UI.”
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
The interface is feature-rich but dated. Developers typically require formal training to work effectively with transformations, mappings, and workflow components.
StreamSets Data Collector provides a drag-and-drop canvas for assembling origin, processor, and destination stages. Schema drift is surfaced automatically. Simple pipelines are approachable, while advanced transformations may require scripting knowledge.
Side-by-side
Weld’s interface is built for clarity and speed, enabling users with varying levels of technical experience to manage data pipelines and models efficiently. Its built-in lineage and orchestration tools provide transparency across workflows.
The interface is feature-rich but dated. Developers typically require formal training to work effectively with transformations, mappings, and workflow components.
StreamSets Data Collector provides a drag-and-drop canvas for assembling origin, processor, and destination stages. Schema drift is surfaced automatically. Simple pipelines are approachable, while advanced transformations may require scripting knowledge.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
Pricing is enterprise-focused and often requires a significant long-term investment. Best suited for large organizations with strict governance requirements rather than small or mid-sized teams.
The open-source Data Collector is free. Enterprise capabilities such as monitoring dashboards, lineage, and governance require licensing the DataOps Platform. Pricing varies based on deployments and enterprise features.
Side-by-side
Weld offers a simple and predictable pricing model starting at $79 for 5 million active rows. This flat, usage-transparent structure makes budgeting straightforward for small and medium-sized teams.
Pricing is enterprise-focused and often requires a significant long-term investment. Best suited for large organizations with strict governance requirements rather than small or mid-sized teams.
The open-source Data Collector is free. Enterprise capabilities such as monitoring dashboards, lineage, and governance require licensing the DataOps Platform. Pricing varies based on deployments and enterprise features.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
PowerCenter offers advanced ETL transformations, workflow orchestration, metadata management, data quality features, mainframe connectivity, and pushdown optimization. It remains a strong choice for complex enterprise data engineering workloads.
Key features include schema drift detection, streaming and batch support, transformation processors, JDBC/Kafka/S3/HDFS connectors, enterprise monitoring and lineage (in paid edition), and containerized deployment.
Side-by-side
Weld provides ELT ingestion, SQL-based transformations, reverse ETL activation, data lineage, orchestration, and workflow management in a single platform. Its AI assistant accelerates modeling and transformation tasks.
PowerCenter offers advanced ETL transformations, workflow orchestration, metadata management, data quality features, mainframe connectivity, and pushdown optimization. It remains a strong choice for complex enterprise data engineering workloads.
Key features include schema drift detection, streaming and batch support, transformation processors, JDBC/Kafka/S3/HDFS connectors, enterprise monitoring and lineage (in paid edition), and containerized deployment.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Users can extend capabilities through Java transformations, stored procedures, command tasks, and custom scripts. While highly customizable, the platform is proprietary—advanced modifications depend on Informatica’s ecosystem and update cycles.
Custom processors can be written in Java or Groovy, and pipelines can be parameterized. StreamSets integrates with external orchestrators such as Airflow and monitoring tools like Prometheus or Grafana.
Side-by-side
Users can model data using SQL enhanced by Weld’s AI assistant, automate workflows, and build custom connectors to any API. This provides strong flexibility for teams that want to tailor integrations and transformations within one platform.
Users can extend capabilities through Java transformations, stored procedures, command tasks, and custom scripts. While highly customizable, the platform is proprietary—advanced modifications depend on Informatica’s ecosystem and update cycles.
Custom processors can be written in Java or Groovy, and pipelines can be parameterized. StreamSets integrates with external orchestrators such as Airflow and monitoring tools like Prometheus or Grafana.
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CUSTOMER STORIES
“One of the biggest impacts has been unlocking new ways to buy media. Before, we didn’t have the data to back up strategic decisions – now we do.”
“Before Weld, we had to rely on custom Python scripts and manual processes that were time-consuming and error-prone.”
“We’re still a team of three, but we’re often doing far more than the equivalent of three full-time employees. That’s down to how we're able to leverage systems, data, and processes.”
“We’ve always been a KPI-driven company. But we wanted to scale that mindset across every team member, every team, every decision.”
“Before Weld, our data infrastructure was limited and we relied heavily on Excel files and custom Python scripts.”
“Weld has saved us a ton of time, from not having data ready to having a fully functional data warehouse and connectors.”
“By integrating Weld, Amazon S3, and Databricks, Soundboks built a modern data pipeline that automates data ingestion, improves reporting, and provides up-to-date visibility into sales performance”
“We didn’t have a good data setup, so we lacked the business insights we needed. Weld has allowed us to set up a structured data infrastructure and access insights quickly.”
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