Matillion vs dbt: 5 Key Differences

In today’s world of big data, it’s important for companies to quickly and effectively change and analyze large data sets to get useful information. Businesses need tools that help them gather, change, and use data easily so they can make smart decisions based on that data. Among the many ETL/ELT tools available, Matillion and dbt (Data Build Tool) are two of the best choices, each with unique features and uses for different data transformation tasks.

In this blog, we’ll discuss Matillion and dbt and when to choose one over the other. This Matillion vs dbt comparison will help you understand each tool’s strengths and use cases.

Matillion Overview

G2 Rating: 4.4 / 5 (77)

Capterra Rating: 4.3/ 5 (111)

Matillion is a cloud-native ETL/ELT tool that makes data integration easier and faster. Its low-code, intuitive interface allows non-technical users to build sophisticated data workflows with minimal coding. The tool integrates nicely with famous cloud data warehouses like Snowflake, Amazon Redshift, and Google BigQuery, thereby becoming one of the favorite solutions for companies using these Data Warehouses in the cloud.

Key features 

• Built-In Connectors: Matillion offers 150+ pre-built connectors, making connections between source and destination accessible and faster for the user. 

• Custom Connectors: Matillion also allows businesses to create custom connectors to REST API sources and request connectors, which are delivered in a few days.

• Visual Interface: Its simple User Interface and drag-and-drop interface allow users to build workflows quickly without technical know-how. This intuitive interface is helpful for users who prefer a low-code approach to building data pipelines.

• Scalability: Matillion’s architecture is designed to handle large data sets, making it suitable for organizations with big data analytics.

Pricing

Matillion offers a flexible and predictable pricing model where users only pay for what they require and use. It provides a credit-based pricing model and has three tiers of pricing, which are: 

• Basic – $2.00 / credit, which starts at 500 credits a month.
• Advanced – $2.50 / credit, which starts at 750 credits a month.
• Enterprise – $2.70 / credit, which starts at 1000 credits a month. Additional add-ons offered for Enterprise include AI capabilities and mission-critical support(dedicated support and rapid response time).

Refer to their pricing page for more information.

dbt Overview

G2 Rating: 4.8/5 (154)

dbt is an open-source tool that provides transformational capabilities to organizations’ data teams. It helps organizations organize and manipulate their data into valuable information for analysis and decision-making. dbt uses SQL to transform raw data rapidly. It is a popular choice among users who value control and transparency in their data workflows. Since it works on SQL, the users need expert knowledge of SQL queries. 

Key features 

• SQL Transformations: dbt utilizes SQL for writing transformation queries to data, which makes it accessible to data teams with SQL expertise and makes the whole process transparent.

• Integration capabilities: dbt is compatible with multiple leading data warehouse platforms such as Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Snapse Analytics. 

• Accessibility: As an open-source tool, dbt offers effective data transformation to businesses. 

• Transparency: It offers features like version control, testing, and documentation to ensure transparency.

Pricing

dbt offers free and open-source frameworks for individual users and premium ones for businesses. It offers three tiers in total: 

• Developer: this is the free tier for developers and also provides them with 14 days free trial of the teams’ plan 
• Team: Priced at $100/mo/seat, provided for teams of up to 8 developers.
• Enterprise: This custom tier is based on the number of developers and successful models built. It is designed for large enterprises with changing needs and more flexibility. 

Refer to their pricing page for more information.

dbt vs. Matillion: Choosing the Right Data Integration Tool

Both dbt (data build tool) and Matillion are powerful tools for data integration and transformation, but they cater to different needs and skill levels.

dbt

• Code-First Approach: Leverages SQL and Python for data transformation, providing flexibility and control.
• Open-Source and Versatile: Compatible with various data warehouses and platforms, offering high flexibility and customization.
• Strong Community and Ecosystem: Benefits from a large and active community, providing access to resources and support.
• Suitable for: Data engineers and analysts who prefer a code-driven approach and require high levels of customization.

Matillion

• Suitable for: Teams with diverse skill levels, including those with limited coding experience, who require a user-friendly and intuitive platform.
• Low-Code/No-Code Interface: Offers a user-friendly, drag-and-drop interface for data transformation, making it accessible to users with limited coding experience.
• Cloud-Based and Integrated: Provides a cloud-based platform with pre-built connectors and seamless integration with other cloud services.
• Focus on Ease of Use: Prioritizes user experience and simplifies data integration tasks.

Head-to-Head Comparison- Matillion vs dbt

Feature	Matillion	dbt
Ease of Use	User-friendly, intuitive design	Steep learning curve-SQL
Integration	Seamless with cloud data warehouses like Snowflake, Redshift	Compatible with various warehouses
Deployement	Cloud-native	Local, cloud, and CI/CD
Scalability	Highly scalable	Depends on the underlying warehouse
Transformation Approach	Visual, low-code ,ELT	SQL based
Pricing & Cost	Consumption-based pricing	Free (open-source) along with Enterprise options
Version Control	Limited	Git integration
Support	Enterprise support with extensive documentation	Community-driven support with Premium support for paid model.
Security Certifications	HIPAA, GDPR, SOC2 type II compliant	GDPR,HIPAA ,SOC2 type II compliant and ISO 27001 certified

Key Differences Between Matillion and dbt

1. Ease of Use

• Matillion: With its user-friendly and low-code interface, Matillion is easier to use for teams with limited or no coding skills. The visual interface reduces people’s learning curve and speeds up data pipeline creation and maintenance.

• dbt: dbt, on the other hand, is a code-first tool that requires a high level of technical expertise, especially in SQL. It does have a steeper learning curve than Matillion. 

2. Deployment and Integration

• Matillion: Matillion is a cloud-based data integration tool that provides 150+ pre-built data connectors, offering high scalability, flexibility, and access to all primary data sources and destinations. It also provides the option to create custom connectors to any data source using REST API.

• dbt: dbt provides the option to be run locally, in the cloud, or as part of CI/CD pipeline, which makes it suitable for a variety of use cases. It supports creating custom complex data models using SQL. It supports all major data warehouses and platforms like Snowflake, Redshift, and BigQuery.

3. Help and Support

• Matillion: Matillion offers access to a dedicated support team for troubleshooting. They also provide extensive documentation and tutorials to help users with common queries. Users can also resolve their doubts and ask for best practices on Matillion’s community forums.
• dbt: dbt has a very active community of contributors and users where you can clear doubts, share information, and troubleshoot. It also provides extensive documentation that covers all things to get started. Enterprise support is offered to organizations using the paid version of dbt. 

4. Transformation

• Matillion: It provides ELT (Extract, Load, Transform), i.e., data is loaded into the warehouse and then transformed. Matillion offers the full service of data integration in a cloud environment. Its post-load transformation allows the creation of refined data that is available for analysis. 
• dbt: dbt’s primary focus is on the transformation part of the ETL. dbt uses SQL to refine and transform large datasets into analysis-worthy information. With the help of complex transformations, dbt improves the decision-making process. Its transformation process requires a moderate level of coding knowledge, primarily SQL. 

5. Pricing Model

• Matillion: Matillion offers predictable pricing with a three-tier pricing model: Basic, Advanced, and Enterprise. It calculates pricing based on consumption and task hours per credit. The basic tier’s pricing starts at $2.00 / credit.
• dbt: dbt is an open-source framework tool that is free for individuals and offers pricing plans for teams and enterprises, which include premium support. The free plan also provides individual developers with a 14-day free trial. 

When to Choose Matillion

Matillion should be the ideal choice for companies needing an easy-to-use ETL/ELT service. If your team has people with less coding knowledge and prefers a drag-and-drop interface to build and maintain pipelines, Matillion should be your go-to choice. It’s well-suited for organizations already using data warehouses like Snowflake, Redshift, or BigQuery. Matillion’s dedicated support teams make sure that your team gets all the help they need. With the help of pre-configured connectors for major data sources and destinations, Matillion makes data migration a breeze for users.

When dbt Takes the Spotlight

dbt is the right choice for teams with coding knowledge who are comfortable working with SQL. It is preferred when organizations require more control and transparency over data transformations. dbt is an excellent option for individuals as it is open source and has extensive community support. For teams prioritizing integrating their data transformations into CI/CD workflows and leveraging Git for version control, dbt’s capabilities in these areas are unmatched. However, you might have to use an ETL service along with dbt because dbt focuses only on the transformation part. 

Why Not Try Hevo Instead?

If you’re evaluating Matillion and dbt, why not consider Hevo? Hevo is a user-friendly, no-code ETL tool that makes the data integration and transformation process effortless. Here are some reasons why Hevo might be a perfect fit for your organization:

• No-code data pipeline: It offers over 150+ pre-built connectors connecting all the major sources and destinations without coding.
• Automatic Schema Mapping: Hevo automatically detects and maps schema changes from source to destination, so there is no need to worry about manual mistakes.
• Real-Time Data Processing: Hevo offers real-time data streaming, making sure the data for analysis is always up-to-date.
• Transparent Pricing: Experience transparent pricing with Hevo.
• 24×7 Support: If you are stuck somewhere, don’t worry. Hevo provides 24×7 support.

Key Considerations Before Choosing a Tool

1. Integration Needs: Evaluate the integration capabilities of each tool with your existing data infrastructure and other tools in your tech stack.
2. Team Expertise: If your team has strong SQL and coding skills, dbt might be a better fit. If you prefer a more user-friendly and intuitive interface, Matillion could be a better choice.
3. Project Requirements: Consider the complexity of your data transformation needs and the level of customization required.

Conclusion 

Choosing between Matillion and dbt comes down to your organization’s needs and preferences. Both tools offer powerful capabilities for data transformation. Matillion offers seamless cloud integration with a user-friendly, low-code UI, whereas dbt offers powerful transformation capabilities with SQL and is a cost-effective solution. Matillion and dbt serve different purposes. Choose which purpose is best for your organization. Matillion vs dbt, who won? That’s for you to decide. 

But why settle for only two options? Hevo’s no-code data integration platform, real-time processing, and automatic schema mapping are reasons your workflows should be as seamless and efficient as possible. Sign up for Hevo’s 14-day free trial today.

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