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Comparing Matillion with StreamSets Data Collector and Weld

You’re comparing Matillion vs StreamSets Data Collector vs Weld. Explore how they differ on connectors, pricing, and features. Ed Logo

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Weld vs Matillion vs StreamSets Data Collector

WeldMatillionStreamSets Data Collector
Connectors200+110+200+
Price$99 / 5M Active Rows$2.00 per creditData Collector: Free (OSS); Data Ops Platform: Custom enterprise pricing
Free tier
LocationEUUKSan 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 ExcelYes (via file connectors)
Load to/from Google Sheets
Two-Way Sync
dbt Core Integration
dbt Cloud Integration
OpenAPI / Developer API
G2 rating4.84.34.5

Overview

Matillion in Short

Matillion is a cloud-native data integration platform that enables enterprises to build and manage ETL/ELT pipelines using a low-code, visual interface. Designed for cloud data warehouses like Snowflake, Redshift, and BigQuery, it allows users to extract, transform, and load data from various sources efficiently. With strong scheduling, orchestration, and monitoring features, Matillion is well-suited for handling complex workflows and large-scale data transformation needs.

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Pros

  • Large number of connectors

  • Robust feature set

  • Lots of advanced features for larger enterprises and data teams

  • Has both ELT and ETL capabilities

  • On premise options

Cons

  • Expensive for small businesses

  • Steep learning curve for non-technical users

  • Requires a large upfront investment and implementation

Reviews & Quotes

A reviewer on G2 said::

What I like about Matillion

Built-in connectors to heaps of systems; ability to create custom connectors; active community and quick responses to forum questions

What I dislike about Matillion

Some components are a bit clunky; cloud version is not mature and lacks some features from the server-based version

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.

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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.

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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.

Feature-by-Feature Comparison

Feature
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Matillion

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StreamSets Data Collector

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Weld

Ease of Use & Interface

Side-by-side

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Matillion

Matillion has a steep learning curve, especially for non-technical users, due to its robust feature set and advanced capabilities designed for larger enterprises.

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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.

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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.

Pricing & Affordability

Side-by-side

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Matillion

Matillion's pricing can be expensive, particularly for small businesses, as it is based on credits and requires a significant upfront investment.

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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.

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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.

Feature Set

Side-by-side

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Matillion

Matillion offers a robust set of features with support for both ETL and ELT, along with numerous connectors and on-premise options, making it ideal for large enterprises with complex data needs.

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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.

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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.

Flexibility & Customization

Side-by-side

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Matillion

Matillion provides extensive customization and flexibility options, particularly for large enterprises, but may be overkill for smaller teams or simpler use cases.

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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).

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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.

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