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Comparing 5X with StreamSets Data Collector and Weld

Carolina Russ
Carolina Russ6 min read
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What is 5X

5X is an end-to-end data platform that combines ETL (500+ connectors), a managed data warehouse, dbt-powered transformations, reverse ETL, and a built-in BI/semantic layer under one roof. It aims to deliver a unified, low-code interface where teams can onboard data, model it, activate it in operational tools, and visualize it—all in a single platform.

Pros

  • 500+ connectors for both source and destination (data warehouses, SaaS apps, ad platforms, etc.).
  • Includes managed Snowflake/BigQuery warehouse, dbt core integration for transformations, and reverse ETL to push data back to apps—all in one platform.
  • Built-in BI layer and semantic metric definitions so you can build dashboards without a separate BI tool.
  • Dedicated in-house data experts provide consulting and implementation support, accelerating time-to-value.

Cons

  • Premium pricing (Starter ~$500/mo, scaling with connectors/warehouse usage) can be high for small teams.
  • Being a newer startup, the community and third-party tutorials are limited; some advanced features are still maturing.
  • Heavily opinionated stack—if you use an alternate data warehouse or BI tool, integration can require workarounds.

5X Testimonials:

What I like about 5X

5X’s all-in-one stack reduced our tool sprawl: data ingested, transformed, and even dashboards were live in days. Their in-house experts helped us onboard quickly.

What I dislike about 5X

As a relatively new entrant, some advanced features (e.g., AI-driven pipeline suggestions) are still in beta, and pricing can scale up quickly with heavy usage.
Read full review

What is StreamSets Data Collector

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.

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.
Read full review

What is Weld

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

  • Premium quality connectors and reliability
  • User-friendly and easy to set up
  • AI assistant
  • Very competitive and easy-to-understand pricing model
  • Reverse ETL option
  • Lineage, orchestration, and workflow features
  • Advanced transformation and SQL modeling capabilities
  • Ability to handle large datasets and near real-time data sync
  • Combines data from a wide range of sources for a single source of truth

Cons

  • Requires some technical knowledge around data warehousing and SQL
  • Limited features for advanced data teams

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

5X vs StreamSets Data Collector: Ease of Use and User Interface

5X

5X’s UI guides users through onboard: connect sources, choose warehouse, define dbt models, and build dashboards—all via a low-code interface. Non-technical users can leverage pre-packaged templates, while power users can write custom SQL/dbt.

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.

5X vs StreamSets Data Collector: Pricing Transparency and Affordability

5X

5X offers a Free Forever tier for small usage (limited connectors/rows). The Starter plan (~$500/mo) covers basic use, but costs increase with data volume and connector count. ROI calculations often justify the cost by consolidating multiple tools.

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.

5X vs StreamSets Data Collector: Comprehensive Feature Set

5X

End-to-end features: ETL ingestion (500+ connectors), managed warehouse provisioning, dbt transformations, reverse ETL, built-in BI/visualization, and a semantic layer. It also includes lineage tracking and API endpoints for data apps.

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.

5X vs StreamSets Data Collector: Flexibility and Customization

5X

While 5X is tightly integrated, it allows custom dbt models, Python UDFs, and can ingest data from arbitrary APIs. If you don’t need the BI layer, you can skip that component. Custom connectors can be built upon request by their team.

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).

Summary of 5X vs StreamSets Data Collector vs Weld

Weld5XStreamSets Data Collector
Connectors200++500+200+
Price€99 / Unlimited usageFree Forever tier; Starter ~$500/month for limited usage, then scales with volumeData Collector: Free (OSS); Data Ops Platform: Custom enterprise pricing
Free tierNoYesYes
LocationEUSingapore (HQ) + USA, UK, IndiaSan Francisco, CA, USA
Extract data (ETL)YesYesYes
Sync data to HubSpot, Salesforce, Klaviyo, Excel etc. (reverse ETL)YesYesNo
TransformationsYesYesYes
AI AssistantYesNoNo
On-PremiseNoNoYes
OrchestrationYesYesYes
LineageYesYesYes
Version controlYesYesYes
Load data to and from ExcelYesYesYes
Load data to and from Google SheetsYesYesNo
Two-Way SyncYesYesNo
dbt Core IntegrationYesYesNo
dbt Cloud IntegrationYesNoNo
OpenAPI / Developer APIYesNoNo
G2 Rating4.84.94.5

Conclusion

You’re comparing 5X, StreamSets Data Collector, Weld. Each of these tools has its own strengths:

  • 5Xend-to-end features: etl ingestion (500+ connectors), managed warehouse provisioning, dbt transformations, reverse etl, built-in bi/visualization, and a semantic layer. it also includes lineage tracking and api endpoints for data apps. 5x offers a free forever tier for small usage (limited connectors/rows). the starter plan (~$500/mo) covers basic use, but costs increase with data volume and connector count. roi calculations often justify the cost by consolidating multiple tools. .
  • StreamSets Data Collectorfeatures: 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. 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. .
  • Weldweld 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.weld offers a straightforward and competitive pricing model, starting at €99 for 2 million active rows, making it more affordable and predictable, especially for small to medium-sized enterprises..
Review the detailed sections above—connectors, pricing, feature set, and integrations—and choose the one that best matches your technical expertise, budget, and use cases.

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