Matillion vs AWS Glue: Which ETL Tool Fits Your Data Stack Best?

Key Takeaways

Choose Matillion if you want a visual pipeline building and need multi-cloud warehouse support without code.

Choose AWS Glue when you’re committed to AWS infrastructure and have developers comfortable with Spark and Python.

Go with Hevo for a no-code, fully automated platform that delivers enterprise efficiency and auto-scaling at an affordable cost.

At some point in your data integration journey, you might have faced the Matillion vs AWS Glue debate. It could be for cleaner transformations, more scalable infrastructure, or simply to explore affordable options.

Matillion provides a visual, warehouse-centric way to build no-code pipelines, and AWS Glue offers a code-first, Spark-powered engine that fits naturally inside the AWS ecosystem. Both support reliable pipelines, but which one is for you?

This guide looks at the differences that matter in real operations. We’ll compare their features, use cases, pricing, and more to help you decide which data integration tool makes sense for your stack.

What Is Matillion?

G2 rating: 4.4 (81)

Garter Rating: 4.4 (174)

Matillion is a cloud-native platform that makes data integration accessible through its visual interface while still offering code-based options for developers. It provides 150+ pre-built integrations with major platforms like Snowflake, Amazon Redshift, Google BigQuery, and Databricks.

The platform pushes transformations down to your data warehouse, so your data never leaves the cloud during processing. This ensures faster performance through your warehouse’s computing power. It is a good fit for teams that want warehouse-focused ETL without heavy infrastructure management.

Key features of Matillion

• Orchestration control: Manages scheduling, dependencies, and event-driven workflows to run complex pipelines without extra tools.
• AI pipeline assistant: Helps you create pipelines and manage routine tasks using natural-language commands through Maia, a virtual data engineer.
• Version control: Lets you track changes, manage versions, and collaborate with your team using familiar DevOps workflows.
• Data security: Secures sensitive data through masking, encryption, and tokenization while integrating with third-party tools, like ALTR, for advanced access control.
• Environment flexibility: Configures pipelines across Development, Test, and Production environments automatically using dynamic variables for minimal errors.

Use cases

• Modernize ETL systems: Replace traditional on-premise ETL tools with cloud-based pipelines that run directly in your warehouse without rewrites.
• Boost BI reporting: Power dashboards in Looker, Tableau, or Power BI by creating fast, reliable transformation workflows.
• Support multi-cloud stacks: Standardize ELT pipeline design across teams that manage multiple cloud data warehouses.

Pricing

Matillion provides a usage-based pricing model.

• Developer: Unlimited projects and Git version control for one user.
• Teams: Adds audit logs and standard support for up to five users.
• Scale: Includes advanced features like security, hybrid deployment, and data lineage tracking for up to five users.

The 14-day free trial allows 500 credits.

Pros and cons

Pros:

• Supports both batch and CDC-based replication.
• Drag-and-drop debugging simplifies troubleshooting complex logic.
• Logs pipeline job history clearly for easy auditability.

Cons:

• Git integration stores JSON files, which can make conflict resolution challenging for some workflows.
• Advanced features often require core SQL expertise.
• Scalability is tied to warehouse cost, which creates budget surprises.

What Is AWS Glue?

G2 Rating: 4.3 (194)

Gartner Rating: 4.3 (496)

AWS Glue is a fully managed, serverless data integration service from Amazon Web Services (AWS). It helps you discover, prepare, and integrate data across multiple sources for analytics, machine learning, and application development.

The platform is suitable for AWS users due to its deep integration with the platform. It integrates easily with services like Amazon S3, Amazon Redshift, and Amazon Athena. Additionally, it offers 70+ third-party connectors through the AWS Marketplace. This greatly reduces setup complexity and ongoing maintenance effort for data teams.

Key features of AWS Glue

• Central data catalog: Provides a central, persistent, and Hive-compatible metadata repository and serves as the single source of truth for all AWS analytics services.
• Automatic PySpark code: Generates production-ready Python or Scala scripts automatically, based on your actions in the visual editor for easier AWS Glue ETL development.
• Job bookmarking: Keeps track of previously processed data to ensure incremental loads run efficiently without reprocessing old files.
• Native Apache Iceberg integration: Enables advanced analytics with features like time travel, schema evolution, and partitioning for large-scale datasets.
• ML data transforms: Uses machine learning-driven functions such as FindMatches to handle deduplication and record linkage, even with imperfect data.

Use cases

• Power data lakes: Transform and catalog raw S3 data into structured formats ready for querying and machine learning.
• Enable real-time analytics: Ingest streaming data from Kinesis or Kafka, transform it, and make it immediately available to BI.
• Support compliance reporting: Prepare and standardize data across multiple systems to generate regulatory or audit-ready reports efficiently.

Pricing

AWS Glue offers a resource consumption-based model.

• ETL jobs and interactive sessions: Billed per Data Processing Unit (DPU) at $0.44 per DPU-hour, charged by the second with a 1-minute minimum.
• Data Catalog: First million metadata objects and access requests each month are free. Beyond that, objects cost $1.00 per 100,000, and requests $1.00 per million.
• Crawlers: Charged $0.44 per DPU-hour with a minimum 10-minute billing per run.
• Data Brew: Interactive sessions cost $1.00 per 30 minutes; job execution runs $0.48 per node-hour.
• Data Quality: Charges $0.44 per DPU-hour for recommendations, evaluations, anomaly detection, and model retraining.
• Zero-ETL: No base fee, data ingestion compute is $1.50 per GB.

Pricing varies by region, and AWS offers a free trial for select Glue services.

Pros and cons

Pros:

• Development Endpoints allow advanced, interactive code testing.
• Detailed lineage maps simplify compliance reporting.
• Multi-environment support prevents production disruptions.

Cons:

• Steep learning curve for teams without Python or Spark experience.
• Limited connector support for non-AWS data sources.
• The UI is less intuitive than no-code AWS Glue alternatives.

Matillion vs AWS Glue vs Hevo: A Detailed Comparison Table

	Try Hevo for Free
Core functions	Fully managed, no-code ELT/ETL	ETL/ELT data orchestration	Serverless ETL service
Ease of use	Easy	Easy	Complex
Connectors	150+	150+	70+ third-party
Real-time sync
Deployment	Cloud SaaS	Cloud-native	Serverless, AWS cloud
Transformations	Simple GUI, Python, and SQL	Visual, Python, and SQL	Advanced Spark ETL
Warehouse support	Multi-warehouse support	Multi-warehouse support	AWS-focused through Data Catalog
AI functionalities	Supports AI-ready pipelines	Maia AI assistant	Amazon Q Developer
Vendor lock-in	Low	Low	High
Customer support	24/7 chat, email	Tier-based, paid support plans	Paid plans
Security compliance	SOC 2 Type II, GDPR, HIPAA, DORA, and CPRA	SOC 2, ISO 27001, and PCI DSS	ISO, SOC, PCI DSS, HIPAA, and more
Free plan			(limited to Data Catalog)
Free trial
Starting price	$239/month	Custom pricing	$0.44 per DPU-hour

AWS Glue vs Matillion: In-Depth Feature & Use Case Comparison

1. Set up complexity

Matillion offers a polished drag-and-drop canvas to build pipelines. The platform uses familiar workflow patterns that make sense even if you haven’t built ETL pipelines before. Teams can onboard new members quickly since the visual representation shows exactly what the pipeline does at each step.

While AWS Glue Studio provides a UI-based builder, most teams end up writing PySpark or Scala code for anything beyond basic transformations. The visual editor works well for simple jobs but becomes limiting for complex logic. Even with detailed AWS Glue tutorials available, your team needs Python or Spark expertise to use the platform effectively.

If you have a mixed team of technical and non-technical members, Matillion is a suitable option. However, AWS Glue is ideal for a team of developers, especially when managing workflows in the AWS ecosystem.

Matillion:

Matillion’s simple UI makes user comfortable to configure and easy to understand the flow. And it’s well maintained configuration section is a big plus for this platform. So even new users can easily understand this platform’s features and design. Additionally Matillion providing learning materials and courses for beginners to understand their tool and the topics in the course also few, because they teaching only necessary topics which can help beginners to work in their platform as a professional.

Syed A.

Associate Trainee (Data Engineering)

AWS Glue:

“The overall experience with AWS Glue has been amazing. AWS Glue fits so well with the AWS data ecosystem, which helps in smooth and efficient functionality with AWS services. As it is an AWS service, AWS Glue is used as ETL and later can be continued for analytical solutions as well.”

Verified Author

View Review

2. Connector coverage

Matillion provides a wide set of fully managed SaaS connectors that you configure through the UI. It also hosts the Flex Connector library that includes a set of pre-built templates of commonly used APIs to build custom connectors. You can also request a connector, and the team will create it for you.

AWS Glue performs well when your data sources stay within AWS. Once you move to external SaaS tools, you often have to script custom code or rely on Marketplace integrations, which can increase maintenance overhead.

Matillion wins for a variety of ready-to-use SaaS connectors, while AWS Glue provides better support if your data sources live mostly inside AWS.

Matillion:

Matillion’s intuitive flow visualization makes orchestrating complex data pipelines feel like painting with digital brushes. The drag-and-drop interface transforms intricate ETL processes into a delightful canvas where connections flow seamlessly. Matillion’s connectors are like well-fitted puzzle pieces. They effortlessly link disparate data sources, allowing you to focus on insights rather than plumbing. Whether it’s cloud services, databases, or APIs, Matillion’s connectors are the bridge to your data dreams. In a nutshell, Matillion combines creativity and convenience, turning data wrangling into a captivating symphony.

Verified User

Non-Profit Organization Management

AWS Glue:

“I mostly liked its serverless ETL service. It also has integration with a wide range of data sources like S3, Amazon DynamoDB, and other JDBC databases. It also has the ability to process real-time data produced by social media platforms.”

Verified Author

View Review

3. Transformation capabilities

Matillion offers visual components for most transformation steps and lets you use SQL or Python when you want more control inside the warehouse. This helps mixed-skill teams because analysts and engineers can work on the same pipelines without slowing each other down.

AWS Glue follows a code-first approach for transformations and has minimal UI support. If your team is proficient with PySpark, you can create powerful transformations and claim deep control over large-scale ETL.

Hence, AWS Glue leads for heavy Spark workloads and highly customized data preparation, while Matillion is better suited for teams that prioritize speed and simplicity.

Matillion:

\"Matillion is one of the most used full-fledged ETL tools, which provides all types of ETL components for Extract, load, and transform components, which helps people who are less familiar with SQL or code to build the whole data pipeline.\"

Verified Author

View Review

AWS Glue:

Option to design ETL jobs: There are 2 ways to create our ETL jobs, a visual GUI-based editor or writing a Python or Spark script for more granular data processing logic. This enables developers to choose options that they are more comfortable working with.

Verified Author

View Review

4. Performance and scalability

Matillion primarily delivers high-performance batch processing. While it supports CDC for some sources, true real-time streaming isn’t the platform’s primary design focus. Performance is tied directly to the data warehouse’s resources, which means if you scale up a warehouse like Redshift, Matillion performs faster.

AWS Glue features dedicated streaming ETL jobs that process data from Kinesis or Kafka. The platform handles both batch and real-time workloads with similar development patterns. Its serverless AWS Glue architecture features auto-scaling, allowing it to efficiently process huge volumes of files, logs, or Spark jobs. However, costs can spike quickly if DPU allocation is misconfigured.

So, Glue is more future-proof for AWS-first engineering teams running both batch and streaming workloads as long as vendor lock-in is not a problem.

AWS Glue:

AWS Glue is a fully managed service that automatically scales based on the workload. It can handle large volumes of data and adjust resources accordingly, ensuring efficient and reliable data processing.

APOORV G.

Software Engineer

View Review

5. Cost management

Both tools have unpredictable pricing models, but in different ways.

Matillion offers credits based on Task Hours consumed by your pipelines. You can track usage through detailed dashboards, and pricing scales with actual usage. If you run out of credits, you must buy additional ones. This can lead to budget unpredictability if instances run inefficiently.

AWS Glue bills per DPU-hour with per-second billing. While this sounds flexible, unexpected costs can surprise you when jobs run longer or use more DPUs than planned. Optimizing AWS Glue jobs to control spending requires specialized knowledge of Spark performance tuning.

The right choice depends entirely on your data frequency and job use cases. Analyze your workload patterns before committing to either model.

Matillion:

The price structure can be a bit expensive especially for small-sized businesses but for large businesses it's cost effective.

Pauline W.

System Engineer

When to Choose Matillion

You should choose Matillion when your priority is speed of pipeline construction, and you rely on non-developer personnel like analysts to build and maintain data flows. Its low-code ELT approach and broad cloud connectivity make it a fit for multi-cloud environments and teams that prefer SQL-driven transformations.

If quick time-to-value and tight warehouse integration matter most, Matillion is ideal. However, if you require features like real-time streaming or hybrid deployments, consider exploring Matillion alternatives.

When to Choose AWS Glue

AWS Glue is a practical choice if most of your data lives in AWS tools like S3, Redshift, or DynamoDB. It proves useful when you need highly customized, large-scale ETL jobs in Python or Scala, and want serverless elasticity for unpredictable workloads.

You can also lean into advanced AWS Glue features, like its Data Catalog and native streaming, when your roadmap includes continuous ingestion or ML-driven transformations. Pick Glue only if you have developers comfortable with Spark to avoid cost and complexity issues.

Why Does Hevo Stand Out?

Hevo Data provides a simple, reliable, and transparent alternative to conventional ETL platforms. You do not need to manage credits, tune DPUs, or write transformation code unless you choose to. Hevo automates extraction, loading, scaling, and monitoring with minimal configuration and complete visibility end-to-end.

This is why Hevo stands out:

• No-code ETL: Sets up your pipeline within minutes without coding.
• Battle-tested connectors: Offers 150+ pre-built connectors, plus custom ones through code or on request.
• Flexible processing: Supports both batch and real-time sync for smooth data movement.
• Auto-scalability: Scales workloads in the background to stabilize pipelines as data volume grows.
• Advanced transformation: Manages transformations using the GUI or SQL, Python, and dbt for custom logic.
• Affordable pricing: Starts at just $239/ month and provides a generous free-forever tier.
• 24/7 human support: A dedicated support team helps you create, launch, and scale pipelines throughout.

If you want simple pipelines that can still handle complexity at scale, Hevo is the best option.

Ready to explore Hevo? Book a free demo with an expert today!

FAQs

Shiny Vineetha

Shiny is a Senior Content Specialist with expertise in B2B SaaS product marketing. A tech marketer with a passion for product-led storytelling, Shiny focuses on creating customer-centric narratives, clear product positioning, and strategic content that drives business growth.